diff --git a/results/module_fidelity.log b/results/module_fidelity.log new file mode 100644 index 0000000000000000000000000000000000000000..59c81599bae39aadc1765a5f8b7e8750c5edcf5e --- /dev/null +++ b/results/module_fidelity.log @@ -0,0 +1,11 @@ + Loading weights: 0%| | 0/338 [00:0032, exact recovery 0.091, copies together 0.844 + +- 2026-09-08 13:52:03 UTC — gpt2 l2: splitting [matched_C] ARI 0.0064 (core atoms 0.0102), C 10->10, exact recovery 0.091, copies together 0.250 + +- 2026-09-08 13:52:07 UTC — gpt2 l2: splitting [matched_C_collinear_collapsed] ARI 1.0000 (core atoms 1.0000), C 10->10, exact recovery 1.000, copies together 0.031 + +- 2026-09-08 13:52:08 UTC — gpt2 l2: naive covariance baseline ARI under scaling 0.3308 + +- 2026-09-08 13:52:17 UTC — gpt2 fisher: identity replay ARI 1.0000, exact recovery 1.000 + +- 2026-09-08 13:52:26 UTC — gpt2 fisher: scaling ARI 1.0000, exact recovery 1.000, Gram change 0.00e+00 + +- 2026-09-08 13:52:30 UTC — gpt2 fisher: splitting [free_C] ARI 0.0769 (core atoms 1.0000), C 4->3, exact recovery 0.000, copies together 0.031 + +- 2026-09-08 13:52:33 UTC — gpt2 fisher: splitting [matched_C] ARI 0.6925 (core atoms 0.2217), C 4->4, exact recovery 0.000, copies together 0.062 + +- 2026-09-08 13:52:37 UTC — gpt2 fisher: splitting [matched_C_collinear_collapsed] ARI 1.0000 (core atoms 1.0000), C 4->4, exact recovery 1.000, copies together 0.031 + +- 2026-09-08 13:52:37 UTC — gpt2 fisher: naive covariance baseline ARI under scaling 0.3308 + +- 2026-09-08 13:52:37 UTC — gpt2: reparameterization stress test complete + +- 2026-09-08 14:01:43 UTC — gpt2: partition sensitivity sweep, metrics=['l2', 'fisher'] + +- 2026-09-08 14:01:51 UTC — gpt2 l2: affinity=square knn=0 C=10 -> best core energy share 1.1496 (67 modules at threshold 1.0, cap 1.0) + +- 2026-09-08 14:01:57 UTC — gpt2 l2: affinity=square knn=10 C=22 -> best core energy share 1.1663 (47 modules at threshold 0.8, cap 1.0) + +- 2026-09-08 14:02:02 UTC — gpt2 l2: affinity=square knn=30 C=19 -> best core energy share 1.1627 (40 modules at threshold 0.8, cap 1.0) + +- 2026-09-08 14:02:09 UTC — gpt2 l2: affinity=abs knn=0 C=2 -> best core energy share 1.0364 (24 modules at threshold 1.0, cap 1.0) + +- 2026-09-08 14:02:16 UTC — gpt2 l2: affinity=abs knn=10 C=3 -> best core energy share 1.0434 (49 modules at threshold 1.0, cap 1.0) + +- 2026-09-08 14:02:23 UTC — gpt2 l2: affinity=abs knn=30 C=2 -> best core energy share 1.0131 (2 modules at threshold 0.7, cap 1.0) + +- 2026-09-08 14:02:29 UTC — gpt2 l2: affinity=square knn=0 C=16 -> best core energy share 1.3221 (222 modules at threshold 1.0, cap 0.5) + +- 2026-09-08 14:02:35 UTC — gpt2 l2: affinity=square knn=0 C=64 -> best core energy share 1.3103 (112 modules at threshold 0.8, cap 0.5) + +- 2026-09-08 14:02:35 UTC — gpt2 l2: sweep complete in 52.5s + +- 2026-09-08 14:02:47 UTC — gpt2 fisher: affinity=square knn=0 C=4 -> best core energy share 1.5879 (125 modules at threshold 1.0, cap 0.5) + +- 2026-09-08 14:02:51 UTC — gpt2 fisher: affinity=square knn=10 C=14 -> best core energy share 1.1148 (49 modules at threshold 0.7, cap 0.5) + +- 2026-09-08 14:02:58 UTC — gpt2 fisher: affinity=square knn=30 C=2 -> best core energy share 1.4501 (28 modules at threshold 1.0, cap 1.0) + +- 2026-09-08 14:03:03 UTC — gpt2 fisher: affinity=abs knn=0 C=2 -> best core energy share 1.3932 (72 modules at threshold 1.0, cap 1.0) + +- 2026-09-08 14:03:08 UTC — gpt2 fisher: affinity=abs knn=10 C=4 -> best core energy share 1.1980 (229 modules at threshold 1.0, cap 0.5) + +- 2026-09-08 14:03:12 UTC — gpt2 fisher: affinity=abs knn=30 C=3 -> best core energy share 1.3942 (177 modules at threshold 1.0, cap 0.5) + +- 2026-09-08 14:03:17 UTC — gpt2 fisher: affinity=square knn=0 C=16 -> best core energy share 1.1465 (45 modules at threshold 0.8, cap 1.0) + +- 2026-09-08 14:03:23 UTC — gpt2 fisher: affinity=square knn=0 C=64 -> best core energy share 1.0741 (91 modules at threshold 0.6, cap 0.5) + +- 2026-09-08 14:03:23 UTC — gpt2 fisher: sweep complete in 47.9s + +- 2026-09-08 14:03:23 UTC — gpt2: partition sensitivity sweep complete + +- 2026-09-08 14:04:15 UTC — gpt2: partition sensitivity sweep, metrics=['l2', 'fisher'] + +- 2026-09-08 14:04:24 UTC — gpt2 l2: affinity=square knn=0 C=10 -> best core energy share 1.1496 (67 modules at threshold 1.0, cap 1.0); at threshold 0.8 median effective-rank ratio 0.81 over 11 modules, 0% below 0.5 + +- 2026-09-08 14:04:29 UTC — gpt2 l2: affinity=square knn=10 C=22 -> best core energy share 1.1663 (47 modules at threshold 0.8, cap 1.0); at threshold 0.8 median effective-rank ratio 0.92 over 46 modules, 0% below 0.5 + +- 2026-09-08 14:04:34 UTC — gpt2 l2: affinity=square knn=30 C=19 -> best core energy share 1.1627 (40 modules at threshold 0.8, cap 1.0); at threshold 0.8 median effective-rank ratio 0.93 over 39 modules, 0% below 0.5 + +- 2026-09-08 14:05:09 UTC — gpt2: partition sensitivity sweep, metrics=['l2', 'fisher'] + +- 2026-09-08 14:05:18 UTC — gpt2 l2: affinity=square knn=0 C=10 -> best core energy share 1.1496 (67 modules at threshold 1.0, cap 1.0); at threshold 0.8 median effective-rank ratio 0.81 over 11 modules, 0% below 0.5 + +- 2026-09-08 14:05:23 UTC — gpt2 l2: affinity=square knn=10 C=22 -> best core energy share 1.1663 (47 modules at threshold 0.8, cap 1.0); at threshold 0.8 median effective-rank ratio 0.92 over 46 modules, 0% below 0.5 + +- 2026-09-08 14:05:28 UTC — gpt2 l2: affinity=square knn=30 C=19 -> best core energy share 1.1627 (40 modules at threshold 0.8, cap 1.0); at threshold 0.8 median effective-rank ratio 0.93 over 39 modules, 0% below 0.5 + +- 2026-09-08 14:05:36 UTC — gpt2 l2: affinity=abs knn=0 C=2 -> best core energy share 1.0364 (24 modules at threshold 1.0, cap 1.0) + +- 2026-09-08 14:05:42 UTC — gpt2 l2: affinity=abs knn=10 C=3 -> best core energy share 1.0434 (49 modules at threshold 1.0, cap 1.0); at threshold 0.8 median effective-rank ratio 0.84 over 1 modules, 0% below 0.5 + +- 2026-09-08 14:05:50 UTC — gpt2 l2: affinity=abs knn=30 C=2 -> best core energy share 1.0131 (2 modules at threshold 0.7, cap 1.0); at threshold 0.8 median effective-rank ratio 0.81 over 2 modules, 0% below 0.5 + +- 2026-09-08 14:05:55 UTC — gpt2 l2: affinity=square knn=0 C=16 -> best core energy share 1.3221 (222 modules at threshold 1.0, cap 0.5); at threshold 0.8 median effective-rank ratio 0.84 over 23 modules, 0% below 0.5 + +- 2026-09-08 14:06:02 UTC — gpt2 l2: affinity=square knn=0 C=64 -> best core energy share 1.3103 (112 modules at threshold 0.8, cap 0.5); at threshold 0.8 median effective-rank ratio 0.90 over 111 modules, 0% below 0.5 + +- 2026-09-08 14:06:02 UTC — gpt2 l2: sweep complete in 53.3s + +- 2026-09-08 14:06:13 UTC — gpt2 fisher: affinity=square knn=0 C=4 -> best core energy share 1.5879 (125 modules at threshold 1.0, cap 0.5); at threshold 0.8 median effective-rank ratio 0.62 over 2 modules, 0% below 0.5 + +- 2026-09-08 14:06:18 UTC — gpt2 fisher: affinity=square knn=10 C=14 -> best core energy share 1.1148 (49 modules at threshold 0.7, cap 0.5); at threshold 0.8 median effective-rank ratio 0.88 over 66 modules, 0% below 0.5 + +- 2026-09-08 14:06:25 UTC — gpt2 fisher: affinity=square knn=30 C=2 -> best core energy share 1.4501 (28 modules at threshold 1.0, cap 1.0) + +- 2026-09-08 14:06:31 UTC — gpt2 fisher: affinity=abs knn=0 C=2 -> best core energy share 1.3932 (72 modules at threshold 1.0, cap 1.0) + +- 2026-09-08 14:06:35 UTC — gpt2 fisher: affinity=abs knn=10 C=4 -> best core energy share 1.1980 (229 modules at threshold 1.0, cap 0.5); at threshold 0.8 median effective-rank ratio 0.95 over 16 modules, 0% below 0.5 + +- 2026-09-08 14:06:40 UTC — gpt2 fisher: affinity=abs knn=30 C=3 -> best core energy share 1.3942 (177 modules at threshold 1.0, cap 0.5); at threshold 0.8 median effective-rank ratio 0.98 over 4 modules, 0% below 0.5 + +- 2026-09-08 14:06:45 UTC — gpt2 fisher: affinity=square knn=0 C=16 -> best core energy share 1.1465 (45 modules at threshold 0.8, cap 1.0); at threshold 0.8 median effective-rank ratio 0.87 over 44 modules, 5% below 0.5 + +- 2026-09-08 14:06:51 UTC — gpt2 fisher: affinity=square knn=0 C=64 -> best core energy share 1.0741 (91 modules at threshold 0.6, cap 0.5); at threshold 0.8 median effective-rank ratio 0.83 over 79 modules, 3% below 0.5 + +- 2026-09-08 14:06:51 UTC — gpt2 fisher: sweep complete in 48.9s + +- 2026-09-08 14:06:51 UTC — gpt2: partition sensitivity sweep complete diff --git a/results/modules/gpt2/REPORT.md b/results/modules/gpt2/REPORT.md new file mode 100644 index 0000000000000000000000000000000000000000..bcee7a6f350d18cef64c140cabe94f543804bb08 --- /dev/null +++ b/results/modules/gpt2/REPORT.md @@ -0,0 +1,156 @@ +# Module partition report: gpt2 + +Modules are exact neuron partitions (f = Σ_c f_{S_c}); clustering used fitting data only, every number below is held-out unless labelled. Rules: core modules = components of co-assignment Π ≥ 0.8 over paired discovery bootstraps; K_c = longest bootstrap-stable prefix (min cos² ≥ 0.8); s*_c = validation-bootstrap certified group-OMP width (empirical rule, not a formal certificate). + +## Metric: l2 + +- Bootstraps B=12, affinity `square` (knn=0), normalization `fcm`; per-bootstrap C: [10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]; full-fit reference C=10. +- 11 core modules (sizes [34, 26, 20, 16, 13, 12, 6, 4, 2, 2, 2]), rest module 2935 of 3072 neurons; 7 neurons have no partner with Π ≥ 0.8. +- Exactness max|f − Σ f_S| = 3.62e-05 (relative to max|f|: 1.48e-06; float32 accumulation). Leakage Σ_{c≠c'}|Γ|/‖f‖² = 0.0543 (test) vs random-partition null median 0.0645, q05 0.0536; 6.0% of 200 null draws at or below observed. + +### Module inventory + +| module | \|S_c\| | Γ_cc/‖f‖² (test) | Π within | eff. rank | K_c | top-K_c coverage (test) | s*_c | s*/\|S\| | semantics (val/test AUC) | dose-1 loss effect (frac. of full) | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | --- | ---: | +| m0 | 34 | 0.020 | 0.87 | 28.7 | 1 | 0.013 | 11 | 0.32 | 0.61/0.50 | +0.0043 (+0.039) | +| m1 | 26 | 0.008 | 0.97 | 20.7 | 2 | 0.060 | 9 | 0.35 | — | -0.0087 (-0.078) | +| m2 | 20 | 0.010 | 0.98 | 16.2 | 2 | 0.203 | 4 | 0.20 | 0.50/0.50 | -0.0021 (-0.019) | +| m3 | 16 | 0.016 | 0.84 | 9.8 | 0 | — | — | — | 0.53/0.52 | +0.0029 (+0.026) | +| m4 | 13 | 0.008 | 0.99 | 9.9 | 2 | 0.059 | 5 | 0.38 | — | +0.0071 (+0.065) | +| m5 | 12 | 0.007 | 1.00 | 10.1 | 1 | 0.089 | 4 | 0.33 | — | -0.0030 (-0.028) | +| m6 | 6 | 0.011 | 1.00 | 3.4 | 4 | 0.994 | 5 | 0.83 | 0.54/0.62 | +0.0026 (+0.023) | +| m7 | 4 | 0.001 | 0.88 | 3.6 | 1 | 0.241 | 1 | 0.25 | — | +0.0012 (+0.011) | +| m8 | 2 | 0.002 | 0.83 | 1.7 | 2 | 1.000 | 2 | 1.00 | — | +0.0032 (+0.029) | +| m9 | 2 | 0.000 | 1.00 | 1.8 | 2 | 1.000 | 2 | 1.00 | — | +0.0004 (+0.003) | +| m10 | 2 | 0.001 | 1.00 | 1.2 | 2 | 1.000 | 2 | 1.00 | — | +0.0014 (+0.013) | +| rest | 2935 | 0.970 | — | 2161.0 | 0 | — | — | — | — | +0.1005 (+0.908) | + + +## Metric: fisher + +- Bootstraps B=12, affinity `square` (knn=0), normalization `fcm`; per-bootstrap C: [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]; full-fit reference C=4. +- 2 core modules (sizes [29, 2]), rest module 3041 of 3072 neurons; 13 neurons have no partner with Π ≥ 0.8. +- Exactness max|f − Σ f_S| = 2.86e-05 (relative to max|f|: 1.16e-06; float32 accumulation). Leakage Σ_{c≠c'}|Γ|/‖f‖² = 0.0309 (test) vs random-partition null median 0.0195, q05 0.0073; 86.5% of 200 null draws at or below observed. + +### Module inventory + +| module | \|S_c\| | Γ_cc/‖f‖² (test) | Π within | eff. rank | K_c | top-K_c coverage (test) | s*_c | s*/\|S\| | semantics (val/test AUC) | dose-1 loss effect (frac. of full) | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | --- | ---: | +| m0 | 29 | 0.027 | 0.83 | 18.4 | 2 | 0.012 | 14 | 0.48 | 0.44/0.18 | +0.0086 (+0.078) | +| m1 | 2 | 0.001 | 1.00 | 1.2 | 2 | 1.000 | 2 | 1.00 | 0.50/0.48 | +0.0014 (+0.013) | +| rest | 3041 | 1.003 | — | 1507.9 | 0 | — | — | — | — | +0.1007 (+0.910) | + + +## Matched-count reconstruction (test relative squared error of the centered write) + +Residual-L2 error for every column; FCM block rows come from the frozen comparison reference. Modules include the rest module as the last object (error reaches 0 exactly). + +**Test error vs number of kept objects C** + +| C | modules_l2 | modules_fisher | fcm_l2_blocks | fcm_fisher_blocks | sae | transcoder | +| ---: | ---: | ---: | ---: | ---: | ---: | ---: | +| 1 | 0.994 | 1.002 | 1.001 | 0.947 | 0.963 | 0.992 | +| 2 | 0.990 | 1.003 | 0.990 | 0.947 | 0.928 | 0.983 | +| 3 | 0.983 | 0.000 | | | 0.910 | 0.983 | +| 4 | 0.980 | | 0.984 | 0.938 | 0.904 | 0.975 | +| 5 | 0.977 | | | | | | +| 6 | 0.974 | | | | | | +| 7 | 0.972 | | | | | | +| 8 | 0.971 | | 0.979 | 0.923 | 0.896 | 0.963 | +| 9 | 0.971 | | | | | | +| 10 | 0.971 | | | | | | +| 11 | 0.970 | | | | | | +| 12 | 0.000 | | | | 0.889 | 0.947 | +| 16 | | | 0.933 | 0.909 | 0.881 | 0.938 | +| 32 | | | 0.914 | 0.865 | 0.864 | 0.916 | +| 64 | | | 0.902 | 0.838 | 0.834 | 0.882 | +| 128 | | | | | 0.793 | 0.841 | +| 1536 | | | | | 0.509 | 0.668 | + +**Reproducible energy share among kept objects (stability ≥ 0.8)** + +| C | modules_l2 | modules_fisher | sae | transcoder | +| ---: | ---: | ---: | ---: | ---: | +| 1 | 0.020 | 0.027 | 0.000 | 0.000 | +| 2 | 0.036 | 0.028 | 0.000 | 0.000 | +| 3 | 0.047 | 0.028 | 0.023 | 0.000 | +| 4 | 0.056 | | 0.031 | 0.000 | +| 5 | 0.065 | | | | +| 6 | 0.072 | | | | +| 7 | 0.079 | | | | +| 8 | 0.081 | | 0.031 | 0.000 | +| 9 | 0.082 | | | | +| 10 | 0.082 | | | | +| 11 | 0.083 | | | | +| 12 | 0.083 | | 0.031 | 0.000 | +| 16 | | | 0.031 | 0.000 | +| 32 | | | 0.031 | 0.000 | +| 64 | | | 0.033 | 0.000 | +| 128 | | | 0.034 | 0.000 | +| 1536 | | | — | — | + +## Function-preserving reparameterization (plan section 4, Table 4) + +Two transformations that leave the layer function exactly unchanged. Agreement is the Adjusted Rand Index of the recovered neuron partition against the original; the rest module counts as one label. The identity replay is the achievable ceiling for this pipeline. + +### l2 + +| transformation | ARI (all neurons) | ARI (core-module neurons) | exact core-module recovery | modules C → C | +| --- | ---: | ---: | ---: | ---: | +| identity replay (control) | 1.0000 | 1.0000 | 1.000 | 11 → 11 | +| positive scaling a_j, 1/a_j | 1.0000 | 1.0000 | 1.000 | 11 → 11 | +| positive half split 32 atoms — free C | 0.3618 | 0.3384 | 0.091 | 11 → 39 | +| positive half split 32 atoms — matched C | 0.0064 | 0.0102 | 0.091 | 11 → 11 | +| positive half split 32 atoms — matched C collinear collapsed | 1.0000 | 1.0000 | 1.000 | 11 → 11 | +| baseline: raw-covariance clustering, same scaling | 0.3308 | — | — | 3 → 2 | + +Affinity change on atoms that were not split: fcm 0.0e+00, cosine 0.0e+00. The clustering input on those atoms is therefore preserved to floating-point precision; the partition still moves because splitting turns an atom's self-coupling into an off-diagonal edge, so each pair of copies forms a near-disconnected 2-clique that adds a near-zero normalized-Laplacian eigenvalue. The raw-covariance baseline row is metric-independent by construction. +Collapsing exactly-collinear atoms first (discoverable from the Gram alone, and a no-op on the original layer: 3072 groups for 3072 neurons) merges 3104 atoms back to 3072 with a Gram differing from the original by 0.0 and restores the partition. + +### fisher + +| transformation | ARI (all neurons) | ARI (core-module neurons) | exact core-module recovery | modules C → C | +| --- | ---: | ---: | ---: | ---: | +| identity replay (control) | 1.0000 | 1.0000 | 1.000 | 2 → 2 | +| positive scaling a_j, 1/a_j | 1.0000 | 1.0000 | 1.000 | 2 → 2 | +| positive half split 32 atoms — free C | 0.0769 | 1.0000 | 0.000 | 2 → 4 | +| positive half split 32 atoms — matched C | 0.6925 | 0.2217 | 0.000 | 2 → 5 | +| positive half split 32 atoms — matched C collinear collapsed | 1.0000 | 1.0000 | 1.000 | 2 → 2 | +| baseline: raw-covariance clustering, same scaling | 0.3308 | — | — | 3 → 2 | + +Affinity change on atoms that were not split: fcm 2.4e-07, cosine 0.0e+00. The clustering input on those atoms is therefore preserved to floating-point precision; the partition still moves because splitting turns an atom's self-coupling into an off-diagonal edge, so each pair of copies forms a near-disconnected 2-clique that adds a near-zero normalized-Laplacian eigenvalue. The raw-covariance baseline row is metric-independent by construction. +Collapsing exactly-collinear atoms first (discoverable from the Gram alone, and a no-op on the original layer: 3072 groups for 3072 neurons) merges 3104 atoms back to 3072 with a Gram differing from the original by 0.0 and restores the partition. + +## Matched-count loss recovered (test, dose 1) + +Full-ablation loss increase 0.1107 nats over 128 sequences; loss recovered = 1 − patched increase / full increase. + +**Loss recovered vs C** + +| C | modules_l2 | modules_fisher | fcm_l2_blocks | fcm_fisher_blocks | sae | transcoder | pca | neuron_mass | +| ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | +| 1 | 0.044 | 0.082 | 0.002 | 0.160 | 0.016 | 0.000 | -0.025 | -0.031 | +| 2 | 0.070 | 0.090 | -0.018 | 0.163 | -0.002 | 0.001 | 0.315 | 0.149 | +| 3 | | 1.000 | | | | | | | +| 4 | 0.049 | | -0.018 | 0.161 | 0.244 | 0.102 | 0.384 | 0.089 | +| 8 | 0.064 | | -0.008 | 0.271 | 0.322 | 0.134 | 0.188 | 0.072 | +| 11 | 0.092 | | | | | | | | +| 12 | 1.000 | | | | | | | | +| 16 | | | 0.172 | 0.340 | 0.739 | 0.179 | 0.522 | 0.014 | +| 32 | | | 0.158 | 0.620 | 0.626 | 0.153 | 0.564 | 0.101 | +| 64 | | | 0.159 | 0.656 | 0.546 | 0.082 | 0.752 | 0.451 | +| 128 | | | | | 0.597 | 0.412 | | | +| 1536 | | | | | 0.909 | 0.804 | | | + +## Failure archetypes (l2, plan section 13) + +- **A, stable and sparse** (9 modules): a bootstrap-stable module with a certified native width at most half its size. m0 (|S|=34, K=1, s*=11), m1 (|S|=26, K=2, s*=9), m2 (|S|=20, K=2, s*=4), m4 (|S|=13, K=2, s*=5), m5 (|S|=12, K=1, s*=4), m7 (|S|=4, K=1, s*=1), m8 (|S|=2, K=2, s*=2), m9 (|S|=2, K=2, s*=2) … +- **B, stable but distributed** (1 modules): closed directions exist but no compact native width. m6 (|S|=6, K=4, s*=5) +- **C, no statistically closed direction** (2 groups, including the rest module): the pipeline declines to interpret these rather than reporting a sparse approximation of an unidentified parent. + +## Failure archetypes (fisher, plan section 13) + +- **A, stable and sparse** (2 modules): a bootstrap-stable module with a certified native width at most half its size. m0 (|S|=29, K=2, s*=14), m1 (|S|=2, K=2, s*=2) +- **B, stable but distributed** (0 modules): closed directions exist but no compact native width. none +- **C, no statistically closed direction** (1 groups, including the rest module): the pipeline declines to interpret these rather than reporting a sparse approximation of an unidentified parent. + diff --git a/results/modules/gpt2/RESULTS.md b/results/modules/gpt2/RESULTS.md new file mode 100644 index 0000000000000000000000000000000000000000..e7ddffe1b826b27655edf14186917b0fb2a7859d --- /dev/null +++ b/results/modules/gpt2/RESULTS.md @@ -0,0 +1,56 @@ + +### gpt2: partition (l2) + +11 core modules (sizes [34, 26, 20, 16, 13, 12, 6, 4, 2, 2, 2]), rest module 2935 neurons, 7 neurons with no co-assignment partner >= 0.8. Per-bootstrap C: [10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]. Saved `results/modules/gpt2/partition/partition_l2.json`. + +### gpt2: partition (fisher) + +2 core modules (sizes [29, 2]), rest module 3041 neurons, 13 neurons with no co-assignment partner >= 0.8. Per-bootstrap C: [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]. Saved `results/modules/gpt2/partition/partition_fisher.json`. + +### gpt2: module geometry (l2) + +Exactness max|f - sum f_S| = 3.62e-05. Test leakage 0.0543 (random-partition null median 0.0645, 6.0% of null draws at or below). Modules: m0 |S|=34 share=0.020 K=1 s*=11; m1 |S|=26 share=0.008 K=2 s*=9; m2 |S|=20 share=0.010 K=2 s*=4; m3 |S|=16 share=0.016 K=0 s*=None; m4 |S|=13 share=0.008 K=2 s*=5; m5 |S|=12 share=0.007 K=1 s*=4; m6 |S|=6 share=0.011 K=4 s*=5; m7 |S|=4 share=0.001 K=1 s*=1; m8 |S|=2 share=0.002 K=2 s*=2; m9 |S|=2 share=0.000 K=2 s*=2; m10 |S|=2 share=0.001 K=2 s*=2; rest |S|=2935 share=0.970 K=0 s*=None. Saved `results/modules/gpt2/fit/modules_l2.json`. + +### gpt2: module geometry (fisher) + +Exactness max|f - sum f_S| = 2.86e-05. Test leakage 0.0309 (random-partition null median 0.0195, 86.5% of null draws at or below). Modules: m0 |S|=29 share=0.027 K=2 s*=14; m1 |S|=2 share=0.001 K=2 s*=2; rest |S|=3041 share=1.003 K=0 s*=None. Saved `results/modules/gpt2/fit/modules_fisher.json`. + +### gpt2: module causal effects (l2) + +Full-ablation loss increase 0.1107 nats on 128 sequences. Dose-1 fraction of full effect per module: m0 +0.039, m3 +0.026, m6 +0.023, m2 -0.019, m1 -0.078, m4 +0.065, m5 -0.028, m8 +0.029, m7 +0.011, m10 +0.013, m9 +0.003, rest +0.908. Saved `results/modules/gpt2/causal/module_effects_l2.json`. + +### gpt2: module causal effects (fisher) + +Full-ablation loss increase 0.1107 nats on 128 sequences. Dose-1 fraction of full effect per module: m0 +0.078, m1 +0.013, rest +0.910. Saved `results/modules/gpt2/causal/module_effects_fisher.json`. + +### gpt2: matched-count loss recovered + +55 patched interventions; loss recovered = 1 - (patched loss increase)/(full-ablation loss increase). Saved `results/modules/gpt2/causal/matched_count.json`. + +### gpt2: module semantic records + +6 records written to `results/modules/gpt2/semantics/records` (module_l2_m0, module_l2_m3, module_l2_m6, module_l2_m2, module_fisher_m0, module_fisher_m1). Hypotheses are generated/selected/frozen by the existing scorer; nothing here touches the LLM. + +### gpt2: module reparameterization (l2) + +Adjusted Rand Index of the recovered neuron partition against the original: identity replay 1.0000, positive scaling 1.0000, positive splitting 0.0064 at matched cluster count, 1.0000 after collapsing exactly-collinear atoms first and 0.3618 when the eigengap rule reselects the cluster count, naive raw-covariance baseline under the same scaling 0.3308. Exact core-module set recovery under scaling 1.000, under splitting at matched cluster count 0.091; 0.250 of split copies are co-assigned with their twin. Affinity change on unsplit atoms under splitting: {'fcm': 0.0, 'cosine': 0.0}. Saved `results/modules/gpt2/reparameterization/reparameterization.json`. + +### gpt2: module reparameterization (fisher) + +Adjusted Rand Index of the recovered neuron partition against the original: identity replay 1.0000, positive scaling 1.0000, positive splitting 0.6925 at matched cluster count, 1.0000 after collapsing exactly-collinear atoms first and 0.0769 when the eigengap rule reselects the cluster count, naive raw-covariance baseline under the same scaling 0.3308. Exact core-module set recovery under scaling 1.000, under splitting at matched cluster count 0.000; 0.062 of split copies are co-assigned with their twin. Affinity change on unsplit atoms under splitting: {'fcm': 2.3508607682742877e-07, 'cosine': 0.0}. Saved `results/modules/gpt2/reparameterization/reparameterization.json`. + +### gpt2: partition sensitivity (l2) + +Swept affinity ['square', 'abs'], k-NN [0, 10, 30], fixed cluster counts [16, 64], co-assignment thresholds [0.6, 0.7, 0.8, 0.9, 1.0] and bulk caps [0.5, 1.0] (80 configurations). Best held-out core-module energy share: 1.3221 (222 modules, affinity square, knn 0, C=16, threshold 1.0, cap 0.5); 1.3221 (222 modules, affinity square, knn 0, C=16, threshold 1.0, cap 1.0); 1.3103 (112 modules, affinity square, knn 0, C=64, threshold 0.8, cap 0.5). Saved `results/modules/gpt2/sweep/sweep.json`. + +### gpt2: partition sensitivity (fisher) + +Swept affinity ['square', 'abs'], k-NN [0, 10, 30], fixed cluster counts [16, 64], co-assignment thresholds [0.6, 0.7, 0.8, 0.9, 1.0] and bulk caps [0.5, 1.0] (80 configurations). Best held-out core-module energy share: 1.5879 (125 modules, affinity square, knn 0, C=4, threshold 1.0, cap 0.5); 1.5879 (125 modules, affinity square, knn 0, C=4, threshold 1.0, cap 1.0); 1.4501 (28 modules, affinity square, knn 30, C=2, threshold 1.0, cap 1.0). Saved `results/modules/gpt2/sweep/sweep.json`. + +### gpt2: partition sensitivity (l2) + +Swept affinity ['square', 'abs'], k-NN [0, 10, 30], fixed cluster counts [16, 64], co-assignment thresholds [0.6, 0.7, 0.8, 0.9, 1.0] and bulk caps [0.5, 1.0] (80 configurations). Best held-out core-module energy share: 1.3221 (222 modules, affinity square, knn 0, C=16, threshold 1.0, cap 0.5); 1.3221 (222 modules, affinity square, knn 0, C=16, threshold 1.0, cap 1.0); 1.3103 (112 modules, affinity square, knn 0, C=64, threshold 0.8, cap 0.5). Saved `results/modules/gpt2/sweep/sweep.json`. + +### gpt2: partition sensitivity (fisher) + +Swept affinity ['square', 'abs'], k-NN [0, 10, 30], fixed cluster counts [16, 64], co-assignment thresholds [0.6, 0.7, 0.8, 0.9, 1.0] and bulk caps [0.5, 1.0] (80 configurations). Best held-out core-module energy share: 1.5879 (125 modules, affinity square, knn 0, C=4, threshold 1.0, cap 0.5); 1.5879 (125 modules, affinity square, knn 0, C=4, threshold 1.0, cap 1.0); 1.4501 (28 modules, affinity square, knn 30, C=2, threshold 1.0, cap 1.0). Saved `results/modules/gpt2/sweep/sweep.json`. diff --git a/results/modules/gpt2/causal/matched_count.json b/results/modules/gpt2/causal/matched_count.json new file mode 100644 index 0000000000000000000000000000000000000000..87f2160e906a3ea162fb9a87b9118a4acf9265f4 --- /dev/null +++ b/results/modules/gpt2/causal/matched_count.json @@ -0,0 +1,490 @@ +{ + "base": { + "sequences": 128, + "mean_clean_loss": 4.176929950714111, + "mean_full_ablation_loss": 4.287600040435791, + "full_ablation_loss_increase": 0.11067008972167969, + "intervention": "subtract dose * write at the penultimate position; predict the final token (same as evaluate.py)" + }, + "rows": [ + { + "method": "modules_l2", + "C": 1, + "object": "m0", + "loss_recovered": 0.043567852403357255, + "mean_loss_change": 0.10584843158721924, + "kl_clean_patched": 0.09389221668243408, + "neurons_used": 34, + "includes_rest": false + }, + { + "method": "modules_l2", + "C": 2, + "object": "m3", + "loss_recovered": 0.07045481964048739, + 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His are free translations , which seek to conceal the parallelisms through enj", + "token_texts": [ + " .", + " His", + " are", + " free", + " translations", + " ,", + " which", + " seek", + " to", + " conceal", + " the", + " parallel", + "isms", + " through", + " en", + "j" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 2.160092353820801 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 2.160092353820801, + "quantile": 0.0732421875, + "active": false, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_102_31", + "text": " to conceal the parallelisms through enjambement and expansion and contraction of the content ; his responses to the allusions", + "token_texts": [ + " to", + " conceal", + " the", + " parallel", + "isms", + " through", + " en", + "j", + "amb", + "ement", + " and", + " expansion", + " and", + " contraction", + " of", + " the", + " content", + " ;", + " his", + " responses", + " to", + " the", + " all", + "usions" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 94.04612731933594 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 94.04612731933594, + "quantile": 0.9345703125, + "active": true, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_102_47", + "text": " content ; his responses to the allusions are firstly to omit most of these poems from his selection , and secondly to", + "token_texts": [ + " content", + " ;", + " his", + " responses", + " to", + " the", + " all", + "usions", + " are", + " first", + "ly", + " to", + " omit", + " most", + " of", + " these", + " poems", + " from", + " his", + " selection", + " ,", + " and", + " secondly", + " to" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 47.02536392211914 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 47.02536392211914, + "quantile": 0.8642578125, + "active": false, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_102_62", + "text": " these poems from his selection , and secondly to \" translate out \" the references in those works which he does select . 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In The Selected Poems", + "token_texts": [ + "@", + " stopped", + " lines", + " and", + " preserve", + " some", + " degree", + " of", + " parallel", + "ism", + " .", + " In", + " The", + " Selected", + " Po", + "ems" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 33.322471618652344 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 33.322471618652344, + "quantile": 0.8173828125, + "active": false, + "media": {}, + "metadata": { + "sequence": 104, + "split": "test" + } + }, + { + "example_id": "test_104_31", + "text": " parallelism . 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Penalty minutes are the total number of minutes assigned to a player for", + "token_texts": [ + " player", + " '", + "s", + " goals", + " and", + " assists", + " are", + " collectively", + " called", + " points", + " .", + " Penalty", + " minutes", + " are", + " the", + " total", + " number", + " of", + " minutes", + " assigned", + " to", + " a", + " player", + " for" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.006846420466899872 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.006846420466899872, + "quantile": 0.207763671875, + "active": false, + "media": {}, + "metadata": { + "sequence": 169, + "split": "train" + } + }, + { + "example_id": "train_169_47", + "text": " number of minutes assigned to a player for infractions assessed during the season.Plus @-@ minus is a statistic that", + "token_texts": [ + " number", + " of", + " minutes", + " assigned", + " to", + " a", + " player", + " for", + " inf", + "ractions", + " assessed", + " during", + " the", + " season", + ".", + "Plus", + " @", + "-", + "@", + " minus", + " is", + " a", + " statistic", + " that" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.014837121590971947 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.014837121590971947, + "quantile": 0.325927734375, + "active": false, + "media": {}, + "metadata": { + "sequence": 169, + "split": "train" + } + }, + { + "example_id": "train_169_62", + "text": "Plus @-@ minus is a statistic that tracks when a player was on the ice while goals were scored , both for", + "token_texts": [ + "Plus", + " @", + "-", + "@", + " minus", + " is", + " a", + " statistic", + " that", + " tracks", + " when", + " a", + " player", + " was", + " on", + " the", + " ice", + " while", + " goals", + " were", + " scored", + " ,", + " both", + " for" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.03435496613383293 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.03435496613383293, + "quantile": 0.522216796875, + "active": false, + "media": {}, + "metadata": { + "sequence": 169, + "split": "train" + } + }, + { + "example_id": "train_170_15", + "text": " against their team , though some in game situations will not effect the statistic . Below", + "token_texts": [ + " against", + " their", + " team", + " ,", + " though", + " some", + " in", + " game", + " situations", + " will", + " not", + " effect", + " the", + " statistic", + " .", + " Below" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.04905342683196068 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.04905342683196068, + "quantile": 0.631103515625, + "active": false, + "media": {}, + "metadata": { + "sequence": 170, + "split": "train" + } + }, + { + "example_id": "train_170_31", + "text": " situations will not effect the statistic . Below is a listing of all player statistics for the Blue Jackets during the season . ", + "token_texts": [ + " situations", + " will", + " not", + " effect", + " the", + " statistic", + " .", + " Below", + " is", + " a", + " listing", + " of", + " all", + " player", + " statistics", + " for", + " the", + " Blue", + " Jackets", + " during", + " the", + " season", + " .", + " " + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.20672085881233215 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.20672085881233215, + "quantile": 0.884521484375, + "active": false, + "media": {}, + "metadata": { + "sequence": 170, + "split": "train" + } + }, + { + "example_id": "train_170_47", + "text": " the Blue Jackets during the season . \n<|endoftext|> = = = Skaters = = = \n<|endoftext|> Note : Pos", + "token_texts": [ + " the", + " Blue", + " Jackets", + " during", + " the", + " season", + " .", + " ", + "\n", + "<|endoftext|>", + " =", + " =", + " =", + " Sk", + "aters", + " =", + " =", + " =", + " ", + "\n", + "<|endoftext|>", + " Note", + " :", + " Pos" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.020780101418495178 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.020780101418495178, + "quantile": 0.401123046875, + "active": false, + "media": {}, + "metadata": { + "sequence": 170, + "split": "train" + } + }, + { + "example_id": "train_170_62", + "text": " = = = \n<|endoftext|> Note : Pos \n<|endoftext|> = Position ; GP = \n<|endoftext|> Games played in ;", + "token_texts": [ + " =", + " =", + " =", + " ", + "\n", + "<|endoftext|>", + " Note", + " :", + " Pos", + " ", + "\n", + "<|endoftext|>", + " =", + " Position", + " ;", + " GP", + " =", + " ", + "\n", + "<|endoftext|>", + " Games", + " played", + " in", + " ;" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.17308364808559418 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.17308364808559418, + "quantile": 0.846435546875, + "active": false, + "media": {}, + "metadata": { + "sequence": 170, + "split": "train" + } + }, + { + "example_id": "train_171_15", + "text": " \n<|endoftext|> = Goals ; A = \n<|endoftext|> Assists ; Pts", + "token_texts": [ + " ", + "\n", + "<|endoftext|>", + " =", + " Goals", + " ;", + " A", + " =", + " ", + "\n", + "<|endoftext|>", + " Ass", + "ists", + " ;", + " P", + "ts" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0011872092727571726 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.0011872092727571726, + "quantile": 0.035400390625, + "active": false, + "media": {}, + "metadata": { + "sequence": 171, + "split": "train" + } + }, + { + "example_id": "train_171_31", + "text": " \n<|endoftext|> Assists ; 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W \n<|endoftext|>", + "token_texts": [ + "I", + " =", + " ", + "\n", + "<|endoftext|>", + " Time", + " On", + " Ice", + " (", + " minutes", + " )", + " ;", + " W", + " ", + "\n", + "<|endoftext|>" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.11200551688671112 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.11200551688671112, + "quantile": 0.781494140625, + "active": false, + "media": {}, + "metadata": { + "sequence": 172, + "split": "train" + } + }, + { + "example_id": "train_172_31", + "text": " ( minutes ) ; W \n<|endoftext|> = Wins ; L = \n<|endoftext|> Losses ; OT \n<|endoftext|> =", + "token_texts": [ + " (", + " minutes", + " )", + " ;", + " W", + " ", + "\n", + "<|endoftext|>", + " =", + " Wins", + " ;", + " L", + " =", + " ", + "\n", + "<|endoftext|>", + " Loss", + "es", + " ;", + " OT", + " ", + "\n", + "<|endoftext|>", + " =" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.06694143265485764 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.06694143265485764, + "quantile": 0.708740234375, + "active": false, + "media": {}, + "metadata": { + "sequence": 172, + "split": "train" + } + }, + { + "example_id": "train_172_47", + "text": " Losses ; OT \n<|endoftext|> = Overtime Losses ; GA = \n<|endoftext|> Goals Against ; GAA =", + "token_texts": [ + " Loss", + "es", + " ;", + " OT", + " ", + "\n", + "<|endoftext|>", + " =", + " Overt", + "ime", + " Loss", + "es", + " ;", + " GA", + " =", + " ", + "\n", + "<|endoftext|>", + " Goals", + " Against", + " ;", + " G", + "AA", + " =" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.014461174607276917 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.014461174607276917, + "quantile": 0.321533203125, + "active": false, + "media": {}, + "metadata": { + "sequence": 172, + "split": "train" + } + }, + { + "example_id": "train_172_62", + "text": " \n<|endoftext|> Goals Against ; GAA = Goals Against Average ; SA = Shots Against ; SV \n<|endoftext|> = S", + "token_texts": [ + " ", + "\n", + "<|endoftext|>", + " Goals", + " Against", + " ;", + " G", + "AA", + " =", + " Goals", + " Against", + " Average", + " ;", + " SA", + " =", + " Shots", + " Against", + " ;", + " SV", + " ", + "\n", + "<|endoftext|>", + " =", + " S" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.005602356977760792 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.005602356977760792, + "quantile": 0.179443359375, + "active": false, + "media": {}, + "metadata": { + "sequence": 172, + "split": "train" + } + }, + { + "example_id": "train_173_15", + "text": " ; 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In front of the ram was a spar torpedo that carried 50 pounds ( 23", + "token_texts": [ + " .", + " In", + " front", + " of", + " the", + " ram", + " was", + " a", + " spar", + " torpedo", + " that", + " carried", + " 50", + " pounds", + " (", + " 23" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 26.877309799194336 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 26.877309799194336, + "quantile": 0.993896484375, + "active": true, + "media": {}, + "metadata": { + "sequence": 265, + "split": "train" + } + }, + { + "example_id": "train_265_31", + "text": " spar torpedo that carried 50 pounds ( 23 kg ) of black powder on a wooden pole connected to an iron lever that could", + "token_texts": [ + " spar", + " torpedo", + " that", + " carried", + " 50", + " pounds", + " (", + " 23", + " kg", + " )", + " of", + " black", + " powder", + " on", + " a", + " wooden", + " pole", + " connected", + " to", + " an", + " iron", + " lever", + " that", + " could" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.08119581639766693 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.08119581639766693, + "quantile": 0.728759765625, + "active": false, + "media": {}, + "metadata": { + "sequence": 265, + "split": "train" + } + }, + { + "example_id": "train_265_47", + "text": " pole connected to an iron lever that could be raised or lowered by means of pulleys . \n<|endoftext|> On 31 July", + "token_texts": [ + " pole", + " connected", + " to", + " an", + " iron", + " lever", + " that", + " could", + " be", + " raised", + " or", + " lowered", + " by", + " means", + " of", + " pul", + "leys", + " .", + " ", + "\n", + "<|endoftext|>", + " On", + " 31", + " July" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0019321250729262829 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0019321250729262829, + "quantile": 0.074462890625, + "active": false, + "media": {}, + "metadata": { + "sequence": 265, + "split": "train" + } + }, + { + "example_id": "train_265_62", + "text": " pulleys . \n<|endoftext|> On 31 July 1862 , under the command of Lieutenant Charles H. 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His are free translations , which seek to conceal the parallelisms through enj", + "token_texts": [ + " .", + " His", + " are", + " free", + " translations", + " ,", + " which", + " seek", + " to", + " conceal", + " the", + " parallel", + "isms", + " through", + " en", + "j" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.021963970735669136 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.021963970735669136, + "quantile": 0.4443359375, + "active": false, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_102_31", + "text": " to conceal the parallelisms through enjambement and expansion and contraction of the content ; his responses to the allusions", + "token_texts": [ + " to", + " conceal", + " the", + " parallel", + "isms", + " through", + " en", + "j", + "amb", + "ement", + " and", + " expansion", + " and", + " contraction", + " of", + " the", + " content", + " ;", + " his", + " responses", + " to", + " the", + " all", + "usions" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.03985780477523804 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.03985780477523804, + "quantile": 0.5751953125, + "active": false, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_102_47", + "text": " content ; his responses to the allusions are firstly to omit most of these poems from his selection , and secondly to", + "token_texts": [ + " content", + " ;", + " his", + " responses", + " to", + " the", + " all", + "usions", + " are", + " first", + "ly", + " to", + " omit", + " most", + " of", + " these", + " poems", + " from", + " his", + " selection", + " ,", + " and", + " secondly", + " to" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0024285949766635895 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0024285949766635895, + "quantile": 0.1142578125, + "active": false, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_102_62", + "text": " these poems from his selection , and secondly to \" translate out \" the references in those works which he does select . ", + "token_texts": [ + " these", + " poems", + " from", + " his", + " selection", + " ,", + " and", + " secondly", + " to", + " \"", + " translate", + " out", + " \"", + " the", + " references", + " in", + " those", + " works", + " which", + " he", + " does", + " select", + " .", + " " + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.08098405599594116 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.08098405599594116, + "quantile": 0.7392578125, + "active": false, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_103_15", + "text": "<|endoftext|> Other translators have placed much greater weight on trying to convey a sense of", + "token_texts": [ + "<|endoftext|>", + " Other", + " transl", + "ators", + " have", + " placed", + " much", + " greater", + " weight", + " on", + " trying", + " to", + " convey", + " a", + " sense", + " of" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.04488077014684677 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.04488077014684677, + "quantile": 0.6201171875, + "active": false, + "media": {}, + "metadata": { + "sequence": 103, + "split": "test" + } + }, + { + "example_id": "test_103_31", + "text": " weight on trying to convey a sense of the poetic forms used by Du Fu . Vikram Seth in Three Chinese Poets", + "token_texts": [ + " weight", + " on", + " trying", + " to", + " convey", + " a", + " sense", + " of", + " the", + " poetic", + " forms", + " used", + " by", + " Du", + " Fu", + " .", + " Vik", + "ram", + " Seth", + " in", + " Three", + " Chinese", + " Po", + "ets" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.00591646321117878 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.00591646321117878, + "quantile": 0.2158203125, + "active": false, + "media": {}, + "metadata": { + "sequence": 103, + "split": "test" + } + }, + { + "example_id": "test_103_47", + "text": " Vikram Seth in Three Chinese Poets uses English @-@ style rhyme schemes , whereas Keith Holyoak in F", + "token_texts": [ + " Vik", + "ram", + " Seth", + " in", + " Three", + " Chinese", + " Po", + "ets", + " uses", + " English", + " @", + "-", + "@", + " style", + " rhy", + "me", + " schemes", + " ,", + " whereas", + " Keith", + " Holy", + "oak", + " in", + " F" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.030077766627073288 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.030077766627073288, + "quantile": 0.5087890625, + "active": false, + "media": {}, + "metadata": { + "sequence": 103, + "split": "test" + } + }, + { + "example_id": "test_103_62", + "text": "me schemes , whereas Keith Holyoak in Facing the Moon approximates the Chinese rhyme scheme ; both use end @", + "token_texts": [ + "me", + " schemes", + " ,", + " whereas", + " Keith", + " Holy", + "oak", + " in", + " F", + "acing", + " the", + " Moon", + " approx", + "imates", + " the", + " Chinese", + " rhy", + "me", + " scheme", + " ;", + " both", + " use", + " end", + " @" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.15934357047080994 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.15934357047080994, + "quantile": 0.8564453125, + "active": false, + "media": {}, + "metadata": { + "sequence": 103, + "split": "test" + } + }, + { + "example_id": "test_104_15", + "text": "@ stopped lines and preserve some degree of parallelism . In The Selected Poems", + "token_texts": [ + "@", + " stopped", + " lines", + " and", + " preserve", + " some", + " degree", + " of", + " parallel", + "ism", + " .", + " In", + " The", + " Selected", + " Po", + "ems" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.006050739903002977 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.006050739903002977, + "quantile": 0.2197265625, + "active": false, + "media": {}, + "metadata": { + "sequence": 104, + "split": "test" + } + }, + { + "example_id": "test_104_31", + "text": " parallelism . 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The equivalent statistics for the 18 @.@ 5 @-@", + "token_texts": [ + " @", + "-", + "@", + " pound", + " (", + " 50", + " kg", + " )", + " explosive", + " shells", + " .", + " The", + " equivalent", + " statistics", + " for", + " the", + " 18", + " @", + ".", + "@", + " 5", + " @", + "-", + "@" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 22.73567771911621 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 22.73567771911621, + "quantile": 0.819091796875, + "active": false, + "media": {}, + "metadata": { + "sequence": 263, + "split": "train" + } + }, + { + "example_id": "train_263_62", + "text": " the 18 @.@ 5 @-@ caliber , 6 @.@ 4 @-@ inch gun were 9 @", + "token_texts": [ + " the", + " 18", + " @", + ".", + "@", + " 5", + " @", + "-", + "@", + " caliber", + " ,", + " 6", + " @", + ".", + "@", + " 4", + " @", + "-", + "@", + " inch", + " gun", + " were", + " 9", + " @" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 4.601607322692871 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 4.601607322692871, + "quantile": 0.489501953125, + "active": false, + "media": {}, + "metadata": { + "sequence": 263, + "split": "train" + } + }, + { + "example_id": "train_264_15", + "text": "@ 110 pounds ( 4 @,@ 130 kg ) with 80 @-@", + "token_texts": [ + "@", + " 110", + " pounds", + " (", + " 4", + " @", + ",", + "@", + " 130", + " kg", + " )", + " with", + " 80", + " @", + "-", + "@" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 3.063373565673828 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 3.063373565673828, + "quantile": 0.339111328125, + "active": false, + "media": {}, + "metadata": { + "sequence": 264, + "split": "train" + } + }, + { + "example_id": "train_264_31", + "text": " 130 kg ) with 80 @-@ pound bolts and 64 @-@ pound ( 29 kg ) shells . 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In front of the ram was a spar torpedo that carried 50 pounds ( 23", + "token_texts": [ + " .", + " In", + " front", + " of", + " the", + " ram", + " was", + " a", + " spar", + " torpedo", + " that", + " carried", + " 50", + " pounds", + " (", + " 23" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 44.897850036621094 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 44.897850036621094, + "quantile": 0.895263671875, + "active": false, + "media": {}, + "metadata": { + "sequence": 265, + "split": "train" + } + }, + { + "example_id": "train_265_31", + "text": " spar torpedo that carried 50 pounds ( 23 kg ) of black powder on a wooden pole connected to an iron lever that could", + "token_texts": [ + " spar", + " torpedo", + " that", + " carried", + " 50", + " pounds", + " (", + " 23", + " kg", + " )", + " of", + " black", + " powder", + " on", + " a", + " wooden", + " pole", + " connected", + " to", + " an", + " iron", + " lever", + " that", + " could" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 3.4787216186523438 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 3.4787216186523438, + "quantile": 0.393310546875, + "active": false, + "media": {}, + "metadata": { + "sequence": 265, + "split": "train" + } + }, + { + "example_id": "train_265_47", + "text": " pole connected to an iron lever that could be raised or lowered by means of pulleys . \n<|endoftext|> On 31 July", + "token_texts": [ + " pole", + " connected", + " to", + " an", + " iron", + " lever", + " that", + " could", + " be", + " raised", + " or", + " lowered", + " by", + " means", + " of", + " pul", + "leys", + " .", + " ", + "\n", + "<|endoftext|>", + " On", + " 31", + " July" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 5.346898078918457 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 5.346898078918457, + "quantile": 0.530029296875, + "active": false, + "media": {}, + "metadata": { + "sequence": 265, + "split": "train" + } + }, + { + "example_id": "train_265_62", + "text": " pulleys . \n<|endoftext|> On 31 July 1862 , under the command of Lieutenant Charles H. 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His are free translations , which seek to conceal the parallelisms through enj", + "token_texts": [ + " .", + " His", + " are", + " free", + " translations", + " ,", + " which", + " seek", + " to", + " conceal", + " the", + " parallel", + "isms", + " through", + " en", + "j" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 10.659202575683594 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 10.659202575683594, + "quantile": 0.7275390625, + "active": false, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_102_31", + "text": " to conceal the parallelisms through enjambement and expansion and contraction of the content ; his responses to the allusions", + "token_texts": [ + " to", + " conceal", + " the", + " parallel", + "isms", + " through", + " en", + "j", + "amb", + "ement", + " and", + " expansion", + " and", + " contraction", + " of", + " the", + " content", + " ;", + " his", + " responses", + " to", + " the", + " all", + "usions" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 100.19828033447266 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 100.19828033447266, + "quantile": 0.9580078125, + "active": true, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_102_47", + "text": " content ; his responses to the allusions are firstly to omit most of these poems from his selection , and secondly to", + "token_texts": [ + " content", + " ;", + " his", + " responses", + " to", + " the", + " all", + "usions", + " are", + " first", + "ly", + " to", + " omit", + " most", + " of", + " these", + " poems", + " from", + " his", + " selection", + " ,", + " and", + " secondly", + " to" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 6.390223026275635 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 6.390223026275635, + "quantile": 0.6279296875, + "active": false, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_102_62", + "text": " these poems from his selection , and secondly to \" translate out \" the references in those works which he does select . 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The equivalent statistics for the 18 @.@ 5 @-@", + "token_texts": [ + " @", + "-", + "@", + " pound", + " (", + " 50", + " kg", + " )", + " explosive", + " shells", + " .", + " The", + " equivalent", + " statistics", + " for", + " the", + " 18", + " @", + ".", + "@", + " 5", + " @", + "-", + "@" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 1.0868964195251465 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 1.0868964195251465, + "quantile": 0.189208984375, + "active": false, + "media": {}, + "metadata": { + "sequence": 263, + "split": "train" + } + }, + { + "example_id": "train_263_62", + "text": " the 18 @.@ 5 @-@ caliber , 6 @.@ 4 @-@ inch gun were 9 @", + "token_texts": [ + " the", + " 18", + " @", + ".", + "@", + " 5", + " @", + "-", + "@", + " caliber", + " ,", + " 6", + " @", + ".", + "@", + " 4", + " @", + "-", + "@", + " inch", + " gun", + " were", + " 9", + " @" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 1.719470739364624 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 1.719470739364624, + "quantile": 0.447021484375, + "active": false, + "media": {}, + "metadata": { + "sequence": 263, + "split": "train" + } + }, + { + "example_id": "train_264_15", + "text": "@ 110 pounds ( 4 @,@ 130 kg ) with 80 @-@", + "token_texts": [ + "@", + " 110", + " pounds", + " (", + " 4", + " @", + ",", + "@", + " 130", + " kg", + " )", + " with", + " 80", + " @", + "-", + "@" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 5.403878211975098 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 5.403878211975098, + "quantile": 0.797119140625, + "active": false, + "media": {}, + "metadata": { + "sequence": 264, + "split": "train" + } + }, + { + "example_id": "train_264_31", + "text": " 130 kg ) with 80 @-@ pound bolts and 64 @-@ pound ( 29 kg ) shells . 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His are free translations , which seek to conceal the parallelisms through enj", + "token_texts": [ + " .", + " His", + " are", + " free", + " translations", + " ,", + " which", + " seek", + " to", + " conceal", + " the", + " parallel", + "isms", + " through", + " en", + "j" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 13.77898120880127 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 13.77898120880127, + "quantile": 0.9189453125, + "active": true, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_102_31", + "text": " to conceal the parallelisms through enjambement and expansion and contraction of the content ; his responses to the allusions", + "token_texts": [ + " to", + " conceal", + " the", + " parallel", + "isms", + " through", + " en", + "j", + "amb", + "ement", + " and", + " expansion", + " and", + " contraction", + " of", + " the", + " content", + " ;", + " his", + " responses", + " to", + " the", + " all", + "usions" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.9635605812072754 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.9635605812072754, + "quantile": 0.1259765625, + "active": false, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_102_47", + "text": " content ; his responses to the allusions are firstly to omit most of these poems from his selection , and secondly to", + "token_texts": [ + " content", + " ;", + " his", + " responses", + " to", + " the", + " all", + "usions", + " are", + " first", + "ly", + " to", + " omit", + " most", + " of", + " these", + " poems", + " from", + " his", + " selection", + " ,", + " and", + " secondly", + " to" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 184.32315063476562 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 184.32315063476562, + "quantile": 0.9892578125, + "active": true, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_102_62", + "text": " these poems from his selection , and secondly to \" translate out \" the references in those works which he does select . 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Vikram Seth in Three Chinese Poets", + "token_texts": [ + " weight", + " on", + " trying", + " to", + " convey", + " a", + " sense", + " of", + " the", + " poetic", + " forms", + " used", + " by", + " Du", + " Fu", + " .", + " Vik", + "ram", + " Seth", + " in", + " Three", + " Chinese", + " Po", + "ets" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 1.0606502294540405 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 1.0606502294540405, + "quantile": 0.1708984375, + "active": false, + "media": {}, + "metadata": { + "sequence": 103, + "split": "test" + } + }, + { + "example_id": "test_103_47", + "text": " Vikram Seth in Three Chinese Poets uses English @-@ style rhyme schemes , whereas Keith Holyoak in F", + "token_texts": [ + " Vik", + "ram", + " Seth", + " in", + " Three", + " Chinese", + " Po", + "ets", + " uses", + " English", + " @", + "-", + "@", + " style", + " rhy", + "me", + " schemes", + " ,", + " whereas", + " Keith", + " Holy", + "oak", + " in", + " F" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 19.30095672607422 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 19.30095672607422, + "quantile": 0.9501953125, + "active": true, + "media": {}, + "metadata": { + "sequence": 103, + "split": "test" + } + }, + { + "example_id": "test_103_62", + "text": "me schemes , whereas Keith Holyoak in Facing the Moon approximates the Chinese rhyme scheme ; both use end @", + "token_texts": [ + "me", + " schemes", + " ,", + " whereas", + " Keith", + " Holy", + "oak", + " in", + " F", + "acing", + " the", + " Moon", + " approx", + "imates", + " the", + " Chinese", + " rhy", + "me", + " scheme", + " ;", + " both", + " use", + " end", + " @" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 1.5983120203018188 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 1.5983120203018188, + "quantile": 0.4072265625, + "active": false, + "media": {}, + "metadata": { + "sequence": 103, + "split": "test" + } + }, + { + "example_id": "test_104_15", + "text": "@ stopped lines and preserve some degree of parallelism . In The Selected Poems", + "token_texts": [ + "@", + " stopped", + " lines", + " and", + " preserve", + " some", + " degree", + " of", + " parallel", + "ism", + " .", + " In", + " The", + " Selected", + " Po", + "ems" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.8739309906959534 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.8739309906959534, + "quantile": 0.0966796875, + "active": false, + "media": {}, + "metadata": { + "sequence": 104, + "split": "test" + } + }, + { + "example_id": "test_104_31", + "text": " parallelism . 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In front of the ram was a spar torpedo that carried 50 pounds ( 23", + "token_texts": [ + " .", + " In", + " front", + " of", + " the", + " ram", + " was", + " a", + " spar", + " torpedo", + " that", + " carried", + " 50", + " pounds", + " (", + " 23" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 5.324897766113281 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 5.324897766113281, + "quantile": 0.644287109375, + "active": false, + "media": {}, + "metadata": { + "sequence": 265, + "split": "train" + } + }, + { + "example_id": "train_265_31", + "text": " spar torpedo that carried 50 pounds ( 23 kg ) of black powder on a wooden pole connected to an iron lever that could", + "token_texts": [ + " spar", + " torpedo", + " that", + " carried", + " 50", + " pounds", + " (", + " 23", + " kg", + " )", + " of", + " black", + " powder", + " on", + " a", + " wooden", + " pole", + " connected", + " to", + " an", + " iron", + " lever", + " that", + " could" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 1.027998924255371 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 1.027998924255371, + "quantile": 0.188720703125, + "active": false, + "media": {}, + "metadata": { + "sequence": 265, + "split": "train" + } + }, + { + "example_id": "train_265_47", + "text": " pole connected to an iron lever that could be raised or lowered by means of pulleys . \n<|endoftext|> On 31 July", + "token_texts": [ + " pole", + " connected", + " to", + " an", + " iron", + " lever", + " that", + " could", + " be", + " raised", + " or", + " lowered", + " by", + " means", + " of", + " pul", + "leys", + " .", + " ", + "\n", + "<|endoftext|>", + " On", + " 31", + " July" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 1.0331432819366455 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 1.0331432819366455, + "quantile": 0.191650390625, + "active": false, + "media": {}, + "metadata": { + "sequence": 265, + "split": "train" + } + }, + { + "example_id": "train_265_62", + "text": " pulleys . \n<|endoftext|> On 31 July 1862 , under the command of Lieutenant Charles H. 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His are free translations , which seek to conceal the parallelisms through enj", + "token_texts": [ + " .", + " His", + " are", + " free", + " translations", + " ,", + " which", + " seek", + " to", + " conceal", + " the", + " parallel", + "isms", + " through", + " en", + "j" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 1.7195303440093994 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 1.7195303440093994, + "quantile": 0.3251953125, + "active": false, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_102_31", + "text": " to conceal the parallelisms through enjambement and expansion and contraction of the content ; his responses to the allusions", + "token_texts": [ + " to", + " conceal", + " the", + " parallel", + "isms", + " through", + " en", + "j", + "amb", + "ement", + " and", + " expansion", + " and", + " contraction", + " of", + " the", + " content", + " ;", + " his", + " responses", + " to", + " the", + " all", + "usions" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.4822652339935303 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.4822652339935303, + "quantile": 0.0068359375, + "active": false, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_102_47", + "text": " content ; his responses to the allusions are firstly to omit most of these poems from his selection , and secondly to", + "token_texts": [ + " content", + " ;", + " his", + " responses", + " to", + " the", + " all", + "usions", + " are", + " first", + "ly", + " to", + " omit", + " most", + " of", + " these", + " poems", + " from", + " his", + " selection", + " ,", + " and", + " secondly", + " to" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 2.2071094512939453 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 2.2071094512939453, + "quantile": 0.3818359375, + "active": false, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_102_62", + "text": " these poems from his selection , and secondly to \" translate out \" the references in those works which he does select . 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In The Selected Poems", + "token_texts": [ + "@", + " stopped", + " lines", + " and", + " preserve", + " some", + " degree", + " of", + " parallel", + "ism", + " .", + " In", + " The", + " Selected", + " Po", + "ems" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.802670955657959 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.802670955657959, + "quantile": 0.0634765625, + "active": false, + "media": {}, + "metadata": { + "sequence": 104, + "split": "test" + } + }, + { + "example_id": "test_104_31", + "text": " parallelism . 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The equivalent statistics for the 18 @.@ 5 @-@", + "token_texts": [ + " @", + "-", + "@", + " pound", + " (", + " 50", + " kg", + " )", + " explosive", + " shells", + " .", + " The", + " equivalent", + " statistics", + " for", + " the", + " 18", + " @", + ".", + "@", + " 5", + " @", + "-", + "@" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 67.21125793457031 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 67.21125793457031, + "quantile": 0.994384765625, + "active": true, + "media": {}, + "metadata": { + "sequence": 263, + "split": "train" + } + }, + { + "example_id": "train_263_62", + "text": " the 18 @.@ 5 @-@ caliber , 6 @.@ 4 @-@ inch gun were 9 @", + "token_texts": [ + " the", + " 18", + " @", + ".", + "@", + " 5", + " @", + "-", + "@", + " caliber", + " ,", + " 6", + " @", + ".", + "@", + " 4", + " @", + "-", + "@", + " inch", + " gun", + " were", + " 9", + " @" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 34.94841003417969 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 34.94841003417969, + "quantile": 0.947021484375, + "active": true, + "media": {}, + "metadata": { + "sequence": 263, + "split": "train" + } + }, + { + "example_id": "train_264_15", + "text": "@ 110 pounds ( 4 @,@ 130 kg ) with 80 @-@", + "token_texts": [ + "@", + " 110", + " pounds", + " (", + " 4", + " @", + ",", + "@", + " 130", + " kg", + " )", + " with", + " 80", + " @", + "-", + "@" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 2.9555721282958984 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 2.9555721282958984, + "quantile": 0.388427734375, + "active": false, + "media": {}, + "metadata": { + "sequence": 264, + "split": "train" + } + }, + { + "example_id": "train_264_31", + "text": " 130 kg ) with 80 @-@ pound bolts and 64 @-@ pound ( 29 kg ) shells . 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His are free translations , which seek to conceal the parallelisms through enj", + "token_texts": [ + " .", + " His", + " are", + " free", + " translations", + " ,", + " which", + " seek", + " to", + " conceal", + " the", + " parallel", + "isms", + " through", + " en", + "j" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.37276607751846313 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.37276607751846313, + "quantile": 0.0380859375, + "active": false, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_102_31", + "text": " to conceal the parallelisms through enjambement and expansion and contraction of the content ; his responses to the allusions", + "token_texts": [ + " to", + " conceal", + " the", + " parallel", + "isms", + " through", + " en", + "j", + "amb", + "ement", + " and", + " expansion", + " and", + " contraction", + " of", + " the", + " content", + " ;", + " his", + " responses", + " to", + " the", + " all", + "usions" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.26694804430007935 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.26694804430007935, + "quantile": 0.0244140625, + "active": false, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_102_47", + "text": " content ; his responses to the allusions are firstly to omit most of these poems from his selection , and secondly to", + "token_texts": [ + " content", + " ;", + " his", + " responses", + " to", + " the", + " all", + "usions", + " are", + " first", + "ly", + " to", + " omit", + " most", + " of", + " these", + " poems", + " from", + " his", + " selection", + " ,", + " and", + " secondly", + " to" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.30711355805397034 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.30711355805397034, + "quantile": 0.0283203125, + "active": false, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_102_62", + "text": " these poems from his selection , and secondly to \" translate out \" the references in those works which he does select . ", + "token_texts": [ + " these", + " poems", + " from", + " his", + " selection", + " ,", + " and", + " secondly", + " to", + " \"", + " translate", + " out", + " \"", + " the", + " references", + " in", + " those", + " works", + " which", + " he", + " does", + " select", + " .", + " " + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 4.869932174682617 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 4.869932174682617, + "quantile": 0.4892578125, + "active": false, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_103_15", + "text": "<|endoftext|> Other translators have placed much greater weight on trying to convey a sense of", + "token_texts": [ + "<|endoftext|>", + " Other", + " transl", + "ators", + " have", + " placed", + " much", + " greater", + " weight", + " on", + " trying", + " to", + " convey", + " a", + " sense", + " of" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 3.262995719909668 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 3.262995719909668, + "quantile": 0.3955078125, + "active": false, + "media": {}, + "metadata": { + "sequence": 103, + "split": "test" + } + }, + { + "example_id": "test_103_31", + "text": " weight on trying to convey a sense of the poetic forms used by Du Fu . Vikram Seth in Three Chinese Poets", + "token_texts": [ + " weight", + " on", + " trying", + " to", + " convey", + " a", + " sense", + " of", + " the", + " poetic", + " forms", + " used", + " by", + " Du", + " Fu", + " .", + " Vik", + "ram", + " Seth", + " in", + " Three", + " Chinese", + " Po", + "ets" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 1.399984359741211 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 1.399984359741211, + "quantile": 0.1943359375, + "active": false, + "media": {}, + "metadata": { + "sequence": 103, + "split": "test" + } + }, + { + "example_id": "test_103_47", + "text": " Vikram Seth in Three Chinese Poets uses English @-@ style rhyme schemes , whereas Keith Holyoak in F", + "token_texts": [ + " Vik", + "ram", + " Seth", + " in", + " Three", + " Chinese", + " Po", + "ets", + " uses", + " English", + " @", + "-", + "@", + " style", + " rhy", + "me", + " schemes", + " ,", + " whereas", + " Keith", + " Holy", + "oak", + " in", + " F" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.1409144252538681 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.1409144252538681, + "quantile": 0.0087890625, + "active": false, + "media": {}, + "metadata": { + "sequence": 103, + "split": "test" + } + }, + { + "example_id": "test_103_62", + "text": "me schemes , whereas Keith Holyoak in Facing the Moon approximates the Chinese rhyme scheme ; both use end @", + "token_texts": [ + "me", + " schemes", + " ,", + " whereas", + " Keith", + " Holy", + "oak", + " in", + " F", + "acing", + " the", + " Moon", + " approx", + "imates", + " the", + " Chinese", + " rhy", + "me", + " scheme", + " ;", + " both", + " use", + " end", + " @" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 3.0889344215393066 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 3.0889344215393066, + "quantile": 0.3779296875, + "active": false, + "media": {}, + "metadata": { + "sequence": 103, + "split": "test" + } + }, + { + "example_id": "test_104_15", + "text": "@ stopped lines and preserve some degree of parallelism . In The Selected Poems", + "token_texts": [ + "@", + " stopped", + " lines", + " and", + " preserve", + " some", + " degree", + " of", + " parallel", + "ism", + " .", + " In", + " The", + " Selected", + " Po", + "ems" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 4.804898738861084 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 4.804898738861084, + "quantile": 0.4833984375, + "active": false, + "media": {}, + "metadata": { + "sequence": 104, + "split": "test" + } + }, + { + "example_id": "test_104_31", + "text": " parallelism . 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"cluster_count_fixed": true, + "largest_core_module_effective_rank": null, + "module_dimension_profile": null, + "threshold": 0.9, + "max_module_fraction": 0.5, + "n_core_modules": 65, + "rest_size": 2512, + "core_energy_share_test": 0.5808143019676208, + "largest_core_module": 65, + "median_core_module_size": 4.0, + "leakage_test": 0.7586692722258054 + }, + { + "metric": "fisher", + "affinity": "square", + "knn": 0, + "cluster_count": 64, + "cluster_count_fixed": true, + "largest_core_module_effective_rank": null, + "module_dimension_profile": null, + "threshold": 0.9, + "max_module_fraction": 1.0, + "n_core_modules": 65, + "rest_size": 2512, + "core_energy_share_test": 0.5808143019676208, + "largest_core_module": 65, + "median_core_module_size": 4.0, + "leakage_test": 0.7586692722258054 + }, + { + "metric": "fisher", + "affinity": "square", + "knn": 0, + "cluster_count": 64, + "cluster_count_fixed": true, + "largest_core_module_effective_rank": null, + "module_dimension_profile": null, + "threshold": 1.0, + "max_module_fraction": 0.5, + "n_core_modules": 73, + "rest_size": 2691, + "core_energy_share_test": 0.45882824063301086, + "largest_core_module": 44, + "median_core_module_size": 3.0, + "leakage_test": 0.6857395708728623 + }, + { + "metric": "fisher", + "affinity": "square", + "knn": 0, + "cluster_count": 64, + "cluster_count_fixed": true, + "largest_core_module_effective_rank": null, + "module_dimension_profile": null, + "threshold": 1.0, + "max_module_fraction": 1.0, + "n_core_modules": 73, + "rest_size": 2691, + "core_energy_share_test": 0.45882824063301086, + "largest_core_module": 44, + "median_core_module_size": 3.0, + "leakage_test": 0.6857395708728623 + } + ], + "settings": { + "root": "results/comparison/gpt2", + "out": "results/modules/gpt2", + "metrics": [ + "l2", + "fisher" + ], + "bootstraps": 12, + "affinities": [ + "square", + "abs" + ], + "knns": [ + 0, + 10, + 30 + ], + "extra_clusters": [ + 16, + 64 + ], + "thresholds": [ + 0.6, + 0.7, + 0.8, + 0.9, + 1.0 + ], + "dimension_threshold": 0.8, + "dimension_max_modules": 400, + "dimension_max_size": 256, + "fractions": [ + 0.5, + 1.0 + ], + "normalization": "fcm", + "min_size": 2, + "restarts": 5, + "seed": 17 + }, + "scope": "core-module count and held-out energy share only; no dimension, width, semantic or causal evaluation" +} \ No newline at end of file diff --git a/results/modules/partition_gpt2.log b/results/modules/partition_gpt2.log new file mode 100644 index 0000000000000000000000000000000000000000..11778e145657c5786a8853954c1534ca0d0da7b2 --- /dev/null +++ b/results/modules/partition_gpt2.log @@ -0,0 +1,30 @@ +2026-09-08 13:49:43 UTC — gpt2: partition, metrics=['l2', 'fisher'], B=12, affinity=square, knn=0 +2026-09-08 13:49:45 UTC — gpt2 l2: bootstrap 1/12 C=10 sizes=[2799, 88, 49, 40, 27, 21, 16, 13] +2026-09-08 13:49:45 UTC — gpt2 l2: bootstrap 2/12 C=10 sizes=[2745, 95, 65, 41, 30, 23, 20, 20] +2026-09-08 13:49:45 UTC — gpt2 l2: bootstrap 3/12 C=10 sizes=[2622, 201, 90, 33, 32, 28, 23, 17] +2026-09-08 13:49:45 UTC — gpt2 l2: bootstrap 4/12 C=10 sizes=[2828, 60, 46, 44, 22, 19, 16, 15] +2026-09-08 13:49:46 UTC — gpt2 l2: bootstrap 5/12 C=10 sizes=[2879, 42, 39, 36, 25, 16, 13, 12] +2026-09-08 13:49:46 UTC — gpt2 l2: bootstrap 6/12 C=10 sizes=[2745, 88, 61, 45, 38, 26, 22, 20] +2026-09-08 13:49:46 UTC — gpt2 l2: bootstrap 7/12 C=10 sizes=[2797, 75, 57, 39, 31, 22, 17, 14] +2026-09-08 13:49:46 UTC — gpt2 l2: bootstrap 8/12 C=10 sizes=[2657, 132, 77, 43, 35, 33, 29, 26] +2026-09-08 13:49:47 UTC — gpt2 l2: bootstrap 9/12 C=10 sizes=[2630, 124, 77, 74, 34, 33, 32, 24] +2026-09-08 13:49:47 UTC — gpt2 l2: bootstrap 10/12 C=10 sizes=[2802, 85, 42, 40, 28, 23, 19, 15] +2026-09-08 13:49:47 UTC — gpt2 l2: bootstrap 11/12 C=10 sizes=[2744, 98, 67, 42, 32, 25, 24, 15] +2026-09-08 13:49:48 UTC — gpt2 l2: bootstrap 12/12 C=10 sizes=[2754, 95, 71, 33, 33, 23, 23, 14] +2026-09-08 13:49:48 UTC — gpt2 l2: reference C=10 (eigengap), 11 core modules, rest 2935/3072 +2026-09-08 13:49:48 UTC — gpt2 l2: 11 core modules, rest 2935, 5.4s +2026-09-08 13:49:56 UTC — gpt2 fisher: bootstrap 1/12 C=4 sizes=[2611, 233, 196, 32] +2026-09-08 13:49:56 UTC — gpt2 fisher: bootstrap 2/12 C=4 sizes=[2093, 755, 174, 50] +2026-09-08 13:49:57 UTC — gpt2 fisher: bootstrap 3/12 C=4 sizes=[1979, 855, 196, 42] +2026-09-08 13:49:57 UTC — gpt2 fisher: bootstrap 4/12 C=4 sizes=[2610, 329, 82, 51] +2026-09-08 13:49:57 UTC — gpt2 fisher: bootstrap 5/12 C=4 sizes=[2931, 105, 19, 17] +2026-09-08 13:49:57 UTC — gpt2 fisher: bootstrap 6/12 C=4 sizes=[2803, 225, 30, 14] +2026-09-08 13:49:58 UTC — gpt2 fisher: bootstrap 7/12 C=4 sizes=[2210, 638, 180, 44] +2026-09-08 13:49:58 UTC — gpt2 fisher: bootstrap 8/12 C=4 sizes=[2884, 147, 27, 14] +2026-09-08 13:49:58 UTC — gpt2 fisher: bootstrap 9/12 C=4 sizes=[2671, 322, 46, 33] +2026-09-08 13:49:58 UTC — gpt2 fisher: bootstrap 10/12 C=4 sizes=[2363, 486, 196, 27] +2026-09-08 13:49:59 UTC — gpt2 fisher: bootstrap 11/12 C=4 sizes=[2316, 529, 183, 44] +2026-09-08 13:49:59 UTC — gpt2 fisher: bootstrap 12/12 C=4 sizes=[2224, 613, 179, 56] +2026-09-08 13:49:59 UTC — gpt2 fisher: reference C=4 (eigengap), 2 core modules, rest 3041/3072 +2026-09-08 13:49:59 UTC — gpt2 fisher: 2 core modules, rest 3041, 11.0s +2026-09-08 13:49:59 UTC — gpt2: partition complete diff --git a/results/modules/partition_qwen.log b/results/modules/partition_qwen.log new file mode 100644 index 0000000000000000000000000000000000000000..2ca07274414ab0a2a30cd382e690160fe4fcd362 --- /dev/null +++ b/results/modules/partition_qwen.log @@ -0,0 +1,30 @@ +2026-09-08 13:52:45 UTC — qwen: partition, metrics=['l2', 'fisher'], B=12, affinity=square, knn=0 +2026-09-08 13:52:50 UTC — qwen l2: bootstrap 1/12 C=6 sizes=[2911, 1648, 215, 43, 31, 16] +2026-09-08 13:52:51 UTC — qwen l2: bootstrap 2/12 C=6 sizes=[2684, 1749, 264, 72, 56, 39] +2026-09-08 13:52:51 UTC — qwen l2: bootstrap 3/12 C=6 sizes=[2681, 1761, 261, 70, 50, 41] +2026-09-08 13:52:52 UTC — qwen l2: bootstrap 4/12 C=6 sizes=[2607, 1800, 259, 92, 68, 38] +2026-09-08 13:52:53 UTC — qwen l2: bootstrap 5/12 C=6 sizes=[2695, 1789, 235, 55, 53, 37] +2026-09-08 13:52:53 UTC — qwen l2: bootstrap 6/12 C=6 sizes=[2061, 1848, 667, 184, 61, 43] +2026-09-08 13:52:54 UTC — qwen l2: bootstrap 7/12 C=6 sizes=[2683, 1785, 258, 65, 40, 33] +2026-09-08 13:52:54 UTC — qwen l2: bootstrap 8/12 C=6 sizes=[2687, 1827, 245, 59, 39, 7] +2026-09-08 13:52:55 UTC — qwen l2: bootstrap 9/12 C=6 sizes=[1707, 1436, 1113, 444, 121, 43] +2026-09-08 13:52:56 UTC — qwen l2: bootstrap 10/12 C=6 sizes=[2176, 1912, 587, 142, 39, 8] +2026-09-08 13:52:56 UTC — qwen l2: bootstrap 11/12 C=6 sizes=[1752, 1437, 1171, 361, 101, 42] +2026-09-08 13:52:57 UTC — qwen l2: bootstrap 12/12 C=6 sizes=[2741, 1825, 205, 40, 38, 15] +2026-09-08 13:52:57 UTC — qwen l2: reference C=6 (eigengap), 2 core modules, rest 4822/4864 +2026-09-08 13:52:57 UTC — qwen l2: 2 core modules, rest 4822, 12.8s +2026-09-08 13:53:13 UTC — qwen fisher: bootstrap 1/12 C=2 sizes=[4819, 45] +2026-09-08 13:53:13 UTC — qwen fisher: bootstrap 2/12 C=2 sizes=[4834, 30] +2026-09-08 13:53:14 UTC — qwen fisher: bootstrap 3/12 C=2 sizes=[4834, 30] +2026-09-08 13:53:14 UTC — qwen fisher: bootstrap 4/12 C=2 sizes=[4783, 81] +2026-09-08 13:53:15 UTC — qwen fisher: bootstrap 5/12 C=2 sizes=[4779, 85] +2026-09-08 13:53:15 UTC — qwen fisher: bootstrap 6/12 C=2 sizes=[4837, 27] +2026-09-08 13:53:16 UTC — qwen fisher: bootstrap 7/12 C=2 sizes=[4795, 69] +2026-09-08 13:53:16 UTC — qwen fisher: bootstrap 8/12 C=2 sizes=[4390, 474] +2026-09-08 13:53:17 UTC — qwen fisher: bootstrap 9/12 C=2 sizes=[3670, 1194] +2026-09-08 13:53:18 UTC — qwen fisher: bootstrap 10/12 C=2 sizes=[4839, 25] +2026-09-08 13:53:18 UTC — qwen fisher: bootstrap 11/12 C=2 sizes=[4827, 37] +2026-09-08 13:53:19 UTC — qwen fisher: bootstrap 12/12 C=2 sizes=[4829, 35] +2026-09-08 13:53:21 UTC — qwen fisher: reference C=2 (eigengap), 0 core modules, rest 4864/4864 +2026-09-08 13:53:21 UTC — qwen fisher: 0 core modules, rest 4864, 23.5s +2026-09-08 13:53:21 UTC — qwen: partition complete diff --git a/results/modules/partition_random_gpt2.log b/results/modules/partition_random_gpt2.log new file mode 100644 index 0000000000000000000000000000000000000000..6800587f659007cd81c658abbf152a19031b05d6 --- /dev/null +++ b/results/modules/partition_random_gpt2.log @@ -0,0 +1,30 @@ +2026-09-08 13:58:49 UTC — random_gpt2: partition, metrics=['l2', 'fisher'], B=12, affinity=square, knn=0 +2026-09-08 13:58:52 UTC — random_gpt2 l2: bootstrap 1/12 C=3 sizes=[1524, 775, 773] +2026-09-08 13:58:52 UTC — random_gpt2 l2: bootstrap 2/12 C=3 sizes=[1559, 784, 729] +2026-09-08 13:58:52 UTC — random_gpt2 l2: bootstrap 3/12 C=3 sizes=[1520, 778, 774] +2026-09-08 13:58:52 UTC — random_gpt2 l2: bootstrap 4/12 C=3 sizes=[1521, 778, 773] +2026-09-08 13:58:52 UTC — random_gpt2 l2: bootstrap 5/12 C=3 sizes=[1478, 815, 779] +2026-09-08 13:58:53 UTC — random_gpt2 l2: bootstrap 6/12 C=3 sizes=[1502, 793, 777] +2026-09-08 13:58:53 UTC — random_gpt2 l2: bootstrap 7/12 C=3 sizes=[1495, 803, 774] +2026-09-08 13:58:53 UTC — random_gpt2 l2: bootstrap 8/12 C=3 sizes=[1573, 762, 737] +2026-09-08 13:58:53 UTC — random_gpt2 l2: bootstrap 9/12 C=3 sizes=[1489, 827, 756] +2026-09-08 13:58:54 UTC — random_gpt2 l2: bootstrap 10/12 C=3 sizes=[1462, 832, 778] +2026-09-08 13:58:54 UTC — random_gpt2 l2: bootstrap 11/12 C=3 sizes=[1537, 808, 727] +2026-09-08 13:58:54 UTC — random_gpt2 l2: bootstrap 12/12 C=3 sizes=[1492, 808, 772] +2026-09-08 13:58:54 UTC — random_gpt2 l2: reference C=3 (eigengap), 3 core modules, rest 3066/3072 +2026-09-08 13:58:54 UTC — random_gpt2 l2: 3 core modules, rest 3066, 4.9s +2026-09-08 13:59:04 UTC — random_gpt2 fisher: bootstrap 1/12 C=2 sizes=[2037, 1035] +2026-09-08 13:59:04 UTC — random_gpt2 fisher: bootstrap 2/12 C=2 sizes=[2052, 1020] +2026-09-08 13:59:04 UTC — random_gpt2 fisher: bootstrap 3/12 C=2 sizes=[1922, 1150] +2026-09-08 13:59:04 UTC — random_gpt2 fisher: bootstrap 4/12 C=2 sizes=[2030, 1042] +2026-09-08 13:59:04 UTC — random_gpt2 fisher: bootstrap 5/12 C=2 sizes=[1554, 1518] +2026-09-08 13:59:05 UTC — random_gpt2 fisher: bootstrap 6/12 C=2 sizes=[1616, 1456] +2026-09-08 13:59:05 UTC — random_gpt2 fisher: bootstrap 7/12 C=2 sizes=[1881, 1191] +2026-09-08 13:59:05 UTC — random_gpt2 fisher: bootstrap 8/12 C=2 sizes=[2096, 976] +2026-09-08 13:59:05 UTC — random_gpt2 fisher: bootstrap 9/12 C=2 sizes=[1750, 1322] +2026-09-08 13:59:05 UTC — random_gpt2 fisher: bootstrap 10/12 C=2 sizes=[1537, 1535] +2026-09-08 13:59:06 UTC — random_gpt2 fisher: bootstrap 11/12 C=2 sizes=[2058, 1014] +2026-09-08 13:59:06 UTC — random_gpt2 fisher: bootstrap 12/12 C=2 sizes=[1895, 1177] +2026-09-08 13:59:06 UTC — random_gpt2 fisher: reference C=2 (eigengap), 0 core modules, rest 3072/3072 +2026-09-08 13:59:06 UTC — random_gpt2 fisher: 0 core modules, rest 3072, 11.7s +2026-09-08 13:59:06 UTC — random_gpt2: partition complete diff --git a/results/modules/qwen/MONITOR.md b/results/modules/qwen/MONITOR.md new file mode 100644 index 0000000000000000000000000000000000000000..b692639e12f2fcc06a201869bb9967422842eef7 --- /dev/null +++ b/results/modules/qwen/MONITOR.md @@ -0,0 +1,162 @@ + +- 2026-09-08 13:52:45 UTC — qwen: partition, metrics=['l2', 'fisher'], B=12, affinity=square, knn=0 + +- 2026-09-08 13:52:50 UTC — qwen l2: bootstrap 1/12 C=6 sizes=[2911, 1648, 215, 43, 31, 16] + +- 2026-09-08 13:52:51 UTC — qwen l2: bootstrap 2/12 C=6 sizes=[2684, 1749, 264, 72, 56, 39] + +- 2026-09-08 13:52:51 UTC — qwen l2: bootstrap 3/12 C=6 sizes=[2681, 1761, 261, 70, 50, 41] + +- 2026-09-08 13:52:52 UTC — qwen l2: bootstrap 4/12 C=6 sizes=[2607, 1800, 259, 92, 68, 38] + +- 2026-09-08 13:52:53 UTC — qwen l2: bootstrap 5/12 C=6 sizes=[2695, 1789, 235, 55, 53, 37] + +- 2026-09-08 13:52:53 UTC — qwen l2: bootstrap 6/12 C=6 sizes=[2061, 1848, 667, 184, 61, 43] + +- 2026-09-08 13:52:54 UTC — qwen l2: bootstrap 7/12 C=6 sizes=[2683, 1785, 258, 65, 40, 33] + +- 2026-09-08 13:52:54 UTC — qwen l2: bootstrap 8/12 C=6 sizes=[2687, 1827, 245, 59, 39, 7] + +- 2026-09-08 13:52:55 UTC — qwen l2: bootstrap 9/12 C=6 sizes=[1707, 1436, 1113, 444, 121, 43] + +- 2026-09-08 13:52:56 UTC — qwen l2: bootstrap 10/12 C=6 sizes=[2176, 1912, 587, 142, 39, 8] + +- 2026-09-08 13:52:56 UTC — qwen l2: bootstrap 11/12 C=6 sizes=[1752, 1437, 1171, 361, 101, 42] + +- 2026-09-08 13:52:57 UTC — qwen l2: bootstrap 12/12 C=6 sizes=[2741, 1825, 205, 40, 38, 15] + +- 2026-09-08 13:52:57 UTC — qwen l2: reference C=6 (eigengap), 2 core modules, rest 4822/4864 + +- 2026-09-08 13:52:57 UTC — qwen l2: 2 core modules, rest 4822, 12.8s + +- 2026-09-08 13:53:13 UTC — qwen fisher: bootstrap 1/12 C=2 sizes=[4819, 45] + +- 2026-09-08 13:53:13 UTC — qwen fisher: bootstrap 2/12 C=2 sizes=[4834, 30] + +- 2026-09-08 13:53:14 UTC — qwen fisher: bootstrap 3/12 C=2 sizes=[4834, 30] + +- 2026-09-08 13:53:14 UTC — qwen fisher: bootstrap 4/12 C=2 sizes=[4783, 81] + +- 2026-09-08 13:53:15 UTC — qwen fisher: bootstrap 5/12 C=2 sizes=[4779, 85] + +- 2026-09-08 13:53:15 UTC — qwen fisher: bootstrap 6/12 C=2 sizes=[4837, 27] + +- 2026-09-08 13:53:16 UTC — qwen fisher: bootstrap 7/12 C=2 sizes=[4795, 69] + +- 2026-09-08 13:53:16 UTC — qwen fisher: bootstrap 8/12 C=2 sizes=[4390, 474] + +- 2026-09-08 13:53:17 UTC — qwen fisher: bootstrap 9/12 C=2 sizes=[3670, 1194] + +- 2026-09-08 13:53:18 UTC — qwen fisher: bootstrap 10/12 C=2 sizes=[4839, 25] + +- 2026-09-08 13:53:18 UTC — qwen fisher: bootstrap 11/12 C=2 sizes=[4827, 37] + +- 2026-09-08 13:53:19 UTC — qwen fisher: bootstrap 12/12 C=2 sizes=[4829, 35] + +- 2026-09-08 13:53:21 UTC — qwen fisher: reference C=2 (eigengap), 0 core modules, rest 4864/4864 + +- 2026-09-08 13:53:21 UTC — qwen fisher: 0 core modules, rest 4864, 23.5s + +- 2026-09-08 13:53:21 UTC — qwen: partition complete + +- 2026-09-08 13:53:24 UTC — qwen: module fit on held-out data, metrics=['l2', 'fisher'] + +- 2026-09-08 13:53:28 UTC — qwen l2: exactness 5.01e-06 (relative 1.19e-06), test leakage 0.0009 vs null median 0.0006 + +- 2026-09-08 13:53:29 UTC — qwen l2 m0: |S|=40 share=0.008 effrank=29.9 K=1 s*=1 + +- 2026-09-08 13:53:29 UTC — qwen l2 m1: |S|=2 share=0.003 effrank=1.8 K=2 s*=2 + +- 2026-09-08 13:53:36 UTC — qwen l2 rest: |S|=4822 share=0.990 effrank=3204.9 K=0 s*=None + +- 2026-09-08 13:53:55 UTC — qwen fisher: exactness 0.00e+00 (relative 0.00e+00), test leakage 0.0000 vs null median 0.0000 + +- 2026-09-08 13:54:02 UTC — qwen fisher rest: |S|=4864 share=1.000 effrank=1883.3 K=0 s*=None + +- 2026-09-08 13:54:06 UTC — qwen: sae matched-count curve done + +- 2026-09-08 13:54:09 UTC — qwen: transcoder matched-count curve done + +- 2026-09-08 13:54:09 UTC — qwen: module fit complete + +- 2026-09-08 13:54:33 UTC — qwen: causal module effects on 128 test sequences, doses [0.25, 0.5, 1.0] + +- 2026-09-08 13:54:49 UTC — qwen l2 m0: dose1 loss change +0.0111 (+0.190 of full) + +- 2026-09-08 13:54:57 UTC — qwen l2 m1: dose1 loss change +0.0009 (+0.015 of full) + +- 2026-09-08 13:55:02 UTC — qwen l2 rest: dose1 loss change +0.0469 (+0.799 of full) + +- 2026-09-08 13:55:20 UTC — qwen fisher rest: dose1 loss change +0.0587 (+1.000 of full) + +- 2026-09-08 13:56:18 UTC — qwen: causal stage complete (50 patched interventions) + +- 2026-09-08 13:56:27 UTC — qwen: 2 semantic records prepared + +- 2026-09-08 13:56:31 UTC — qwen: reparameterization stress test, metrics=['l2', 'fisher'], B=12, split 32 atoms + +- 2026-09-08 13:56:43 UTC — qwen l2: identity replay ARI 1.0000, exact recovery 1.000 + +- 2026-09-08 13:56:55 UTC — qwen l2: scaling ARI 1.0000, exact recovery 1.000, Gram change 0.00e+00 + +- 2026-09-08 13:57:05 UTC — qwen l2: splitting [free_C] ARI 0.0013 (core atoms -0.0473), C 6->28, exact recovery 0.000, copies together 0.8750 + +- 2026-09-08 13:57:15 UTC — qwen l2: splitting [matched_C] ARI -0.0053 (core atoms 0.0000), C 6->6, exact recovery 0.000, copies together 0.1875 + +- 2026-09-08 13:57:25 UTC — qwen l2: splitting [matched_C_collinear_collapsed] ARI 1.0000 (core atoms 1.0000), C 6->6, exact recovery 1.000, copies together 0.0000 + +- 2026-09-08 13:57:27 UTC — qwen l2: naive covariance baseline ARI under scaling 0.3792 + +- 2026-09-08 13:57:48 UTC — qwen fisher: identity replay ARI 1.0000, exact recovery 0.000 + +- 2026-09-08 13:58:08 UTC — qwen fisher: scaling ARI 1.0000, exact recovery 0.000, Gram change 0.00e+00 + +- 2026-09-08 13:58:17 UTC — qwen fisher: splitting [free_C] ARI 1.0000 (core atoms n/a), C 2->2, exact recovery 0.000, copies together 0.0000 + +- 2026-09-08 13:58:26 UTC — qwen fisher: splitting [matched_C] ARI 1.0000 (core atoms n/a), C 2->2, exact recovery 0.000, copies together 0.0000 + +- 2026-09-08 13:58:37 UTC — qwen fisher: splitting [matched_C_collinear_collapsed] ARI 1.0000 (core atoms n/a), C 2->2, exact recovery 0.000, copies together 0.0000 + +- 2026-09-08 13:58:38 UTC — qwen fisher: naive covariance baseline ARI under scaling 0.3792 + +- 2026-09-08 13:58:38 UTC — qwen: reparameterization stress test complete + +- 2026-09-08 14:06:55 UTC — qwen: partition sensitivity sweep, metrics=['l2', 'fisher'] + +- 2026-09-08 14:07:12 UTC — qwen l2: affinity=square knn=0 C=6 -> best core energy share 1.0007 (3 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.83 over 2 modules, 0% below 0.5 + +- 2026-09-08 14:07:25 UTC — qwen l2: affinity=square knn=10 C=12 -> best core energy share 0.9958 (4 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.89 over 13 modules, 0% below 0.5 + +- 2026-09-08 14:07:38 UTC — qwen l2: affinity=square knn=30 C=12 -> best core energy share 0.9954 (5 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.88 over 14 modules, 0% below 0.5 + +- 2026-09-08 14:07:54 UTC — qwen l2: affinity=abs knn=0 C=2 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0) + +- 2026-09-08 14:08:07 UTC — qwen l2: affinity=abs knn=10 C=3 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0) + +- 2026-09-08 14:08:23 UTC — qwen l2: affinity=abs knn=30 C=2 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0) + +- 2026-09-08 14:08:36 UTC — qwen l2: affinity=square knn=0 C=16 -> best core energy share 0.9966 (3 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.94 over 20 modules, 0% below 0.5 + +- 2026-09-08 14:08:51 UTC — qwen l2: affinity=square knn=0 C=64 -> best core energy share 0.9707 (23 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.95 over 153 modules, 1% below 0.5 + +- 2026-09-08 14:08:51 UTC — qwen l2: sweep complete in 116.3s + +- 2026-09-08 14:09:31 UTC — qwen fisher: affinity=square knn=0 C=2 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0) + +- 2026-09-08 14:09:59 UTC — qwen fisher: affinity=square knn=10 C=2 -> best core energy share 1.0159 (32 modules at threshold 1.0, cap 1.0) + +- 2026-09-08 14:10:22 UTC — qwen fisher: affinity=square knn=30 C=2 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0) + +- 2026-09-08 14:10:38 UTC — qwen fisher: affinity=abs knn=0 C=2 -> best core energy share 1.0144 (16 modules at threshold 0.9, cap 1.0) + +- 2026-09-08 14:10:54 UTC — qwen fisher: affinity=abs knn=10 C=2 -> best core energy share 1.0293 (78 modules at threshold 1.0, cap 0.5) + +- 2026-09-08 14:11:05 UTC — qwen fisher: affinity=abs knn=30 C=6 -> best core energy share 0.9925 (2 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.98 over 23 modules, 4% below 0.5 + +- 2026-09-08 14:11:17 UTC — qwen fisher: affinity=square knn=0 C=16 -> best core energy share 0.9528 (18 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.93 over 84 modules, 2% below 0.5 + +- 2026-09-08 14:11:30 UTC — qwen fisher: affinity=square knn=0 C=64 -> best core energy share 0.6211 (113 modules at threshold 0.6, cap 0.5); at threshold 0.8 median effective-rank ratio 0.87 over 92 modules, 8% below 0.5 + +- 2026-09-08 14:11:30 UTC — qwen fisher: sweep complete in 159.4s + +- 2026-09-08 14:11:30 UTC — qwen: partition sensitivity sweep complete diff --git a/results/modules/qwen/REPORT.md b/results/modules/qwen/REPORT.md new file mode 100644 index 0000000000000000000000000000000000000000..437e9d63c3eed97dd73d3b85407328a32bfbde07 --- /dev/null +++ b/results/modules/qwen/REPORT.md @@ -0,0 +1,130 @@ +# Module partition report: qwen + +Modules are exact neuron partitions (f = Σ_c f_{S_c}); clustering used fitting data only, every number below is held-out unless labelled. Rules: core modules = components of co-assignment Π ≥ 0.8 over paired discovery bootstraps; K_c = longest bootstrap-stable prefix (min cos² ≥ 0.8); s*_c = validation-bootstrap certified group-OMP width (empirical rule, not a formal certificate). + +## Metric: l2 + +- Bootstraps B=12, affinity `square` (knn=0), normalization `fcm`; per-bootstrap C: [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6]; full-fit reference C=6. +- 2 core modules (sizes [40, 2]), rest module 4822 of 4864 neurons; 4 neurons have no partner with Π ≥ 0.8. +- Exactness max|f − Σ f_S| = 5.01e-06 (relative to max|f|: 1.19e-06; float32 accumulation). Leakage Σ_{c≠c'}|Γ|/‖f‖² = 0.0009 (test) vs random-partition null median 0.0006, q05 0.0001; 75.5% of 200 null draws at or below observed. + +### Module inventory + +| module | \|S_c\| | Γ_cc/‖f‖² (test) | Π within | eff. rank | K_c | top-K_c coverage (test) | s*_c | s*/\|S\| | semantics (val/test AUC) | dose-1 loss effect (frac. of full) | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | --- | ---: | +| m0 | 40 | 0.008 | 0.91 | 29.9 | 1 | 0.193 | 1 | 0.03 | 0.44/0.25 | +0.0111 (+0.190) | +| m1 | 2 | 0.003 | 1.00 | 1.8 | 2 | 1.000 | 2 | 1.00 | 0.76/0.62 | +0.0009 (+0.015) | +| rest | 4822 | 0.990 | — | 3204.9 | 0 | — | — | — | — | +0.0469 (+0.799) | + + +## Metric: fisher + +- Bootstraps B=12, affinity `square` (knn=0), normalization `fcm`; per-bootstrap C: [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]; full-fit reference C=2. +- 0 core modules (sizes []), rest module 4864 of 4864 neurons; 6 neurons have no partner with Π ≥ 0.8. +- Exactness max|f − Σ f_S| = 0.00e+00 (relative to max|f|: 0.00e+00; float32 accumulation). Leakage Σ_{c≠c'}|Γ|/‖f‖² = 0.0000 (test) vs random-partition null median 0.0000, q05 0.0000; 100.0% of 200 null draws at or below observed. + +### Module inventory + +| module | \|S_c\| | Γ_cc/‖f‖² (test) | Π within | eff. rank | K_c | top-K_c coverage (test) | s*_c | s*/\|S\| | semantics (val/test AUC) | dose-1 loss effect (frac. of full) | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | --- | ---: | +| rest | 4864 | 1.000 | — | 1883.3 | 0 | — | — | — | — | +0.0587 (+1.000) | + +_semantic scoring not run (records: 0 prepared)_ + +## Matched-count reconstruction (test relative squared error of the centered write) + +Residual-L2 error for every column; FCM block rows come from the frozen comparison reference. Modules include the rest module as the last object (error reaches 0 exactly). + +**Test error vs number of kept objects C** + +| C | modules_l2 | modules_fisher | fcm_l2_blocks | fcm_fisher_blocks | sae | transcoder | +| ---: | ---: | ---: | ---: | ---: | ---: | ---: | +| 1 | 0.993 | 0.000 | 0.999 | 0.988 | 0.999 | 0.989 | +| 2 | 0.990 | | 0.997 | 0.965 | 0.989 | 0.988 | +| 3 | 0.000 | | | | 0.989 | 0.983 | +| 4 | | | 0.997 | 0.951 | 0.982 | 0.978 | +| 8 | | | 0.984 | 0.952 | 0.967 | 0.973 | +| 16 | | | 0.977 | 0.925 | 0.953 | 0.957 | +| 32 | | | 0.939 | 0.887 | 0.925 | 0.931 | +| 64 | | | 0.892 | 0.842 | 0.881 | 0.886 | +| 128 | | | | | 0.809 | 0.824 | +| 1792 | | | | | 0.438 | 0.501 | + +**Reproducible energy share among kept objects (stability ≥ 0.8)** + +| C | modules_l2 | modules_fisher | sae | transcoder | +| ---: | ---: | ---: | ---: | ---: | +| 1 | 0.008 | 0.000 | 0.000 | 0.000 | +| 2 | 0.011 | | 0.000 | 0.000 | +| 3 | 0.011 | | 0.000 | 0.000 | +| 4 | | | 0.000 | 0.000 | +| 8 | | | 0.005 | 0.000 | +| 16 | | | 0.013 | 0.000 | +| 32 | | | 0.018 | 0.000 | +| 64 | | | 0.027 | 0.000 | +| 128 | | | 0.030 | 0.000 | +| 1792 | | | — | — | + +## Function-preserving reparameterization (plan section 4, Table 4) + +Two transformations that leave the layer function exactly unchanged. Agreement is the Adjusted Rand Index of the recovered neuron partition against the original; the rest module counts as one label. The identity replay is the achievable ceiling for this pipeline. + +### l2 + +| transformation | ARI (all neurons) | ARI (core-module neurons) | exact core-module recovery | modules C → C | +| --- | ---: | ---: | ---: | ---: | +| identity replay (control) | 1.0000 | 1.0000 | 1.000 | 2 → 2 | +| positive scaling a_j, 1/a_j | 1.0000 | 1.0000 | 1.000 | 2 → 2 | +| positive half split 32 atoms — free C | 0.0013 | -0.0473 | 0.000 | 2 → 36 | +| positive half split 32 atoms — matched C | -0.0053 | 0.0000 | 0.000 | 2 → 7 | +| positive half split 32 atoms — matched C collinear collapsed | 1.0000 | 1.0000 | 1.000 | 2 → 2 | +| baseline: raw-covariance clustering, same scaling | 0.3792 | — | — | 3 → 2 | + +Affinity change on atoms that were not split: fcm 0.0e+00, cosine 0.0e+00. The clustering input on those atoms is therefore preserved to floating-point precision; the partition still moves because splitting turns an atom's self-coupling into an off-diagonal edge, so each pair of copies forms a near-disconnected 2-clique that adds a near-zero normalized-Laplacian eigenvalue. The raw-covariance baseline row is metric-independent by construction. +Collapsing exactly-collinear atoms first (discoverable from the Gram alone, and a no-op on the original layer: 4864 groups for 4864 neurons) merges 4896 atoms back to 4864 with a Gram differing from the original by 0.0 and restores the partition. + +### fisher + +| transformation | ARI (all neurons) | ARI (core-module neurons) | exact core-module recovery | modules C → C | +| --- | ---: | ---: | ---: | ---: | +| identity replay (control) | 1.0000 | — | 0.000 | 0 → 0 | +| positive scaling a_j, 1/a_j | 1.0000 | — | 0.000 | 0 → 0 | +| positive half split 32 atoms — free C | 1.0000 | — | 0.000 | 0 → 0 | +| positive half split 32 atoms — matched C | 1.0000 | — | 0.000 | 0 → 0 | +| positive half split 32 atoms — matched C collinear collapsed | 1.0000 | — | 0.000 | 0 → 0 | +| baseline: raw-covariance clustering, same scaling | 0.3792 | — | — | 3 → 2 | + +Affinity change on atoms that were not split: fcm 2.0e-07, cosine 0.0e+00. The clustering input on those atoms is therefore preserved to floating-point precision; the partition still moves because splitting turns an atom's self-coupling into an off-diagonal edge, so each pair of copies forms a near-disconnected 2-clique that adds a near-zero normalized-Laplacian eigenvalue. The raw-covariance baseline row is metric-independent by construction. +Collapsing exactly-collinear atoms first (discoverable from the Gram alone, and a no-op on the original layer: 4864 groups for 4864 neurons) merges 4896 atoms back to 4864 with a Gram differing from the original by 0.0 and restores the partition. + +## Matched-count loss recovered (test, dose 1) + +Full-ablation loss increase 0.0587 nats over 128 sequences; loss recovered = 1 − patched increase / full increase. + +**Loss recovered vs C** + +| C | modules_l2 | modules_fisher | fcm_l2_blocks | fcm_fisher_blocks | sae | transcoder | pca | neuron_mass | +| ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | +| 1 | 0.188 | 1.000 | 0.017 | -0.005 | 0.025 | 0.020 | 0.081 | 0.121 | +| 2 | 0.201 | | 0.005 | 0.056 | 0.049 | 0.018 | 0.121 | 0.103 | +| 3 | 1.000 | | | | | | | | +| 4 | | | -0.010 | 0.183 | 0.067 | 0.005 | 0.014 | 0.121 | +| 8 | | | 0.132 | 0.170 | 0.103 | 0.023 | -0.147 | 0.028 | +| 16 | | | 0.092 | 0.197 | 0.105 | 0.008 | -0.003 | -0.007 | +| 32 | | | 0.132 | 0.130 | 0.013 | 0.092 | -0.077 | 0.043 | +| 64 | | | 0.110 | 0.013 | -0.026 | 0.104 | -0.197 | 0.121 | +| 128 | | | | | -0.016 | 0.066 | | | +| 1792 | | | | | 0.176 | 0.413 | | | + +## Failure archetypes (l2, plan section 13) + +- **A, stable and sparse** (2 modules): a bootstrap-stable module with a certified native width at most half its size. m0 (|S|=40, K=1, s*=1), m1 (|S|=2, K=2, s*=2) +- **B, stable but distributed** (0 modules): closed directions exist but no compact native width. none +- **C, no statistically closed direction** (1 groups, including the rest module): the pipeline declines to interpret these rather than reporting a sparse approximation of an unidentified parent. + +## Failure archetypes (fisher, plan section 13) + +- **A, stable and sparse** (0 modules): a bootstrap-stable module with a certified native width at most half its size. none +- **B, stable but distributed** (0 modules): closed directions exist but no compact native width. none +- **C, no statistically closed direction** (1 groups, including the rest module): the pipeline declines to interpret these rather than reporting a sparse approximation of an unidentified parent. + diff --git a/results/modules/qwen/RESULTS.md b/results/modules/qwen/RESULTS.md new file mode 100644 index 0000000000000000000000000000000000000000..3ecc3e146bbfe26e3323a1598e8570b26067c8f5 --- /dev/null +++ b/results/modules/qwen/RESULTS.md @@ -0,0 +1,48 @@ + +### qwen: partition (l2) + +2 core modules (sizes [40, 2]), rest module 4822 neurons, 4 neurons with no co-assignment partner >= 0.8. Per-bootstrap C: [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6]. Saved `results/modules/qwen/partition/partition_l2.json`. + +### qwen: partition (fisher) + +0 core modules (sizes []), rest module 4864 neurons, 6 neurons with no co-assignment partner >= 0.8. Per-bootstrap C: [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]. Saved `results/modules/qwen/partition/partition_fisher.json`. + +### qwen: module geometry (l2) + +Exactness max|f - sum f_S| = 5.01e-06. Test leakage 0.0009 (random-partition null median 0.0006, 75.5% of null draws at or below). Modules: m0 |S|=40 share=0.008 K=1 s*=1; m1 |S|=2 share=0.003 K=2 s*=2; rest |S|=4822 share=0.990 K=0 s*=None. Saved `results/modules/qwen/fit/modules_l2.json`. + +### qwen: module geometry (fisher) + +Exactness max|f - sum f_S| = 0.00e+00. Test leakage 0.0000 (random-partition null median 0.0000, 100.0% of null draws at or below). Modules: rest |S|=4864 share=1.000 K=0 s*=None. Saved `results/modules/qwen/fit/modules_fisher.json`. + +### qwen: module causal effects (l2) + +Full-ablation loss increase 0.0587 nats on 128 sequences. Dose-1 fraction of full effect per module: m0 +0.190, m1 +0.015, rest +0.799. Saved `results/modules/qwen/causal/module_effects_l2.json`. + +### qwen: module causal effects (fisher) + +Full-ablation loss increase 0.0587 nats on 128 sequences. Dose-1 fraction of full effect per module: rest +1.000. Saved `results/modules/qwen/causal/module_effects_fisher.json`. + +### qwen: matched-count loss recovered + +50 patched interventions; loss recovered = 1 - (patched loss increase)/(full-ablation loss increase). Saved `results/modules/qwen/causal/matched_count.json`. + +### qwen: module semantic records + +2 records written to `results/modules/qwen/semantics/records` (module_l2_m0, module_l2_m1). Hypotheses are generated/selected/frozen by the existing scorer; nothing here touches the LLM. + +### qwen: module reparameterization (l2) + +Adjusted Rand Index of the recovered neuron partition against the original: identity replay 1.0000, positive scaling 1.0000, positive splitting -0.0053 at matched cluster count, 1.0000 after collapsing exactly-collinear atoms first and 0.0013 when the eigengap rule reselects the cluster count, naive raw-covariance baseline under the same scaling 0.3792. Exact core-module set recovery under scaling 1.000, under splitting at matched cluster count 0.000; 0.1875 of split copies are co-assigned with their twin. Affinity change on unsplit atoms under splitting: {'fcm': 0.0, 'cosine': 0.0}. Saved `results/modules/qwen/reparameterization/reparameterization.json`. + +### qwen: module reparameterization (fisher) + +Adjusted Rand Index of the recovered neuron partition against the original: identity replay 1.0000, positive scaling 1.0000, positive splitting 1.0000 at matched cluster count, 1.0000 after collapsing exactly-collinear atoms first and 1.0000 when the eigengap rule reselects the cluster count, naive raw-covariance baseline under the same scaling 0.3792. Exact core-module set recovery under scaling 0.000, under splitting at matched cluster count 0.000; 0.0000 of split copies are co-assigned with their twin. Affinity change on unsplit atoms under splitting: {'fcm': 2.022281080371613e-07, 'cosine': 0.0}. Saved `results/modules/qwen/reparameterization/reparameterization.json`. + +### qwen: partition sensitivity (l2) + +Swept affinity ['square', 'abs'], k-NN [0, 10, 30], fixed cluster counts [16, 64], co-assignment thresholds [0.6, 0.7, 0.8, 0.9, 1.0] and bulk caps [0.5, 1.0] (80 configurations). Best held-out core-module energy share: 1.0007 (3 modules, affinity square, knn 0, C=6, threshold 0.6, cap 1.0); 1.0005 (3 modules, affinity square, knn 0, C=6, threshold 0.7, cap 1.0); 1.0000 (1 modules, affinity abs, knn 0, C=2, threshold 0.6, cap 1.0). Saved `results/modules/qwen/sweep/sweep.json`. + +### qwen: partition sensitivity (fisher) + +Swept affinity ['square', 'abs'], k-NN [0, 10, 30], fixed cluster counts [16, 64], co-assignment thresholds [0.6, 0.7, 0.8, 0.9, 1.0] and bulk caps [0.5, 1.0] (80 configurations). Best held-out core-module energy share: 1.0293 (78 modules, affinity abs, knn 10, C=2, threshold 1.0, cap 0.5); 1.0293 (78 modules, affinity abs, knn 10, C=2, threshold 1.0, cap 1.0); 1.0159 (32 modules, affinity square, knn 10, C=2, threshold 1.0, cap 1.0). Saved `results/modules/qwen/sweep/sweep.json`. diff --git a/results/modules/qwen/causal/matched_count.json b/results/modules/qwen/causal/matched_count.json new file mode 100644 index 0000000000000000000000000000000000000000..041e219fe3289900a00fc7a798094e7d556406a7 --- /dev/null +++ b/results/modules/qwen/causal/matched_count.json @@ -0,0 +1,440 @@ +{ + "base": { + "sequences": 128, + "mean_clean_loss": 3.539125442504883, + "mean_full_ablation_loss": 3.5977871417999268, + "full_ablation_loss_increase": 0.058661699295043945, + "intervention": "subtract dose * write at the penultimate position; predict the final token (same as evaluate.py)" + }, + "rows": [ + { + "method": "modules_l2", + "C": 1, + "object": "m0", + "loss_recovered": 0.18755410595622757, + "mean_loss_change": 0.047659456729888916, + "kl_clean_patched": 0.05378227308392525, + "neurons_used": 40, + "includes_rest": false + }, + { + "method": "modules_l2", + "C": 2, + "object": "m1", + "loss_recovered": 0.20133816984697916, 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Litter size is one or two ; the newborns measure 12 \u2013 14 cm (", + "token_texts": [ + " the", + " left", + " .", + " L", + "itter", + " size", + " is", + " one", + " or", + " two", + " ;", + " the", + " newborn", + "s", + " measure", + " ", + "1", + "2", + " \u2013", + " ", + "1", + "4", + " cm", + " (" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.09575362503528595 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.09575362503528595, + "quantile": 0.260986328125, + "active": false, + "media": {}, + "metadata": { + "sequence": 122, + "split": "train" + } + }, + { + "example_id": "train_122_47", + "text": "12 \u2013 14 cm ( 4 @.@ 7 \u2013 5 @.@ 5 in ) across", + "token_texts": [ + "1", + "2", + " \u2013", + " ", + "1", + "4", + " cm", + " (", + " ", + "4", + " @", + ".@", + " ", + "7", + " \u2013", + " ", + "5", + " @", + ".@", + " ", + "5", + " in", + " )", + " across" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.1757413148880005 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.1757413148880005, + "quantile": 0.626708984375, + "active": false, + "media": {}, + "metadata": { + "sequence": 122, + "split": "train" + } + }, + { + "example_id": "train_122_62", + "text": " 5 @.@ 5 in ) across . Males and females reach sexual maturity at disc widths of 20", + "token_texts": [ + " ", + "5", + " @", + ".@", + " ", + "5", + " in", + " )", + " across", + " .", + " M", + "ales", + " and", + " females", + " reach", + " sexual", + " maturity", + " at", + " disc", + " widths", + " of", + " ", + "2", + "0" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.1844259798526764 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.1844259798526764, + "quantile": 0.656494140625, + "active": false, + "media": {}, + "metadata": { + "sequence": 122, + "split": "train" + } + }, + { + "example_id": "train_123_15", + "text": " 21 cm ( 7 @.@ 9 \u2013 8 @.@", + "token_texts": [ + " ", + "2", + "1", + " cm", + " (", + " ", + "7", + " @", + ".@", + " ", + "9", + " \u2013", + " ", + "8", + " @", + ".@" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.41731855273246765 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.41731855273246765, + "quantile": 0.909912109375, + "active": false, + "media": {}, + "metadata": { + "sequence": 123, + "split": "train" + } + }, + { + "example_id": "train_123_31", + "text": ".@ 9 \u2013 8 @.@ 3 in ) and 18 \u2013 19 cm ( 7", + "token_texts": [ + ".@", + " ", + "9", + " \u2013", + " ", + "8", + " @", + ".@", + " ", + "3", + " in", + " )", + " and", + " ", + "1", + "8", + " \u2013", + " ", + "1", + "9", + " cm", + " (", + " ", + "7" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.26945459842681885 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.26945459842681885, + "quantile": 0.818115234375, + "active": false, + "media": {}, + "metadata": { + "sequence": 123, + "split": "train" + } + }, + { + "example_id": "train_123_47", + "text": " \u2013 19 cm ( 7 @.@ 1 \u2013 7 @.@ 5 in ) respectively . The", + "token_texts": [ + " \u2013", + " ", + "1", + "9", + " cm", + " (", + " ", + "7", + " @", + ".@", + " ", + "1", + " \u2013", + " ", + "7", + " @", + ".@", + " ", + "5", + " in", + " )", + " respectively", + " .", + " The" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.2970041036605835 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.2970041036605835, + "quantile": 0.847900390625, + "active": false, + "media": {}, + "metadata": { + "sequence": 123, + "split": "train" + } + }, + { + "example_id": "train_123_62", + "text": " @.@ 5 in ) respectively . 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Penalty minutes", + "token_texts": [ + " =", + " \n", + "<|endoftext|>", + " In", + " ice", + " hockey", + " ,", + " a", + " combination", + " of", + " a", + " player", + " '", + "s", + " goals", + " and", + " assists", + " are", + " collectively", + " called", + " points", + " .", + " Penalty", + " minutes" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.09654548764228821 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.09654548764228821, + "quantile": 0.266357421875, + "active": false, + "media": {}, + "metadata": { + "sequence": 187, + "split": "train" + } + }, + { + "example_id": "train_187_47", + "text": " assists are collectively called points . Penalty minutes are the total number of minutes assigned to a player for infractions assessed during the", + "token_texts": [ + " assists", + " are", + " collectively", + " called", + " points", + " .", + " Penalty", + " minutes", + " are", + " the", + " total", + " number", + " of", + " minutes", + " assigned", + " to", + " a", + " player", + " for", + " infra", + "ctions", + " assessed", + " during", + " the" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.5000468492507935 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.5000468492507935, + "quantile": 0.931396484375, + "active": true, + "media": {}, + "metadata": { + "sequence": 187, + "split": "train" + } + }, + { + "example_id": "train_187_62", + "text": " to a player for infractions assessed during the season.Plus @-@ minus is a statistic that tracks when a player", + "token_texts": [ + " to", + " a", + " player", + " for", + " infra", + "ctions", + " assessed", + " during", + " the", + " season", + ".Pl", + "us", + " @", + "-", + "@", + " minus", + " is", + " a", + " statistic", + " that", + " tracks", + " when", + " a", + " player" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.14297011494636536 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.14297011494636536, + "quantile": 0.493896484375, + "active": false, + "media": {}, + "metadata": { + "sequence": 187, + "split": "train" + } + }, + { + "example_id": "train_188_15", + "text": " on the ice while goals were scored , both for and against their team , though", + "token_texts": [ + " on", + " the", + " ice", + " while", + " goals", + " were", + " scored", + " ,", + " both", + " for", + " and", + " against", + " their", + " team", + " ,", + " though" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.11123692989349365 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.11123692989349365, + "quantile": 0.330810546875, + "active": false, + "media": {}, + "metadata": { + "sequence": 188, + "split": "train" + } + }, + { + "example_id": "train_188_31", + "text": " both for and against their team , though some in game situations will not effect the statistic . Below is a listing of all", + "token_texts": [ + " both", + " for", + " and", + " against", + " their", + " team", + " ,", + " though", + " some", + " in", + " game", + " situations", + " will", + " not", + " effect", + " the", + " statistic", + " .", + " Below", + " is", + " a", + " listing", + " of", + " all" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.07569791376590729 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.07569791376590729, + "quantile": 0.149169921875, + "active": false, + "media": {}, + "metadata": { + "sequence": 188, + "split": "train" + } + }, + { + "example_id": "train_188_47", + "text": " statistic . Below is a listing of all player statistics for the Blue Jackets during the season . \n<|endoftext|> = = = Sk", + "token_texts": [ + " statistic", + " .", + " Below", + " is", + " a", + " listing", + " of", + " all", + " player", + " statistics", + " for", + " the", + " Blue", + " Jackets", + " during", + " the", + " season", + " .", + " \n", + "<|endoftext|>", + " =", + " =", + " =", + " Sk" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.1554751992225647 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.1554751992225647, + "quantile": 0.547607421875, + "active": false, + "media": {}, + "metadata": { + "sequence": 188, + "split": "train" + } + }, + { + "example_id": "train_188_62", + "text": " the season . \n<|endoftext|> = = = Skaters = = = \n<|endoftext|> Note : Pos \n<|endoftext|> = Position ; GP", + "token_texts": [ + " the", + " season", + " .", + " \n", + "<|endoftext|>", + " =", + " =", + " =", + " Sk", + "aters", + " =", + " =", + " =", + " \n", + "<|endoftext|>", + " Note", + " :", + " Pos", + " \n", + "<|endoftext|>", + " =", + " Position", + " ;", + " GP" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.08530010282993317 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.08530010282993317, + "quantile": 0.202880859375, + "active": false, + "media": {}, + "metadata": { + "sequence": 188, + "split": "train" + } + }, + { + "example_id": "train_189_15", + "text": " \n<|endoftext|> Games played in ; G \n<|endoftext|> = Goals ; A = \n<|endoftext|>", + "token_texts": [ + " \n", + "<|endoftext|>", + " Games", + " played", + " in", + " ;", + " G", + " \n", + "<|endoftext|>", + " =", + " Goals", + " ;", + " A", + " =", + " \n", + "<|endoftext|>" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 1.4098823070526123 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 1.4098823070526123, + "quantile": 0.991455078125, + "active": true, + "media": {}, + "metadata": { + "sequence": 189, + "split": "train" + } + }, + { + "example_id": "train_189_31", + "text": "<|endoftext|> = Goals ; A = \n<|endoftext|> Assists ; Pts \n<|endoftext|> = Points ; PIM = \n<|endoftext|> Penalty", + "token_texts": [ + "<|endoftext|>", + " =", + " Goals", + " ;", + " A", + " =", + " \n", + "<|endoftext|>", + " Ass", + "ists", + " ;", + " P", + "ts", + " \n", + "<|endoftext|>", + " =", + " Points", + " ;", + " P", + "IM", + " =", + " \n", + "<|endoftext|>", + " Penalty" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0942322313785553 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0942322313785553, + "quantile": 0.254638671875, + "active": false, + "media": {}, + "metadata": { + "sequence": 189, + "split": "train" + } + }, + { + "example_id": "train_189_47", + "text": " Points ; 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They had a bore of 39 inches ( 991", + "token_texts": [ + " km", + " /", + " h", + " ;", + " ", + "1", + "5", + " mph", + " )", + " .", + " They", + " had", + " a", + " bore", + " of", + " ", + "3", + "9", + " inches", + " (", + " ", + "9", + "9", + "1" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.17603223025798798 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.17603223025798798, + "quantile": 0.627685546875, + "active": false, + "media": {}, + "metadata": { + "sequence": 268, + "split": "train" + } + }, + { + "example_id": "train_268_47", + "text": "39 inches ( 991 mm ) and a stroke of 30 inches ( 762 mm", + "token_texts": [ + "3", + "9", + " inches", + " (", + " ", + "9", + "9", + "1", + " mm", + " )", + " and", + " a", + " stroke", + " of", + " ", + "3", + "0", + " inches", + " (", + " ", + "7", + "6", + "2", + " mm" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.2850112318992615 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.2850112318992615, + "quantile": 0.839111328125, + "active": false, + "media": {}, + "metadata": { + "sequence": 268, + "split": "train" + } + }, + { + "example_id": "train_268_62", + "text": "30 inches ( 762 mm ) . \n<|endoftext|> The ship briefly operated between Glasgow and other ports in Scotland", + "token_texts": [ + "3", + "0", + " inches", + " (", + " ", + "7", + "6", + "2", + " mm", + " )", + " .", + " \n", + "<|endoftext|>", + " The", + " ship", + " briefly", + " operated", + " between", + " Glasgow", + " and", + " other", + " ports", + " in", + " Scotland" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.13211312890052795 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.13211312890052795, + "quantile": 0.444091796875, + "active": false, + "media": {}, + "metadata": { + "sequence": 268, + "split": "train" + } + }, + { + "example_id": "train_269_15", + "text": " Hutcheson 's West Highland Service before she was purchased in September 1", + "token_texts": [ + " Hut", + "ches", + "on", + " '", + "s", + " West", + " Highland", + " Service", + " before", + " she", + " was", + " purchased", + " in", + " September", + " ", + "1" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.13140161335468292 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.13140161335468292, + "quantile": 0.437255859375, + "active": false, + "media": {}, + "metadata": { + "sequence": 269, + "split": "train" + } + }, + { + "example_id": "train_269_31", + "text": " before she was purchased in September 1861 by James D. 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{ + "sequence": 186, + "split": "train" + } + }, + { + "example_id": "train_187_15", + "text": " . \n<|endoftext|> = = Player statistics = = \n<|endoftext|> In ice hockey , a", + "token_texts": [ + " .", + " \n", + "<|endoftext|>", + " =", + " =", + " Player", + " statistics", + " =", + " =", + " \n", + "<|endoftext|>", + " In", + " ice", + " hockey", + " ,", + " a" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.06539862602949142 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.06539862602949142, + "quantile": 0.903076171875, + "active": true, + "media": {}, + "metadata": { + "sequence": 187, + "split": "train" + } + }, + { + "example_id": "train_187_31", + "text": " = \n<|endoftext|> In ice hockey , a combination of a player 's goals and assists are collectively called points . Penalty minutes", + "token_texts": [ + " =", + " \n", + "<|endoftext|>", + " In", + " ice", + " hockey", + " ,", + " a", + " combination", + " of", + " a", + " player", + " '", + "s", + " goals", + " and", + " assists", + " are", + " collectively", + " called", + " points", + " .", + " Penalty", + " minutes" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0001385500218020752 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0001385500218020752, + "quantile": 0.020751953125, + "active": false, + "media": {}, + "metadata": { + "sequence": 187, + "split": "train" + } + }, + { + "example_id": "train_187_47", + "text": " assists are collectively called points . Penalty minutes are the total number of minutes assigned to a player for infractions assessed during the", + "token_texts": [ + " assists", + " are", + " collectively", + " called", + " points", + " .", + " Penalty", + " minutes", + " are", + " the", + " total", + " number", + " of", + " minutes", + " assigned", + " to", + " a", + " player", + " for", + " infra", + "ctions", + " assessed", + " during", + " the" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.029316237196326256 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.029316237196326256, + "quantile": 0.783935546875, + "active": false, + "media": {}, + "metadata": { + "sequence": 187, + "split": "train" + } + }, + { + "example_id": "train_187_62", + "text": " to a player for infractions assessed during the season.Plus @-@ minus is a statistic that tracks when a player", + "token_texts": [ + " to", + " a", + " player", + " for", + " infra", + "ctions", + " assessed", + " during", + " the", + " season", + ".Pl", + "us", + " @", + "-", + "@", + " minus", + " is", + " a", + " statistic", + " that", + " tracks", + " when", + " a", + " player" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0021754244808107615 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0021754244808107615, + "quantile": 0.266357421875, + "active": false, + "media": {}, + "metadata": { + "sequence": 187, + "split": "train" + } + }, + { + "example_id": "train_188_15", + "text": " on the ice while goals were scored , both for and against their team , though", + "token_texts": [ + " on", + " the", + " ice", + " while", + " goals", + " were", + " scored", + " ,", + " both", + " for", + " and", + " against", + " their", + " team", + " ,", + " though" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.01799280196428299 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.01799280196428299, + "quantile": 0.694580078125, + "active": false, + "media": {}, + "metadata": { + "sequence": 188, + "split": "train" + } + }, + { + "example_id": "train_188_31", + "text": " both for and against their team , though some in game situations will not effect the statistic . Below is a listing of all", + "token_texts": [ + " both", + " for", + " and", + " against", + " their", + " team", + " ,", + " though", + " some", + " in", + " game", + " situations", + " will", + " not", + " effect", + " the", + " statistic", + " .", + " Below", + " is", + " a", + " listing", + " of", + " all" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.06146344542503357 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.06146344542503357, + "quantile": 0.897705078125, + "active": false, + "media": {}, + "metadata": { + "sequence": 188, + "split": "train" + } + }, + { + "example_id": "train_188_47", + "text": " statistic . Below is a listing of all player statistics for the Blue Jackets during the season . \n<|endoftext|> = = = Sk", + "token_texts": [ + " statistic", + " .", + " Below", + " is", + " a", + " listing", + " of", + " all", + " player", + " statistics", + " for", + " the", + " Blue", + " Jackets", + " during", + " the", + " season", + " .", + " \n", + "<|endoftext|>", + " =", + " =", + " =", + " Sk" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0126413032412529 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0126413032412529, + "quantile": 0.621826171875, + "active": false, + "media": {}, + "metadata": { + "sequence": 188, + "split": "train" + } + }, + { + "example_id": "train_188_62", + "text": " the season . \n<|endoftext|> = = = Skaters = = = \n<|endoftext|> Note : Pos \n<|endoftext|> = Position ; GP", + "token_texts": [ + " the", + " season", + " .", + " \n", + "<|endoftext|>", + " =", + " =", + " =", + " Sk", + "aters", + " =", + " =", + " =", + " \n", + "<|endoftext|>", + " Note", + " :", + " Pos", + " \n", + "<|endoftext|>", + " =", + " Position", + " ;", + " GP" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.01426499243825674 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.01426499243825674, + "quantile": 0.644287109375, + "active": false, + "media": {}, + "metadata": { + "sequence": 188, + "split": "train" + } + }, + { + "example_id": "train_189_15", + "text": " \n<|endoftext|> Games played in ; G \n<|endoftext|> = Goals ; A = \n<|endoftext|>", + "token_texts": [ + " \n", + "<|endoftext|>", + " Games", + " played", + " in", + " ;", + " G", + " \n", + "<|endoftext|>", + " =", + " Goals", + " ;", + " A", + " =", + " \n", + "<|endoftext|>" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.008320034481585026 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.008320034481585026, + "quantile": 0.531494140625, + "active": false, + "media": {}, + "metadata": { + "sequence": 189, + "split": "train" + } + }, + { + "example_id": "train_189_31", + "text": "<|endoftext|> = Goals ; A = \n<|endoftext|> Assists ; Pts \n<|endoftext|> = Points ; PIM = \n<|endoftext|> Penalty", + "token_texts": [ + "<|endoftext|>", + " =", + " Goals", + " ;", + " A", + " =", + " \n", + "<|endoftext|>", + " Ass", + "ists", + " ;", + " P", + "ts", + " \n", + "<|endoftext|>", + " =", + " Points", + " ;", + " P", + "IM", + " =", + " \n", + "<|endoftext|>", + " Penalty" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0011258262675255537 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0011258262675255537, + "quantile": 0.169677734375, + "active": false, + "media": {}, + "metadata": { + "sequence": 189, + "split": "train" + } + }, + { + "example_id": "train_189_47", + "text": " Points ; PIM = \n<|endoftext|> Penalty minutes ; + / - = Plus / minus \n<|endoftext|> = = = Goalt", + "token_texts": [ + " Points", + " ;", + " P", + "IM", + " =", + " \n", + "<|endoftext|>", + " Penalty", + " minutes", + " ;", + " +", + " /", + " -", + " =", + " Plus", + " /", + " minus", + " \n", + "<|endoftext|>", + " =", + " =", + " =", + " Go", + "alt" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.014574535191059113 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.014574535191059113, + "quantile": 0.649658203125, + "active": false, + "media": {}, + "metadata": { + "sequence": 189, + "split": "train" + } + }, + { + "example_id": "train_189_62", + "text": " / minus \n<|endoftext|> = = = Goaltenders = = = \n<|endoftext|> Note : GP \n<|endoftext|> = Games Played ;", + "token_texts": [ + " /", + " minus", + " \n", + "<|endoftext|>", + " =", + " =", + " =", + " Go", + "alt", + "enders", + " =", + " =", + " =", + " \n", + "<|endoftext|>", + " Note", + " :", + " GP", + " \n", + "<|endoftext|>", + " =", + " Games", + " Played", + " ;" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.008645007386803627 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.008645007386803627, + "quantile": 0.540283203125, + "active": false, + "media": {}, + "metadata": { + "sequence": 189, + "split": "train" + } + }, + { + "example_id": "train_190_15", + "text": "I = \n<|endoftext|> Time On Ice ( minutes ) ; W \n<|endoftext|> = Wins", + "token_texts": [ + "I", + " =", + " \n", + "<|endoftext|>", + " Time", + " On", + " Ice", + " (", + " minutes", + " )", + " ;", + " W", + " \n", + "<|endoftext|>", + " =", + " Wins" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.002007900271564722 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.002007900271564722, + "quantile": 0.253173828125, + "active": false, + "media": {}, + "metadata": { + "sequence": 190, + "split": "train" + } + }, + { + "example_id": "train_190_31", + "text": " minutes ) ; W \n<|endoftext|> = Wins ; L = \n<|endoftext|> Losses ; OT \n<|endoftext|> = Overtime Losses", + "token_texts": [ + " minutes", + " )", + " ;", + " W", + " \n", + "<|endoftext|>", + " =", + " Wins", + " ;", + " L", + " =", + " \n", + "<|endoftext|>", + " Loss", + "es", + " ;", + " OT", + " \n", + "<|endoftext|>", + " =", + " O", + "vertime", + " Loss", + "es" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.017542369663715363 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.017542369663715363, + "quantile": 0.688720703125, + "active": false, + "media": {}, + "metadata": { + "sequence": 190, + "split": "train" + } + }, + { + "example_id": "train_190_47", + "text": " OT \n<|endoftext|> = Overtime Losses ; 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They had a bore of 39 inches ( 991", + "token_texts": [ + " km", + " /", + " h", + " ;", + " ", + "1", + "5", + " mph", + " )", + " .", + " They", + " had", + " a", + " bore", + " of", + " ", + "3", + "9", + " inches", + " (", + " ", + "9", + "9", + "1" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.03494016081094742 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.03494016081094742, + "quantile": 0.815673828125, + "active": false, + "media": {}, + "metadata": { + "sequence": 268, + "split": "train" + } + }, + { + "example_id": "train_268_47", + "text": "39 inches ( 991 mm ) and a stroke of 30 inches ( 762 mm", + "token_texts": [ + "3", + "9", + " inches", + " (", + " ", + "9", + "9", + "1", + " mm", + " )", + " and", + " a", + " stroke", + " of", + " ", + "3", + "0", + " inches", + " (", + " ", + "7", + "6", + "2", + " mm" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.01613563485443592 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.01613563485443592, + "quantile": 0.669189453125, + "active": false, + "media": {}, + "metadata": { + "sequence": 268, + "split": "train" + } + }, + { + "example_id": "train_268_62", + "text": "30 inches ( 762 mm ) . \n<|endoftext|> The ship briefly operated between Glasgow and other ports in Scotland", + "token_texts": [ + "3", + "0", + " inches", + " (", + " ", + "7", + "6", + "2", + " mm", + " )", + " .", + " \n", + "<|endoftext|>", + " The", + " ship", + " briefly", + " operated", + " between", + " Glasgow", + " and", + " other", + " ports", + " in", + " Scotland" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0009194700396619737 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0009194700396619737, + "quantile": 0.149658203125, + "active": false, + "media": {}, + "metadata": { + "sequence": 268, + "split": "train" + } + }, + { + "example_id": "train_269_15", + "text": " Hutcheson 's West Highland Service before she was purchased in September 1", + "token_texts": [ + " Hut", + "ches", + "on", + " '", + "s", + " West", + " Highland", + " Service", + " before", + " she", + " was", + " purchased", + " in", + " September", + " ", + "1" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.004458180628716946 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.004458180628716946, + "quantile": 0.397216796875, + "active": false, + "media": {}, + "metadata": { + "sequence": 269, + "split": "train" + } + }, + { + "example_id": "train_269_31", + "text": " before she was purchased in September 1861 by James D. 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+ "l2", + "fisher" + ], + "bootstraps": 12, + "affinities": [ + "square", + "abs" + ], + "knns": [ + 0, + 10, + 30 + ], + "extra_clusters": [ + 16, + 64 + ], + "thresholds": [ + 0.6, + 0.7, + 0.8, + 0.9, + 1.0 + ], + "dimension_threshold": 0.8, + "dimension_max_modules": 400, + "dimension_max_size": 256, + "fractions": [ + 0.5, + 1.0 + ], + "normalization": "fcm", + "min_size": 2, + "restarts": 5, + "seed": 17 + }, + "scope": "core-module count and held-out energy share only; no dimension, width, semantic or causal evaluation" +} \ No newline at end of file diff --git a/results/modules/random_gpt2/MONITOR.md b/results/modules/random_gpt2/MONITOR.md new file mode 100644 index 0000000000000000000000000000000000000000..57e2db4cf73f0230f2bc25dd21475b7655c0dd8f --- /dev/null +++ b/results/modules/random_gpt2/MONITOR.md @@ -0,0 +1,166 @@ + +- 2026-09-08 13:58:49 UTC — random_gpt2: partition, metrics=['l2', 'fisher'], B=12, affinity=square, knn=0 + +- 2026-09-08 13:58:52 UTC — random_gpt2 l2: bootstrap 1/12 C=3 sizes=[1524, 775, 773] + +- 2026-09-08 13:58:52 UTC — random_gpt2 l2: bootstrap 2/12 C=3 sizes=[1559, 784, 729] + +- 2026-09-08 13:58:52 UTC — random_gpt2 l2: bootstrap 3/12 C=3 sizes=[1520, 778, 774] + +- 2026-09-08 13:58:52 UTC — random_gpt2 l2: bootstrap 4/12 C=3 sizes=[1521, 778, 773] + +- 2026-09-08 13:58:52 UTC — random_gpt2 l2: bootstrap 5/12 C=3 sizes=[1478, 815, 779] + +- 2026-09-08 13:58:53 UTC — random_gpt2 l2: bootstrap 6/12 C=3 sizes=[1502, 793, 777] + +- 2026-09-08 13:58:53 UTC — random_gpt2 l2: bootstrap 7/12 C=3 sizes=[1495, 803, 774] + +- 2026-09-08 13:58:53 UTC — random_gpt2 l2: bootstrap 8/12 C=3 sizes=[1573, 762, 737] + +- 2026-09-08 13:58:53 UTC — random_gpt2 l2: bootstrap 9/12 C=3 sizes=[1489, 827, 756] + +- 2026-09-08 13:58:54 UTC — random_gpt2 l2: bootstrap 10/12 C=3 sizes=[1462, 832, 778] + +- 2026-09-08 13:58:54 UTC — random_gpt2 l2: bootstrap 11/12 C=3 sizes=[1537, 808, 727] + +- 2026-09-08 13:58:54 UTC — random_gpt2 l2: bootstrap 12/12 C=3 sizes=[1492, 808, 772] + +- 2026-09-08 13:58:54 UTC — random_gpt2 l2: reference C=3 (eigengap), 3 core modules, rest 3066/3072 + +- 2026-09-08 13:58:54 UTC — random_gpt2 l2: 3 core modules, rest 3066, 4.9s + +- 2026-09-08 13:59:04 UTC — random_gpt2 fisher: bootstrap 1/12 C=2 sizes=[2037, 1035] + +- 2026-09-08 13:59:04 UTC — random_gpt2 fisher: bootstrap 2/12 C=2 sizes=[2052, 1020] + +- 2026-09-08 13:59:04 UTC — random_gpt2 fisher: bootstrap 3/12 C=2 sizes=[1922, 1150] + +- 2026-09-08 13:59:04 UTC — random_gpt2 fisher: bootstrap 4/12 C=2 sizes=[2030, 1042] + +- 2026-09-08 13:59:04 UTC — random_gpt2 fisher: bootstrap 5/12 C=2 sizes=[1554, 1518] + +- 2026-09-08 13:59:05 UTC — random_gpt2 fisher: bootstrap 6/12 C=2 sizes=[1616, 1456] + +- 2026-09-08 13:59:05 UTC — random_gpt2 fisher: bootstrap 7/12 C=2 sizes=[1881, 1191] + +- 2026-09-08 13:59:05 UTC — random_gpt2 fisher: bootstrap 8/12 C=2 sizes=[2096, 976] + +- 2026-09-08 13:59:05 UTC — random_gpt2 fisher: bootstrap 9/12 C=2 sizes=[1750, 1322] + +- 2026-09-08 13:59:05 UTC — random_gpt2 fisher: bootstrap 10/12 C=2 sizes=[1537, 1535] + +- 2026-09-08 13:59:06 UTC — random_gpt2 fisher: bootstrap 11/12 C=2 sizes=[2058, 1014] + +- 2026-09-08 13:59:06 UTC — random_gpt2 fisher: bootstrap 12/12 C=2 sizes=[1895, 1177] + +- 2026-09-08 13:59:06 UTC — random_gpt2 fisher: reference C=2 (eigengap), 0 core modules, rest 3072/3072 + +- 2026-09-08 13:59:06 UTC — random_gpt2 fisher: 0 core modules, rest 3072, 11.7s + +- 2026-09-08 13:59:06 UTC — random_gpt2: partition complete + +- 2026-09-08 13:59:09 UTC — random_gpt2: module fit on held-out data, metrics=['l2', 'fisher'] + +- 2026-09-08 13:59:11 UTC — random_gpt2 l2: exactness 1.67e-06 (relative 1.10e-06), test leakage 0.0004 vs null median 0.0004 + +- 2026-09-08 13:59:11 UTC — random_gpt2 l2 m0: |S|=2 share=0.001 effrank=1.7 K=2 s*=2 + +- 2026-09-08 13:59:11 UTC — random_gpt2 l2 m1: |S|=2 share=0.000 effrank=2.0 K=2 s*=2 + +- 2026-09-08 13:59:11 UTC — random_gpt2 l2 m2: |S|=2 share=0.001 effrank=1.5 K=2 s*=2 + +- 2026-09-08 13:59:14 UTC — random_gpt2 l2 rest: |S|=3066 share=0.998 effrank=2514.7 K=0 s*=None + +- 2026-09-08 13:59:24 UTC — random_gpt2 fisher: exactness 0.00e+00 (relative 0.00e+00), test leakage 0.0000 vs null median 0.0000 + +- 2026-09-08 13:59:27 UTC — random_gpt2 fisher rest: |S|=3072 share=1.000 effrank=2396.0 K=0 s*=None + +- 2026-09-08 13:59:30 UTC — random_gpt2: sae matched-count curve done + +- 2026-09-08 13:59:32 UTC — random_gpt2: transcoder matched-count curve done + +- 2026-09-08 13:59:32 UTC — random_gpt2: module fit complete + +- 2026-09-08 13:59:41 UTC — random_gpt2: causal module effects on 128 test sequences, doses [0.25, 0.5, 1.0] + +- 2026-09-08 13:59:45 UTC — random_gpt2 l2 m0: dose1 loss change +0.0003 (-0.272 of full) + +- 2026-09-08 13:59:47 UTC — random_gpt2 l2 m2: dose1 loss change -0.0001 (+0.072 of full) + +- 2026-09-08 13:59:50 UTC — random_gpt2 l2 m1: dose1 loss change +0.0000 (-0.022 of full) + +- 2026-09-08 13:59:51 UTC — random_gpt2 l2 rest: dose1 loss change -0.0012 (+1.218 of full) + +- 2026-09-08 13:59:57 UTC — random_gpt2 fisher rest: dose1 loss change -0.0010 (+1.000 of full) + +- 2026-09-08 14:00:13 UTC — random_gpt2: causal stage complete (51 patched interventions) + +- 2026-09-08 14:00:22 UTC — random_gpt2: 3 semantic records prepared + +- 2026-09-08 14:00:26 UTC — random_gpt2: reparameterization stress test, metrics=['l2', 'fisher'], B=12, split 32 atoms + +- 2026-09-08 14:00:31 UTC — random_gpt2 l2: identity replay ARI 1.0000, exact recovery 1.000 + +- 2026-09-08 14:00:35 UTC — random_gpt2 l2: scaling ARI 1.0000, exact recovery 1.000, Gram change 0.00e+00 + +- 2026-09-08 14:00:41 UTC — random_gpt2 l2: splitting [free_C] ARI -0.0036 (core atoms 0.0000), C 3->31, exact recovery 0.000, copies together 0.9688 + +- 2026-09-08 14:00:44 UTC — random_gpt2 l2: splitting [matched_C] ARI -0.0019 (core atoms 0.0000), C 3->3, exact recovery 0.000, copies together 0.0938 + +- 2026-09-08 14:00:48 UTC — random_gpt2 l2: splitting [matched_C_collinear_collapsed] ARI 1.0000 (core atoms 1.0000), C 3->3, exact recovery 1.000, copies together 0.0000 + +- 2026-09-08 14:00:48 UTC — random_gpt2 l2: naive covariance baseline ARI under scaling 0.8119 + +- 2026-09-08 14:00:57 UTC — random_gpt2 fisher: identity replay ARI 1.0000, exact recovery 0.000 + +- 2026-09-08 14:01:05 UTC — random_gpt2 fisher: scaling ARI 1.0000, exact recovery 0.000, Gram change 0.00e+00 + +- 2026-09-08 14:01:11 UTC — random_gpt2 fisher: splitting [free_C] ARI 0.0000 (core atoms n/a), C 2->30, exact recovery 0.000, copies together 0.9688 + +- 2026-09-08 14:01:14 UTC — random_gpt2 fisher: splitting [matched_C] ARI 0.0000 (core atoms n/a), C 2->2, exact recovery 0.000, copies together 0.0312 + +- 2026-09-08 14:01:17 UTC — random_gpt2 fisher: splitting [matched_C_collinear_collapsed] ARI 1.0000 (core atoms n/a), C 2->2, exact recovery 0.000, copies together 0.0000 + +- 2026-09-08 14:01:17 UTC — random_gpt2 fisher: naive covariance baseline ARI under scaling 0.8119 + +- 2026-09-08 14:01:17 UTC — random_gpt2: reparameterization stress test complete + +- 2026-09-08 14:11:34 UTC — random_gpt2: partition sensitivity sweep, metrics=['l2', 'fisher'] + +- 2026-09-08 14:11:41 UTC — random_gpt2 l2: affinity=square knn=0 C=3 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.83 over 3 modules, 0% below 0.5 + +- 2026-09-08 14:11:45 UTC — random_gpt2 l2: affinity=square knn=10 C=3 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.91 over 7 modules, 0% below 0.5 + +- 2026-09-08 14:11:50 UTC — random_gpt2 l2: affinity=square knn=30 C=3 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.79 over 6 modules, 0% below 0.5 + +- 2026-09-08 14:11:55 UTC — random_gpt2 l2: affinity=abs knn=0 C=3 -> best core energy share 0.9989 (1 modules at threshold 0.6, cap 1.0) + +- 2026-09-08 14:11:59 UTC — random_gpt2 l2: affinity=abs knn=10 C=3 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.96 over 6 modules, 0% below 0.5 + +- 2026-09-08 14:12:03 UTC — random_gpt2 l2: affinity=abs knn=30 C=3 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.95 over 6 modules, 0% below 0.5 + +- 2026-09-08 14:12:09 UTC — random_gpt2 l2: affinity=square knn=0 C=16 -> best core energy share 0.8646 (26 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.94 over 110 modules, 0% below 0.5 + +- 2026-09-08 14:12:15 UTC — random_gpt2 l2: affinity=square knn=0 C=64 -> best core energy share 0.3387 (195 modules at threshold 0.6, cap 0.5); at threshold 0.8 median effective-rank ratio 0.94 over 58 modules, 0% below 0.5 + +- 2026-09-08 14:12:15 UTC — random_gpt2 l2: sweep complete in 41.1s + +- 2026-09-08 14:12:26 UTC — random_gpt2 fisher: affinity=square knn=0 C=2 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0) + +- 2026-09-08 14:12:30 UTC — random_gpt2 fisher: affinity=square knn=10 C=3 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.94 over 25 modules, 0% below 0.5 + +- 2026-09-08 14:12:34 UTC — random_gpt2 fisher: affinity=square knn=30 C=3 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.92 over 11 modules, 0% below 0.5 + +- 2026-09-08 14:12:39 UTC — random_gpt2 fisher: affinity=abs knn=0 C=2 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0) + +- 2026-09-08 14:12:43 UTC — random_gpt2 fisher: affinity=abs knn=10 C=3 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.97 over 29 modules, 0% below 0.5 + +- 2026-09-08 14:12:47 UTC — random_gpt2 fisher: affinity=abs knn=30 C=3 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.95 over 10 modules, 0% below 0.5 + +- 2026-09-08 14:12:53 UTC — random_gpt2 fisher: affinity=square knn=0 C=16 -> best core energy share 0.6003 (82 modules at threshold 0.6, cap 0.5); at threshold 0.8 median effective-rank ratio 0.95 over 67 modules, 0% below 0.5 + +- 2026-09-08 14:13:00 UTC — random_gpt2 fisher: affinity=square knn=0 C=64 -> best core energy share 0.1091 (69 modules at threshold 0.6, cap 0.5); at threshold 0.8 median effective-rank ratio 0.90 over 25 modules, 0% below 0.5 + +- 2026-09-08 14:13:00 UTC — random_gpt2 fisher: sweep complete in 44.1s + +- 2026-09-08 14:13:00 UTC — random_gpt2: partition sensitivity sweep complete diff --git a/results/modules/random_gpt2/REPORT.md b/results/modules/random_gpt2/REPORT.md new file mode 100644 index 0000000000000000000000000000000000000000..f1cf96f30f19eb19b118c1c2599fecdab40a8ed0 --- /dev/null +++ b/results/modules/random_gpt2/REPORT.md @@ -0,0 +1,131 @@ +# Module partition report: random_gpt2 + +Modules are exact neuron partitions (f = Σ_c f_{S_c}); clustering used fitting data only, every number below is held-out unless labelled. Rules: core modules = components of co-assignment Π ≥ 0.8 over paired discovery bootstraps; K_c = longest bootstrap-stable prefix (min cos² ≥ 0.8); s*_c = validation-bootstrap certified group-OMP width (empirical rule, not a formal certificate). + +## Metric: l2 + +- Bootstraps B=12, affinity `square` (knn=0), normalization `fcm`; per-bootstrap C: [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]; full-fit reference C=3. +- 3 core modules (sizes [2, 2, 2]), rest module 3066 of 3072 neurons; 46 neurons have no partner with Π ≥ 0.8. +- Exactness max|f − Σ f_S| = 1.67e-06 (relative to max|f|: 1.10e-06; float32 accumulation). Leakage Σ_{c≠c'}|Γ|/‖f‖² = 0.0004 (test) vs random-partition null median 0.0004, q05 0.0001; 52.0% of 200 null draws at or below observed. + +### Module inventory + +| module | \|S_c\| | Γ_cc/‖f‖² (test) | Π within | eff. rank | K_c | top-K_c coverage (test) | s*_c | s*/\|S\| | semantics (val/test AUC) | dose-1 loss effect (frac. of full) | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | --- | ---: | +| m0 | 2 | 0.001 | 1.00 | 1.7 | 2 | 1.000 | 2 | 1.00 | 0.94/0.62 | +0.0003 (-0.272) | +| m1 | 2 | 0.000 | 0.83 | 2.0 | 2 | 1.000 | 2 | 1.00 | 0.70/0.45 | +0.0000 (-0.022) | +| m2 | 2 | 0.001 | 0.83 | 1.5 | 2 | 1.000 | 2 | 1.00 | 0.58/0.50 | -0.0001 (+0.072) | +| rest | 3066 | 0.998 | — | 2514.7 | 0 | — | — | — | — | -0.0012 (+1.218) | + + +## Metric: fisher + +- Bootstraps B=12, affinity `square` (knn=0), normalization `fcm`; per-bootstrap C: [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]; full-fit reference C=2. +- 0 core modules (sizes []), rest module 3072 of 3072 neurons; 0 neurons have no partner with Π ≥ 0.8. +- Exactness max|f − Σ f_S| = 0.00e+00 (relative to max|f|: 0.00e+00; float32 accumulation). Leakage Σ_{c≠c'}|Γ|/‖f‖² = 0.0000 (test) vs random-partition null median 0.0000, q05 0.0000; 100.0% of 200 null draws at or below observed. + +### Module inventory + +| module | \|S_c\| | Γ_cc/‖f‖² (test) | Π within | eff. rank | K_c | top-K_c coverage (test) | s*_c | s*/\|S\| | semantics (val/test AUC) | dose-1 loss effect (frac. of full) | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | --- | ---: | +| rest | 3072 | 1.000 | — | 2396.0 | 0 | — | — | — | — | -0.0010 (+1.000) | + +_semantic scoring not run (records: 0 prepared)_ + +## Matched-count reconstruction (test relative squared error of the centered write) + +Residual-L2 error for every column; FCM block rows come from the frozen comparison reference. Modules include the rest module as the last object (error reaches 0 exactly). + +**Test error vs number of kept objects C** + +| C | modules_l2 | modules_fisher | fcm_l2_blocks | fcm_fisher_blocks | sae | transcoder | +| ---: | ---: | ---: | ---: | ---: | ---: | ---: | +| 1 | 0.999 | 0.000 | 0.998 | 0.998 | 0.982 | 0.987 | +| 2 | 0.998 | | 0.998 | 0.997 | 0.968 | 0.975 | +| 3 | 0.998 | | | | | | +| 4 | 0.000 | | 0.997 | 0.996 | 0.963 | 0.967 | +| 8 | | | 0.987 | 0.995 | 0.955 | 0.951 | +| 16 | | | 0.976 | 0.984 | 0.939 | 0.926 | +| 32 | | | 0.958 | 0.955 | 0.911 | 0.885 | +| 64 | | | 0.931 | 0.931 | 0.852 | 0.822 | +| 128 | | | | | 0.789 | 0.741 | +| 1536 | | | | | 0.457 | 0.482 | + +**Reproducible energy share among kept objects (stability ≥ 0.8)** + +| C | modules_l2 | modules_fisher | sae | transcoder | +| ---: | ---: | ---: | ---: | ---: | +| 1 | 0.001 | 0.000 | 0.000 | 0.000 | +| 2 | 0.002 | | 0.000 | 0.000 | +| 3 | 0.002 | | | | +| 4 | 0.002 | | 0.009 | 0.000 | +| 8 | | | 0.021 | 0.000 | +| 16 | | | 0.043 | 0.003 | +| 32 | | | 0.078 | 0.019 | +| 64 | | | 0.101 | 0.029 | +| 128 | | | 0.104 | 0.038 | +| 1536 | | | — | — | + +## Function-preserving reparameterization (plan section 4, Table 4) + +Two transformations that leave the layer function exactly unchanged. Agreement is the Adjusted Rand Index of the recovered neuron partition against the original; the rest module counts as one label. The identity replay is the achievable ceiling for this pipeline. + +### l2 + +| transformation | ARI (all neurons) | ARI (core-module neurons) | exact core-module recovery | modules C → C | +| --- | ---: | ---: | ---: | ---: | +| identity replay (control) | 1.0000 | 1.0000 | 1.000 | 3 → 3 | +| positive scaling a_j, 1/a_j | 1.0000 | 1.0000 | 1.000 | 3 → 3 | +| positive half split 32 atoms — free C | -0.0036 | 0.0000 | 0.000 | 3 → 43 | +| positive half split 32 atoms — matched C | -0.0019 | 0.0000 | 0.000 | 3 → 3 | +| positive half split 32 atoms — matched C collinear collapsed | 1.0000 | 1.0000 | 1.000 | 3 → 3 | +| baseline: raw-covariance clustering, same scaling | 0.8119 | — | — | 3 → 3 | + +Affinity change on atoms that were not split: fcm 0.0e+00, cosine 0.0e+00. The clustering input on those atoms is therefore preserved to floating-point precision; the partition still moves because splitting turns an atom's self-coupling into an off-diagonal edge, so each pair of copies forms a near-disconnected 2-clique that adds a near-zero normalized-Laplacian eigenvalue. The raw-covariance baseline row is metric-independent by construction. +Collapsing exactly-collinear atoms first (discoverable from the Gram alone, and a no-op on the original layer: 3072 groups for 3072 neurons) merges 3104 atoms back to 3072 with a Gram differing from the original by 0.0 and restores the partition. + +### fisher + +| transformation | ARI (all neurons) | ARI (core-module neurons) | exact core-module recovery | modules C → C | +| --- | ---: | ---: | ---: | ---: | +| identity replay (control) | 1.0000 | — | 0.000 | 0 → 0 | +| positive scaling a_j, 1/a_j | 1.0000 | — | 0.000 | 0 → 0 | +| positive half split 32 atoms — free C | 0.0000 | — | 0.000 | 0 → 63 | +| positive half split 32 atoms — matched C | 0.0000 | — | 0.000 | 0 → 1 | +| positive half split 32 atoms — matched C collinear collapsed | 1.0000 | — | 0.000 | 0 → 0 | +| baseline: raw-covariance clustering, same scaling | 0.8119 | — | — | 3 → 3 | + +Affinity change on atoms that were not split: fcm 0.0e+00, cosine 0.0e+00. The clustering input on those atoms is therefore preserved to floating-point precision; the partition still moves because splitting turns an atom's self-coupling into an off-diagonal edge, so each pair of copies forms a near-disconnected 2-clique that adds a near-zero normalized-Laplacian eigenvalue. The raw-covariance baseline row is metric-independent by construction. +Collapsing exactly-collinear atoms first (discoverable from the Gram alone, and a no-op on the original layer: 3072 groups for 3072 neurons) merges 3104 atoms back to 3072 with a Gram differing from the original by 0.0 and restores the partition. + +## Matched-count loss recovered (test, dose 1) + +Full-ablation loss increase -0.0010 nats over 128 sequences; loss recovered = 1 − patched increase / full increase. + +**Loss recovered vs C** + +| C | modules_l2 | modules_fisher | fcm_l2_blocks | fcm_fisher_blocks | sae | transcoder | pca | neuron_mass | +| ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | +| 1 | -0.286 | 1.000 | -0.050 | -0.218 | 0.696 | 0.033 | -1.330 | -0.164 | +| 2 | -0.203 | | -0.039 | -0.192 | 0.604 | 0.015 | -1.687 | -0.211 | +| 3 | -0.218 | | | | | | | | +| 4 | 1.000 | | -0.086 | -0.683 | 1.506 | 0.030 | -3.449 | 0.052 | +| 8 | | | -1.320 | -1.187 | 6.271 | 1.506 | -1.217 | 0.093 | +| 16 | | | -0.468 | 0.212 | 6.063 | 6.491 | 1.839 | -0.729 | +| 32 | | | 0.244 | 0.190 | 5.905 | 6.925 | -0.534 | -0.762 | +| 64 | | | -1.315 | 0.220 | 6.447 | 7.776 | -6.239 | 1.346 | +| 128 | | | | | 8.264 | 5.911 | | | +| 1536 | | | | | 5.713 | 2.806 | | | + +## Failure archetypes (l2, plan section 13) + +- **A, stable and sparse** (3 modules): a bootstrap-stable module with a certified native width at most half its size. m0 (|S|=2, K=2, s*=2), m1 (|S|=2, K=2, s*=2), m2 (|S|=2, K=2, s*=2) +- **B, stable but distributed** (0 modules): closed directions exist but no compact native width. none +- **C, no statistically closed direction** (1 groups, including the rest module): the pipeline declines to interpret these rather than reporting a sparse approximation of an unidentified parent. + +## Failure archetypes (fisher, plan section 13) + +- **A, stable and sparse** (0 modules): a bootstrap-stable module with a certified native width at most half its size. none +- **B, stable but distributed** (0 modules): closed directions exist but no compact native width. none +- **C, no statistically closed direction** (1 groups, including the rest module): the pipeline declines to interpret these rather than reporting a sparse approximation of an unidentified parent. + diff --git a/results/modules/random_gpt2/RESULTS.md b/results/modules/random_gpt2/RESULTS.md new file mode 100644 index 0000000000000000000000000000000000000000..1574d690f90569664ecfa6808efd0e8508bc0a28 --- /dev/null +++ b/results/modules/random_gpt2/RESULTS.md @@ -0,0 +1,48 @@ + +### random_gpt2: partition (l2) + +3 core modules (sizes [2, 2, 2]), rest module 3066 neurons, 46 neurons with no co-assignment partner >= 0.8. Per-bootstrap C: [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]. Saved `results/modules/random_gpt2/partition/partition_l2.json`. + +### random_gpt2: partition (fisher) + +0 core modules (sizes []), rest module 3072 neurons, 0 neurons with no co-assignment partner >= 0.8. Per-bootstrap C: [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]. Saved `results/modules/random_gpt2/partition/partition_fisher.json`. + +### random_gpt2: module geometry (l2) + +Exactness max|f - sum f_S| = 1.67e-06. Test leakage 0.0004 (random-partition null median 0.0004, 52.0% of null draws at or below). Modules: m0 |S|=2 share=0.001 K=2 s*=2; m1 |S|=2 share=0.000 K=2 s*=2; m2 |S|=2 share=0.001 K=2 s*=2; rest |S|=3066 share=0.998 K=0 s*=None. Saved `results/modules/random_gpt2/fit/modules_l2.json`. + +### random_gpt2: module geometry (fisher) + +Exactness max|f - sum f_S| = 0.00e+00. Test leakage 0.0000 (random-partition null median 0.0000, 100.0% of null draws at or below). Modules: rest |S|=3072 share=1.000 K=0 s*=None. Saved `results/modules/random_gpt2/fit/modules_fisher.json`. + +### random_gpt2: module causal effects (l2) + +Full-ablation loss increase -0.0010 nats on 128 sequences. Dose-1 fraction of full effect per module: m0 -0.272, m2 +0.072, m1 -0.022, rest +1.218. Saved `results/modules/random_gpt2/causal/module_effects_l2.json`. + +### random_gpt2: module causal effects (fisher) + +Full-ablation loss increase -0.0010 nats on 128 sequences. Dose-1 fraction of full effect per module: rest +1.000. Saved `results/modules/random_gpt2/causal/module_effects_fisher.json`. + +### random_gpt2: matched-count loss recovered + +51 patched interventions; loss recovered = 1 - (patched loss increase)/(full-ablation loss increase). Saved `results/modules/random_gpt2/causal/matched_count.json`. + +### random_gpt2: module semantic records + +3 records written to `results/modules/random_gpt2/semantics/records` (module_l2_m0, module_l2_m2, module_l2_m1). Hypotheses are generated/selected/frozen by the existing scorer; nothing here touches the LLM. + +### random_gpt2: module reparameterization (l2) + +Adjusted Rand Index of the recovered neuron partition against the original: identity replay 1.0000, positive scaling 1.0000, positive splitting -0.0019 at matched cluster count, 1.0000 after collapsing exactly-collinear atoms first and -0.0036 when the eigengap rule reselects the cluster count, naive raw-covariance baseline under the same scaling 0.8119. Exact core-module set recovery under scaling 1.000, under splitting at matched cluster count 0.000; 0.0938 of split copies are co-assigned with their twin. Affinity change on unsplit atoms under splitting: {'fcm': 0.0, 'cosine': 0.0}. Saved `results/modules/random_gpt2/reparameterization/reparameterization.json`. + +### random_gpt2: module reparameterization (fisher) + +Adjusted Rand Index of the recovered neuron partition against the original: identity replay 1.0000, positive scaling 1.0000, positive splitting 0.0000 at matched cluster count, 1.0000 after collapsing exactly-collinear atoms first and 0.0000 when the eigengap rule reselects the cluster count, naive raw-covariance baseline under the same scaling 0.8119. Exact core-module set recovery under scaling 0.000, under splitting at matched cluster count 0.000; 0.0312 of split copies are co-assigned with their twin. Affinity change on unsplit atoms under splitting: {'fcm': 0.0, 'cosine': 0.0}. Saved `results/modules/random_gpt2/reparameterization/reparameterization.json`. + +### random_gpt2: partition sensitivity (l2) + +Swept affinity ['square', 'abs'], k-NN [0, 10, 30], fixed cluster counts [16, 64], co-assignment thresholds [0.6, 0.7, 0.8, 0.9, 1.0] and bulk caps [0.5, 1.0] (80 configurations). Best held-out core-module energy share: 1.0000 (1 modules, affinity square, knn 0, C=3, threshold 0.6, cap 1.0); 1.0000 (1 modules, affinity square, knn 10, C=3, threshold 0.6, cap 1.0); 1.0000 (1 modules, affinity square, knn 30, C=3, threshold 0.6, cap 1.0). Saved `results/modules/random_gpt2/sweep/sweep.json`. + +### random_gpt2: partition sensitivity (fisher) + +Swept affinity ['square', 'abs'], k-NN [0, 10, 30], fixed cluster counts [16, 64], co-assignment thresholds [0.6, 0.7, 0.8, 0.9, 1.0] and bulk caps [0.5, 1.0] (80 configurations). Best held-out core-module energy share: 1.0000 (1 modules, affinity square, knn 0, C=2, threshold 0.6, cap 1.0); 1.0000 (1 modules, affinity square, knn 0, C=2, threshold 0.7, cap 1.0); 1.0000 (1 modules, affinity square, knn 0, C=2, threshold 0.8, cap 1.0). Saved `results/modules/random_gpt2/sweep/sweep.json`. diff --git a/results/modules/random_gpt2/causal/matched_count.json b/results/modules/random_gpt2/causal/matched_count.json new file mode 100644 index 0000000000000000000000000000000000000000..d16c1a77f26ff2a5c909a16e51acd16cf8b3163f --- /dev/null +++ b/results/modules/random_gpt2/causal/matched_count.json @@ -0,0 +1,450 @@ +{ + "base": { + "sequences": 128, + "mean_clean_loss": 11.041707038879395, + "mean_full_ablation_loss": 11.04073715209961, + "full_ablation_loss_increase": -0.0009698867797851562, + "intervention": "subtract dose * write at the penultimate position; predict the final token (same as evaluate.py)" + }, + "rows": [ + { + "method": "modules_l2", + "C": 1, + "object": "m0", + "loss_recovered": -0.28624323992133727, + "mean_loss_change": -0.001247510313987732, + "kl_clean_patched": 0.005266418214887381, + "neurons_used": 2, + "includes_rest": false + }, + { + "method": "modules_l2", + "C": 2, + "object": "m2", + 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One extreme on each issue is represented by Kenneth Rexroth 's One Hundred Poems From the", + "token_texts": [ + ".", + " ", + "ix", + " )", + " .", + " One", + " extreme", + " on", + " each", + " issue", + " is", + " represented", + " by", + " Kenneth", + " Rex", + "roth", + " '", + "s", + " One", + " Hundred", + " Po", + "ems", + " From", + " the" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.06553281843662262 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.06553281843662262, + "quantile": 0.7607421875, + "active": false, + "media": {}, + "metadata": { + "sequence": 101, + "split": "test" + } + }, + { + "example_id": "test_102_15", + "text": " . His are free translations , which seek to conceal the parallelisms through enj", + "token_texts": [ + " .", + " His", + " are", + " free", + " translations", + " ,", + " which", + " seek", + " to", + " conceal", + " the", + " parallel", + "isms", + " through", + " en", + "j" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.04378531128168106 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.04378531128168106, + "quantile": 0.6337890625, + "active": false, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_102_31", + "text": " to conceal the parallelisms through enjambement and expansion and contraction of the content ; his responses to the allusions", + "token_texts": [ + " to", + " conceal", + " the", + " parallel", + "isms", + " through", + " en", + "j", + "amb", + "ement", + " and", + " expansion", + " and", + " contraction", + " of", + " the", + " content", + " ;", + " his", + " responses", + " to", + " the", + " all", + "usions" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.020990394055843353 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.020990394055843353, + "quantile": 0.4189453125, + "active": false, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_102_47", + "text": " content ; his responses to the allusions are firstly to omit most of these poems from his selection , and secondly to", + "token_texts": [ + " content", + " ;", + " his", + " responses", + " to", + " the", + " all", + "usions", + " are", + " first", + "ly", + " to", + " omit", + " most", + " of", + " these", + " poems", + " from", + " his", + " selection", + " ,", + " and", + " secondly", + " to" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.03667783737182617 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.03667783737182617, + "quantile": 0.5927734375, + "active": false, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_102_62", + "text": " these poems from his selection , and secondly to \" translate out \" the references in those works which he does select . ", + "token_texts": [ + " these", + " poems", + " from", + " his", + " selection", + " ,", + " and", + " secondly", + " to", + " \"", + " translate", + " out", + " \"", + " the", + " references", + " in", + " those", + " works", + " which", + " he", + " does", + " select", + " .", + " " + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0360080860555172 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0360080860555172, + "quantile": 0.5869140625, + "active": false, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_103_15", + "text": "<|endoftext|> Other translators have placed much greater weight on trying to convey a sense of", + "token_texts": [ + "<|endoftext|>", + " Other", + " transl", + "ators", + " have", + " placed", + " much", + " greater", + " weight", + " on", + " trying", + " to", + " convey", + " a", + " sense", + " of" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.022331729531288147 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.022331729531288147, + "quantile": 0.4462890625, + "active": false, + "media": {}, + "metadata": { + "sequence": 103, + "split": "test" + } + }, + { + "example_id": "test_103_31", + "text": " weight on trying to convey a sense of the poetic forms used by Du Fu . Vikram Seth in Three Chinese Poets", + "token_texts": [ + " weight", + " on", + " trying", + " to", + " convey", + " a", + " sense", + " of", + " the", + " poetic", + " forms", + " used", + " by", + " Du", + " Fu", + " .", + " Vik", + "ram", + " Seth", + " in", + " Three", + " Chinese", + " Po", + "ets" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.023151276633143425 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.023151276633143425, + "quantile": 0.4599609375, + "active": false, + "media": {}, + "metadata": { + "sequence": 103, + "split": "test" + } + }, + { + "example_id": "test_103_47", + "text": " Vikram Seth in Three Chinese Poets uses English @-@ style rhyme schemes , whereas Keith Holyoak in F", + "token_texts": [ + " Vik", + "ram", + " Seth", + " in", + " Three", + " Chinese", + " Po", + "ets", + " uses", + " English", + " @", + "-", + "@", + " style", + " rhy", + "me", + " schemes", + " ,", + " whereas", + " Keith", + " Holy", + "oak", + " in", + " F" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.05343860015273094 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.05343860015273094, + "quantile": 0.7080078125, + "active": false, + "media": {}, + "metadata": { + "sequence": 103, + "split": "test" + } + }, + { + "example_id": "test_103_62", + "text": "me schemes , whereas Keith Holyoak in Facing the Moon approximates the Chinese rhyme scheme ; both use end @", + "token_texts": [ + "me", + " schemes", + " ,", + " whereas", + " Keith", + " Holy", + "oak", + " in", + " F", + "acing", + " the", + " Moon", + " approx", + "imates", + " the", + " Chinese", + " rhy", + "me", + " scheme", + " ;", + " both", + " use", + " end", + " @" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.012461750768125057 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.012461750768125057, + "quantile": 0.2802734375, + "active": false, + "media": {}, + "metadata": { + "sequence": 103, + "split": "test" + } + }, + { + "example_id": "test_104_15", + "text": "@ stopped lines and preserve some degree of parallelism . In The Selected Poems", + "token_texts": [ + "@", + " stopped", + " lines", + " and", + " preserve", + " some", + " degree", + " of", + " parallel", + "ism", + " .", + " In", + " The", + " Selected", + " Po", + "ems" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.08790609240531921 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.08790609240531921, + "quantile": 0.8251953125, + "active": false, + "media": {}, + "metadata": { + "sequence": 104, + "split": "test" + } + }, + { + "example_id": "test_104_31", + "text": " parallelism . 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Penalty minutes are the total number of minutes assigned to a player for", + "token_texts": [ + " player", + " '", + "s", + " goals", + " and", + " assists", + " are", + " collectively", + " called", + " points", + " .", + " Penalty", + " minutes", + " are", + " the", + " total", + " number", + " of", + " minutes", + " assigned", + " to", + " a", + " player", + " for" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0013391461689025164 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0013391461689025164, + "quantile": 0.583740234375, + "active": false, + "media": {}, + "metadata": { + "sequence": 169, + "split": "train" + } + }, + { + "example_id": "train_169_47", + "text": " number of minutes assigned to a player for infractions assessed during the season.Plus @-@ minus is a statistic that", + "token_texts": [ + " number", + " of", + " minutes", + " assigned", + " to", + " a", + " player", + " for", + " inf", + "ractions", + " assessed", + " during", + " the", + " season", + ".", + "Plus", + " @", + "-", + "@", + " minus", + " is", + " a", + " statistic", + " that" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.001861505676060915 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.001861505676060915, + "quantile": 0.763916015625, + "active": false, + "media": {}, + "metadata": { + "sequence": 169, + "split": "train" + } + }, + { + "example_id": "train_169_62", + "text": "Plus @-@ minus is a statistic that tracks when a player was on the ice while goals were scored , both for", + "token_texts": [ + "Plus", + " @", + "-", + "@", + " minus", + " is", + " a", + " statistic", + " that", + " tracks", + " when", + " a", + " player", + " was", + " on", + " the", + " ice", + " while", + " goals", + " were", + " scored", + " ,", + " both", + " for" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0009609872940927744 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0009609872940927744, + "quantile": 0.343505859375, + "active": false, + "media": {}, + "metadata": { + "sequence": 169, + "split": "train" + } + }, + { + "example_id": "train_170_15", + "text": " against their team , though some in game situations will not effect the statistic . 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", + "token_texts": [ + " situations", + " will", + " not", + " effect", + " the", + " statistic", + " .", + " Below", + " is", + " a", + " listing", + " of", + " all", + " player", + " statistics", + " for", + " the", + " Blue", + " Jackets", + " during", + " the", + " season", + " .", + " " + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0014504343271255493 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0014504343271255493, + "quantile": 0.635498046875, + "active": false, + "media": {}, + "metadata": { + "sequence": 170, + "split": "train" + } + }, + { + "example_id": "train_170_47", + "text": " the Blue Jackets during the season . \n<|endoftext|> = = = Skaters = = = \n<|endoftext|> Note : Pos", + "token_texts": [ + " the", + " Blue", + " Jackets", + " during", + " the", + " season", + " .", + " ", + "\n", + "<|endoftext|>", + " =", + " =", + " =", + " Sk", + "aters", + " =", + " =", + " =", + " ", + "\n", + "<|endoftext|>", + " Note", + " :", + " Pos" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0010814749402925372 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0010814749402925372, + "quantile": 0.426513671875, + "active": false, + "media": {}, + "metadata": { + "sequence": 170, + "split": "train" + } + }, + { + "example_id": "train_170_62", + "text": " = = = \n<|endoftext|> Note : Pos \n<|endoftext|> = Position ; GP = \n<|endoftext|> Games played in ;", + "token_texts": [ + " =", + " =", + " =", + " ", + "\n", + "<|endoftext|>", + " Note", + " :", + " Pos", + " ", + "\n", + "<|endoftext|>", + " =", + " Position", + " ;", + " GP", + " =", + " ", + "\n", + "<|endoftext|>", + " Games", + " played", + " in", + " ;" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0008586274925619364 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0008586274925619364, + "quantile": 0.285888671875, + "active": false, + "media": {}, + "metadata": { + "sequence": 170, + "split": "train" + } + }, + { + "example_id": "train_171_15", + "text": " \n<|endoftext|> = Goals ; A = \n<|endoftext|> Assists ; Pts", + "token_texts": [ + " ", + "\n", + "<|endoftext|>", + " =", + " Goals", + " ;", + " A", + " =", + " ", + "\n", + "<|endoftext|>", + " Ass", + "ists", + " ;", + " P", + "ts" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.049453552812337875 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.049453552812337875, + "quantile": 0.985107421875, + "active": true, + "media": {}, + "metadata": { + "sequence": 171, + "split": "train" + } + }, + { + "example_id": "train_171_31", + "text": " \n<|endoftext|> Assists ; 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W \n<|endoftext|>", + "token_texts": [ + "I", + " =", + " ", + "\n", + "<|endoftext|>", + " Time", + " On", + " Ice", + " (", + " minutes", + " )", + " ;", + " W", + " ", + "\n", + "<|endoftext|>" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0006464781472459435 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.0006464781472459435, + "quantile": 0.184326171875, + "active": false, + "media": {}, + "metadata": { + "sequence": 172, + "split": "train" + } + }, + { + "example_id": "train_172_31", + "text": " ( minutes ) ; W \n<|endoftext|> = Wins ; L = \n<|endoftext|> Losses ; OT \n<|endoftext|> =", + "token_texts": [ + " (", + " minutes", + " )", + " ;", + " W", + " ", + "\n", + "<|endoftext|>", + " =", + " Wins", + " ;", + " L", + " =", + " ", + "\n", + "<|endoftext|>", + " Loss", + "es", + " ;", + " OT", + " ", + "\n", + "<|endoftext|>", + " =" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0015049583744257689 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0015049583744257689, + "quantile": 0.660400390625, + "active": false, + "media": {}, + "metadata": { + "sequence": 172, + "split": "train" + } + }, + { + "example_id": "train_172_47", + "text": " Losses ; OT \n<|endoftext|> = Overtime Losses ; GA = \n<|endoftext|> Goals Against ; GAA =", + "token_texts": [ + " Loss", + "es", + " ;", + " OT", + " ", + "\n", + "<|endoftext|>", + " =", + " Overt", + "ime", + " Loss", + "es", + " ;", + " GA", + " =", + " ", + "\n", + "<|endoftext|>", + " Goals", + " Against", + " ;", + " G", + "AA", + " =" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0011373471934348345 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0011373471934348345, + "quantile": 0.466552734375, + "active": false, + "media": {}, + "metadata": { + "sequence": 172, + "split": "train" + } + }, + { + "example_id": "train_172_62", + "text": " \n<|endoftext|> Goals Against ; GAA = Goals Against Average ; SA = Shots Against ; SV \n<|endoftext|> = S", + "token_texts": [ + " ", + "\n", + "<|endoftext|>", + " Goals", + " Against", + " ;", + " G", + "AA", + " =", + " Goals", + " Against", + " Average", + " ;", + " SA", + " =", + " Shots", + " Against", + " ;", + " SV", + " ", + "\n", + "<|endoftext|>", + " =", + " S" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0010386209469288588 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0010386209469288588, + "quantile": 0.396240234375, + "active": false, + "media": {}, + "metadata": { + "sequence": 172, + "split": "train" + } + }, + { + "example_id": "train_173_15", + "text": " ; 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The equivalent statistics for the 18 @.@ 5 @-@", + "token_texts": [ + " @", + "-", + "@", + " pound", + " (", + " 50", + " kg", + " )", + " explosive", + " shells", + " .", + " The", + " equivalent", + " statistics", + " for", + " the", + " 18", + " @", + ".", + "@", + " 5", + " @", + "-", + "@" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0008165671024471521 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0008165671024471521, + "quantile": 0.265869140625, + "active": false, + "media": {}, + "metadata": { + "sequence": 263, + "split": "train" + } + }, + { + "example_id": "train_263_62", + "text": " the 18 @.@ 5 @-@ caliber , 6 @.@ 4 @-@ inch gun were 9 @", + "token_texts": [ + " the", + " 18", + " @", + ".", + "@", + " 5", + " @", + "-", + "@", + " caliber", + " ,", + " 6", + " @", + ".", + "@", + " 4", + " @", + "-", + "@", + " inch", + " gun", + " were", + " 9", + " @" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.000772516243159771 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.000772516243159771, + "quantile": 0.250244140625, + "active": false, + "media": {}, + "metadata": { + "sequence": 263, + "split": "train" + } + }, + { + "example_id": "train_264_15", + "text": "@ 110 pounds ( 4 @,@ 130 kg ) with 80 @-@", + "token_texts": [ + "@", + " 110", + " pounds", + " (", + " 4", + " @", + ",", + "@", + " 130", + " kg", + " )", + " with", + " 80", + " @", + "-", + "@" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0008894450147636235 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.0008894450147636235, + "quantile": 0.301025390625, + "active": false, + "media": {}, + "metadata": { + "sequence": 264, + "split": "train" + } + }, + { + "example_id": "train_264_31", + "text": " 130 kg ) with 80 @-@ pound bolts and 64 @-@ pound ( 29 kg ) shells . 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In front of the ram was a spar torpedo that carried 50 pounds ( 23", + "token_texts": [ + " .", + " In", + " front", + " of", + " the", + " ram", + " was", + " a", + " spar", + " torpedo", + " that", + " carried", + " 50", + " pounds", + " (", + " 23" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.03319624811410904 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.03319624811410904, + "quantile": 0.973876953125, + "active": true, + "media": {}, + "metadata": { + "sequence": 265, + "split": "train" + } + }, + { + "example_id": "train_265_31", + "text": " spar torpedo that carried 50 pounds ( 23 kg ) of black powder on a wooden pole connected to an iron lever that could", + "token_texts": [ + " spar", + " torpedo", + " that", + " carried", + " 50", + " pounds", + " (", + " 23", + " kg", + " )", + " of", + " black", + " powder", + " on", + " a", + " wooden", + " pole", + " connected", + " to", + " an", + " iron", + " lever", + " that", + " could" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0009459470747970045 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0009459470747970045, + "quantile": 0.336669921875, + "active": false, + "media": {}, + "metadata": { + "sequence": 265, + "split": "train" + } + }, + { + "example_id": "train_265_47", + "text": " pole connected to an iron lever that could be raised or lowered by means of pulleys . \n<|endoftext|> On 31 July", + "token_texts": [ + " pole", + " connected", + " to", + " an", + " iron", + " lever", + " that", + " could", + " be", + " raised", + " or", + " lowered", + " by", + " means", + " of", + " pul", + "leys", + " .", + " ", + "\n", + "<|endoftext|>", + " On", + " 31", + " July" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0016164893750101328 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0016164893750101328, + "quantile": 0.718017578125, + "active": false, + "media": {}, + "metadata": { + "sequence": 265, + "split": "train" + } + }, + { + "example_id": "train_265_62", + "text": " pulleys . \n<|endoftext|> On 31 July 1862 , under the command of Lieutenant Charles H. 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One extreme on each issue is represented by Kenneth Rexroth 's One Hundred Poems From the", + "token_texts": [ + ".", + " ", + "ix", + " )", + " .", + " One", + " extreme", + " on", + " each", + " issue", + " is", + " represented", + " by", + " Kenneth", + " Rex", + "roth", + " '", + "s", + " One", + " Hundred", + " Po", + "ems", + " From", + " the" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0013425822835415602 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0013425822835415602, + "quantile": 0.6103515625, + "active": false, + "media": {}, + "metadata": { + "sequence": 101, + "split": "test" + } + }, + { + "example_id": "test_102_15", + "text": " . His are free translations , which seek to conceal the parallelisms through enj", + "token_texts": [ + " .", + " His", + " are", + " free", + " translations", + " ,", + " which", + " seek", + " to", + " conceal", + " the", + " parallel", + "isms", + " through", + " en", + "j" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.000843903049826622 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.000843903049826622, + "quantile": 0.3408203125, + "active": false, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_102_31", + "text": " to conceal the parallelisms through enjambement and expansion and contraction of the content ; his responses to the allusions", + "token_texts": [ + " to", + " conceal", + " the", + " parallel", + "isms", + " through", + " en", + "j", + "amb", + "ement", + " and", + " expansion", + " and", + " contraction", + " of", + " the", + " content", + " ;", + " his", + " responses", + " to", + " the", + " all", + "usions" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0007033413276076317 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0007033413276076317, + "quantile": 0.2490234375, + "active": false, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_102_47", + "text": " content ; his responses to the allusions are firstly to omit most of these poems from his selection , and secondly to", + "token_texts": [ + " content", + " ;", + " his", + " responses", + " to", + " the", + " all", + "usions", + " are", + " first", + "ly", + " to", + " omit", + " most", + " of", + " these", + " poems", + " from", + " his", + " selection", + " ,", + " and", + " secondly", + " to" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0006944815395399928 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0006944815395399928, + "quantile": 0.2431640625, + "active": false, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_102_62", + "text": " these poems from his selection , and secondly to \" translate out \" the references in those works which he does select . ", + "token_texts": [ + " these", + " poems", + " from", + " his", + " selection", + " ,", + " and", + " secondly", + " to", + " \"", + " translate", + " out", + " \"", + " the", + " references", + " in", + " those", + " works", + " which", + " he", + " does", + " select", + " .", + " " + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0016495913732796907 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0016495913732796907, + "quantile": 0.7919921875, + "active": false, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_103_15", + "text": "<|endoftext|> Other translators have placed much greater weight on trying to convey a sense of", + "token_texts": [ + "<|endoftext|>", + " Other", + " transl", + "ators", + " have", + " placed", + " much", + " greater", + " weight", + " on", + " trying", + " to", + " convey", + " a", + " sense", + " of" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.039466358721256256 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.039466358721256256, + "quantile": 0.9853515625, + "active": true, + "media": {}, + "metadata": { + "sequence": 103, + "split": "test" + } + }, + { + "example_id": "test_103_31", + "text": " weight on trying to convey a sense of the poetic forms used by Du Fu . Vikram Seth in Three Chinese Poets", + "token_texts": [ + " weight", + " on", + " trying", + " to", + " convey", + " a", + " sense", + " of", + " the", + " poetic", + " forms", + " used", + " by", + " Du", + " Fu", + " .", + " Vik", + "ram", + " Seth", + " in", + " Three", + " Chinese", + " Po", + "ets" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0003816945827566087 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0003816945827566087, + "quantile": 0.0986328125, + "active": false, + "media": {}, + "metadata": { + "sequence": 103, + "split": "test" + } + }, + { + "example_id": "test_103_47", + "text": " Vikram Seth in Three Chinese Poets uses English @-@ style rhyme schemes , whereas Keith Holyoak in F", + "token_texts": [ + " Vik", + "ram", + " Seth", + " in", + " Three", + " Chinese", + " Po", + "ets", + " uses", + " English", + " @", + "-", + "@", + " style", + " rhy", + "me", + " schemes", + " ,", + " whereas", + " Keith", + " Holy", + "oak", + " in", + " F" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0010404511122033 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0010404511122033, + "quantile": 0.4482421875, + "active": false, + "media": {}, + "metadata": { + "sequence": 103, + "split": "test" + } + }, + { + "example_id": "test_103_62", + "text": "me schemes , whereas Keith Holyoak in Facing the Moon approximates the Chinese rhyme scheme ; both use end @", + "token_texts": [ + "me", + " schemes", + " ,", + " whereas", + " Keith", + " Holy", + "oak", + " in", + " F", + "acing", + " the", + " Moon", + " approx", + "imates", + " the", + " Chinese", + " rhy", + "me", + " scheme", + " ;", + " both", + " use", + " end", + " @" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0005562150618061423 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0005562150618061423, + "quantile": 0.1650390625, + "active": false, + "media": {}, + "metadata": { + "sequence": 103, + "split": "test" + } + }, + { + "example_id": "test_104_15", + "text": "@ stopped lines and preserve some degree of parallelism . In The Selected Poems", + "token_texts": [ + "@", + " stopped", + " lines", + " and", + " preserve", + " some", + " degree", + " of", + " parallel", + "ism", + " .", + " In", + " The", + " Selected", + " Po", + "ems" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0035323668271303177 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.0035323668271303177, + "quantile": 0.8681640625, + "active": false, + "media": {}, + "metadata": { + "sequence": 104, + "split": "test" + } + }, + { + "example_id": "test_104_31", + "text": " parallelism . 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Penalty minutes are the total number of minutes assigned to a player for", + "token_texts": [ + " player", + " '", + "s", + " goals", + " and", + " assists", + " are", + " collectively", + " called", + " points", + " .", + " Penalty", + " minutes", + " are", + " the", + " total", + " number", + " of", + " minutes", + " assigned", + " to", + " a", + " player", + " for" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.06282906234264374 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.06282906234264374, + "quantile": 0.781005859375, + "active": false, + "media": {}, + "metadata": { + "sequence": 169, + "split": "train" + } + }, + { + "example_id": "train_169_47", + "text": " number of minutes assigned to a player for infractions assessed during the season.Plus @-@ minus is a statistic that", + "token_texts": [ + " number", + " of", + " minutes", + " assigned", + " to", + " a", + " player", + " for", + " inf", + "ractions", + " assessed", + " during", + " the", + " season", + ".", + "Plus", + " @", + "-", + "@", + " minus", + " is", + " a", + " statistic", + " that" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.042226217687129974 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.042226217687129974, + "quantile": 0.678955078125, + "active": false, + "media": {}, + "metadata": { + "sequence": 169, + "split": "train" + } + }, + { + "example_id": "train_169_62", + "text": "Plus @-@ minus is a statistic that tracks when a player was on the ice while goals were scored , both for", + "token_texts": [ + "Plus", + " @", + "-", + "@", + " minus", + " is", + " a", + " statistic", + " that", + " tracks", + " when", + " a", + " player", + " was", + " on", + " the", + " ice", + " while", + " goals", + " were", + " scored", + " ,", + " both", + " for" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.11791794747114182 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.11791794747114182, + "quantile": 0.927978515625, + "active": true, + "media": {}, + "metadata": { + "sequence": 169, + "split": "train" + } + }, + { + "example_id": "train_170_15", + "text": " against their team , though some in game situations will not effect the statistic . Below", + "token_texts": [ + " against", + " their", + " team", + " ,", + " though", + " some", + " in", + " game", + " situations", + " will", + " not", + " effect", + " the", + " statistic", + " .", + " Below" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.01217563170939684 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.01217563170939684, + "quantile": 0.369873046875, + "active": false, + "media": {}, + "metadata": { + "sequence": 170, + "split": "train" + } + }, + { + "example_id": "train_170_31", + "text": " situations will not effect the statistic . Below is a listing of all player statistics for the Blue Jackets during the season . ", + "token_texts": [ + " situations", + " will", + " not", + " effect", + " the", + " statistic", + " .", + " Below", + " is", + " a", + " listing", + " of", + " all", + " player", + " statistics", + " for", + " the", + " Blue", + " Jackets", + " during", + " the", + " season", + " .", + " " + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.05352386087179184 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.05352386087179184, + "quantile": 0.730712890625, + "active": false, + "media": {}, + "metadata": { + "sequence": 170, + "split": "train" + } + }, + { + "example_id": "train_170_47", + "text": " the Blue Jackets during the season . \n<|endoftext|> = = = Skaters = = = \n<|endoftext|> Note : Pos", + "token_texts": [ + " the", + " Blue", + " Jackets", + " during", + " the", + " season", + " .", + " ", + "\n", + "<|endoftext|>", + " =", + " =", + " =", + " Sk", + "aters", + " =", + " =", + " =", + " ", + "\n", + "<|endoftext|>", + " Note", + " :", + " Pos" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0027897930704057217 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0027897930704057217, + "quantile": 0.156494140625, + "active": false, + "media": {}, + "metadata": { + "sequence": 170, + "split": "train" + } + }, + { + "example_id": "train_170_62", + "text": " = = = \n<|endoftext|> Note : Pos \n<|endoftext|> = Position ; GP = \n<|endoftext|> Games played in ;", + "token_texts": [ + " =", + " =", + " =", + " ", + "\n", + "<|endoftext|>", + " Note", + " :", + " Pos", + " ", + "\n", + "<|endoftext|>", + " =", + " Position", + " ;", + " GP", + " =", + " ", + "\n", + "<|endoftext|>", + " Games", + " played", + " in", + " ;" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.03892260789871216 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.03892260789871216, + "quantile": 0.653564453125, + "active": false, + "media": {}, + "metadata": { + "sequence": 170, + "split": "train" + } + }, + { + "example_id": "train_171_15", + "text": " \n<|endoftext|> = Goals ; A = \n<|endoftext|> Assists ; Pts", + "token_texts": [ + " ", + "\n", + "<|endoftext|>", + " =", + " Goals", + " ;", + " A", + " =", + " ", + "\n", + "<|endoftext|>", + " Ass", + "ists", + " ;", + " P", + "ts" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0049326783046126366 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.0049326783046126366, + "quantile": 0.217041015625, + "active": false, + "media": {}, + "metadata": { + "sequence": 171, + "split": "train" + } + }, + { + "example_id": "train_171_31", + "text": " \n<|endoftext|> Assists ; 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W \n<|endoftext|>", + "token_texts": [ + "I", + " =", + " ", + "\n", + "<|endoftext|>", + " Time", + " On", + " Ice", + " (", + " minutes", + " )", + " ;", + " W", + " ", + "\n", + "<|endoftext|>" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.04042965918779373 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.04042965918779373, + "quantile": 0.663818359375, + "active": false, + "media": {}, + "metadata": { + "sequence": 172, + "split": "train" + } + }, + { + "example_id": "train_172_31", + "text": " ( minutes ) ; W \n<|endoftext|> = Wins ; L = \n<|endoftext|> Losses ; OT \n<|endoftext|> =", + "token_texts": [ + " (", + " minutes", + " )", + " ;", + " W", + " ", + "\n", + "<|endoftext|>", + " =", + " Wins", + " ;", + " L", + " =", + " ", + "\n", + "<|endoftext|>", + " Loss", + "es", + " ;", + " OT", + " ", + "\n", + "<|endoftext|>", + " =" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0037588062696158886 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0037588062696158886, + "quantile": 0.184326171875, + "active": false, + "media": {}, + "metadata": { + "sequence": 172, + "split": "train" + } + }, + { + "example_id": "train_172_47", + "text": " Losses ; OT \n<|endoftext|> = Overtime Losses ; GA = \n<|endoftext|> Goals Against ; GAA =", + "token_texts": [ + " Loss", + "es", + " ;", + " OT", + " ", + "\n", + "<|endoftext|>", + " =", + " Overt", + "ime", + " Loss", + "es", + " ;", + " GA", + " =", + " ", + "\n", + "<|endoftext|>", + " Goals", + " Against", + " ;", + " G", + "AA", + " =" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.02395416423678398 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.02395416423678398, + "quantile": 0.529541015625, + "active": false, + "media": {}, + "metadata": { + "sequence": 172, + "split": "train" + } + }, + { + "example_id": "train_172_62", + "text": " \n<|endoftext|> Goals Against ; GAA = Goals Against Average ; SA = Shots Against ; SV \n<|endoftext|> = S", + "token_texts": [ + " ", + "\n", + "<|endoftext|>", + " Goals", + " Against", + " ;", + " G", + "AA", + " =", + " Goals", + " Against", + " Average", + " ;", + " SA", + " =", + " Shots", + " Against", + " ;", + " SV", + " ", + "\n", + "<|endoftext|>", + " =", + " S" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0354657843708992 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0354657843708992, + "quantile": 0.631103515625, + "active": false, + "media": {}, + "metadata": { + "sequence": 172, + "split": "train" + } + }, + { + "example_id": "train_173_15", + "text": " ; 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One extreme on each issue is represented by Kenneth Rexroth 's One Hundred Poems From the", + "token_texts": [ + ".", + " ", + "ix", + " )", + " .", + " One", + " extreme", + " on", + " each", + " issue", + " is", + " represented", + " by", + " Kenneth", + " Rex", + "roth", + " '", + "s", + " One", + " Hundred", + " Po", + "ems", + " From", + " the" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.006918599829077721 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.006918599829077721, + "quantile": 0.2880859375, + "active": false, + "media": {}, + "metadata": { + "sequence": 101, + "split": "test" + } + }, + { + "example_id": "test_102_15", + "text": " . His are free translations , which seek to conceal the parallelisms through enj", + "token_texts": [ + " .", + " His", + " are", + " free", + " translations", + " ,", + " which", + " seek", + " to", + " conceal", + " the", + " parallel", + "isms", + " through", + " en", + "j" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.35692158341407776 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.35692158341407776, + "quantile": 0.9990234375, + "active": true, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_102_31", + "text": " to conceal the parallelisms through enjambement and expansion and contraction of the content ; his responses to the allusions", + "token_texts": [ + " to", + " conceal", + " the", + " parallel", + "isms", + " through", + " en", + "j", + "amb", + "ement", + " and", + " expansion", + " and", + " contraction", + " of", + " the", + " content", + " ;", + " his", + " responses", + " to", + " the", + " all", + "usions" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.14771637320518494 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.14771637320518494, + "quantile": 0.9638671875, + "active": true, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_102_47", + "text": " content ; his responses to the allusions are firstly to omit most of these poems from his selection , and secondly to", + "token_texts": [ + " content", + " ;", + " his", + " responses", + " to", + " the", + " all", + "usions", + " are", + " first", + "ly", + " to", + " omit", + " most", + " of", + " these", + " poems", + " from", + " his", + " selection", + " ,", + " and", + " secondly", + " to" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.21628759801387787 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.21628759801387787, + "quantile": 0.9892578125, + "active": true, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_102_62", + "text": " these poems from his selection , and secondly to \" translate out \" the references in those works which he does select . ", + "token_texts": [ + " these", + " poems", + " from", + " his", + " selection", + " ,", + " and", + " secondly", + " to", + " \"", + " translate", + " out", + " \"", + " the", + " references", + " in", + " those", + " works", + " which", + " he", + " does", + " select", + " .", + " " + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.09903081506490707 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.09903081506490707, + "quantile": 0.8955078125, + "active": true, + "media": {}, + "metadata": { + "sequence": 102, + "split": "test" + } + }, + { + "example_id": "test_103_15", + "text": "<|endoftext|> Other translators have placed much greater weight on trying to convey a sense of", + "token_texts": [ + "<|endoftext|>", + " Other", + " transl", + "ators", + " have", + " placed", + " much", + " greater", + " weight", + " on", + " trying", + " to", + " convey", + " a", + " sense", + " of" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.09052129089832306 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.09052129089832306, + "quantile": 0.8759765625, + "active": false, + "media": {}, + "metadata": { + "sequence": 103, + "split": "test" + } + }, + { + "example_id": "test_103_31", + "text": " weight on trying to convey a sense of the poetic forms used by Du Fu . Vikram Seth in Three Chinese Poets", + "token_texts": [ + " weight", + " on", + " trying", + " to", + " convey", + " a", + " sense", + " of", + " the", + " poetic", + " forms", + " used", + " by", + " Du", + " Fu", + " .", + " Vik", + "ram", + " Seth", + " in", + " Three", + " Chinese", + " Po", + "ets" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0009798177052289248 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.0009798177052289248, + "quantile": 0.0849609375, + "active": false, + "media": {}, + "metadata": { + "sequence": 103, + "split": "test" + } + }, + { + "example_id": "test_103_47", + "text": " Vikram Seth in Three Chinese Poets uses English @-@ style rhyme schemes , whereas Keith Holyoak in F", + "token_texts": [ + " Vik", + "ram", + " Seth", + " in", + " Three", + " Chinese", + " Po", + "ets", + " uses", + " English", + " @", + "-", + "@", + " style", + " rhy", + "me", + " schemes", + " ,", + " whereas", + " Keith", + " Holy", + "oak", + " in", + " F" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.05518767237663269 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.05518767237663269, + "quantile": 0.7568359375, + "active": false, + "media": {}, + "metadata": { + "sequence": 103, + "split": "test" + } + }, + { + "example_id": "test_103_62", + "text": "me schemes , whereas Keith Holyoak in Facing the Moon approximates the Chinese rhyme scheme ; both use end @", + "token_texts": [ + "me", + " schemes", + " ,", + " whereas", + " Keith", + " Holy", + "oak", + " in", + " F", + "acing", + " the", + " Moon", + " approx", + "imates", + " the", + " Chinese", + " rhy", + "me", + " scheme", + " ;", + " both", + " use", + " end", + " @" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.027315683662891388 + ], + "focus_positions": [ + 23 + ], + "feature_strength": 0.027315683662891388, + "quantile": 0.5751953125, + "active": false, + "media": {}, + "metadata": { + "sequence": 103, + "split": "test" + } + }, + { + "example_id": "test_104_15", + "text": "@ stopped lines and preserve some degree of parallelism . In The Selected Poems", + "token_texts": [ + "@", + " stopped", + " lines", + " and", + " preserve", + " some", + " degree", + " of", + " parallel", + "ism", + " .", + " In", + " The", + " Selected", + " Po", + "ems" + ], + "token_scores": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.002430104883387685 + ], + "focus_positions": [ + 15 + ], + "feature_strength": 0.002430104883387685, + "quantile": 0.1396484375, + "active": false, + "media": {}, + "metadata": { + "sequence": 104, + "split": "test" + } + }, + { + "example_id": "test_104_31", + "text": " parallelism . 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"results/modules/random_gpt2", + "metrics": [ + "l2", + "fisher" + ], + "bootstraps": 12, + "affinities": [ + "square", + "abs" + ], + "knns": [ + 0, + 10, + 30 + ], + "extra_clusters": [ + 16, + 64 + ], + "thresholds": [ + 0.6, + 0.7, + 0.8, + 0.9, + 1.0 + ], + "dimension_threshold": 0.8, + "dimension_max_modules": 400, + "dimension_max_size": 256, + "fractions": [ + 0.5, + 1.0 + ], + "normalization": "fcm", + "min_size": 2, + "restarts": 5, + "seed": 17 + }, + "scope": "core-module count and held-out energy share only; no dimension, width, semantic or causal evaluation" +} \ No newline at end of file diff --git a/results/modules/repar_gpt2.log b/results/modules/repar_gpt2.log new file mode 100644 index 0000000000000000000000000000000000000000..5d04f66e0f83b28b55a351d430a58c56388403ce --- /dev/null +++ b/results/modules/repar_gpt2.log @@ -0,0 +1,14 @@ +2026-09-08 13:51:45 UTC — gpt2: reparameterization stress test, metrics=['l2', 'fisher'], B=12, split 32 atoms +2026-09-08 13:51:50 UTC — gpt2 l2: identity replay ARI 1.0000, exact recovery 1.000 +2026-09-08 13:51:55 UTC — gpt2 l2: scaling ARI 1.0000, exact recovery 1.000, Gram change 0.00e+00 +2026-09-08 13:52:00 UTC — gpt2 l2: splitting [free_C] ARI 0.3618 (core atoms 0.3384), C 10->32, exact recovery 0.091, copies together 0.844 +2026-09-08 13:52:03 UTC — gpt2 l2: splitting [matched_C] ARI 0.0064 (core atoms 0.0102), C 10->10, exact recovery 0.091, copies together 0.250 +2026-09-08 13:52:07 UTC — gpt2 l2: splitting [matched_C_collinear_collapsed] ARI 1.0000 (core atoms 1.0000), C 10->10, exact recovery 1.000, copies together 0.031 +2026-09-08 13:52:08 UTC — gpt2 l2: naive covariance baseline ARI under scaling 0.3308 +2026-09-08 13:52:17 UTC — gpt2 fisher: identity replay ARI 1.0000, exact recovery 1.000 +2026-09-08 13:52:26 UTC — gpt2 fisher: scaling ARI 1.0000, exact recovery 1.000, Gram change 0.00e+00 +2026-09-08 13:52:30 UTC — gpt2 fisher: splitting [free_C] ARI 0.0769 (core atoms 1.0000), C 4->3, exact recovery 0.000, copies together 0.031 +2026-09-08 13:52:33 UTC — gpt2 fisher: splitting [matched_C] ARI 0.6925 (core atoms 0.2217), C 4->4, exact recovery 0.000, copies together 0.062 +2026-09-08 13:52:37 UTC — gpt2 fisher: splitting [matched_C_collinear_collapsed] ARI 1.0000 (core atoms 1.0000), C 4->4, exact recovery 1.000, copies together 0.031 +2026-09-08 13:52:37 UTC — gpt2 fisher: naive covariance baseline ARI under scaling 0.3308 +2026-09-08 13:52:37 UTC — gpt2: reparameterization stress test complete diff --git a/results/modules/repar_qwen.log b/results/modules/repar_qwen.log new file mode 100644 index 0000000000000000000000000000000000000000..da1c856a761e2db687aa9e18419c8ac1042b2648 --- /dev/null +++ b/results/modules/repar_qwen.log @@ -0,0 +1,14 @@ +2026-09-08 13:56:31 UTC — qwen: reparameterization stress test, metrics=['l2', 'fisher'], B=12, split 32 atoms +2026-09-08 13:56:43 UTC — qwen l2: identity replay ARI 1.0000, exact recovery 1.000 +2026-09-08 13:56:55 UTC — qwen l2: scaling ARI 1.0000, exact recovery 1.000, Gram change 0.00e+00 +2026-09-08 13:57:05 UTC — qwen l2: splitting [free_C] ARI 0.0013 (core atoms -0.0473), C 6->28, exact recovery 0.000, copies together 0.8750 +2026-09-08 13:57:15 UTC — qwen l2: splitting [matched_C] ARI -0.0053 (core atoms 0.0000), C 6->6, exact recovery 0.000, copies together 0.1875 +2026-09-08 13:57:25 UTC — qwen l2: splitting [matched_C_collinear_collapsed] ARI 1.0000 (core atoms 1.0000), C 6->6, exact recovery 1.000, copies together 0.0000 +2026-09-08 13:57:27 UTC — qwen l2: naive covariance baseline ARI under scaling 0.3792 +2026-09-08 13:57:48 UTC — qwen fisher: identity replay ARI 1.0000, exact recovery 0.000 +2026-09-08 13:58:08 UTC — qwen fisher: scaling ARI 1.0000, exact recovery 0.000, Gram change 0.00e+00 +2026-09-08 13:58:17 UTC — qwen fisher: splitting [free_C] ARI 1.0000 (core atoms n/a), C 2->2, exact recovery 0.000, copies together 0.0000 +2026-09-08 13:58:26 UTC — qwen fisher: splitting [matched_C] ARI 1.0000 (core atoms n/a), C 2->2, exact recovery 0.000, copies together 0.0000 +2026-09-08 13:58:37 UTC — qwen fisher: splitting [matched_C_collinear_collapsed] ARI 1.0000 (core atoms n/a), C 2->2, exact recovery 0.000, copies together 0.0000 +2026-09-08 13:58:38 UTC — qwen fisher: naive covariance baseline ARI under scaling 0.3792 +2026-09-08 13:58:38 UTC — qwen: reparameterization stress test complete diff --git a/results/modules/repar_random_gpt2.log b/results/modules/repar_random_gpt2.log new file mode 100644 index 0000000000000000000000000000000000000000..3d8209e24a7ab2e91d8d982b454908af7a435d6a --- /dev/null +++ b/results/modules/repar_random_gpt2.log @@ -0,0 +1,14 @@ +2026-09-08 14:00:26 UTC — random_gpt2: reparameterization stress test, metrics=['l2', 'fisher'], B=12, split 32 atoms +2026-09-08 14:00:31 UTC — random_gpt2 l2: identity replay ARI 1.0000, exact recovery 1.000 +2026-09-08 14:00:35 UTC — random_gpt2 l2: scaling ARI 1.0000, exact recovery 1.000, Gram change 0.00e+00 +2026-09-08 14:00:41 UTC — random_gpt2 l2: splitting [free_C] ARI -0.0036 (core atoms 0.0000), C 3->31, exact recovery 0.000, copies together 0.9688 +2026-09-08 14:00:44 UTC — random_gpt2 l2: splitting [matched_C] ARI -0.0019 (core atoms 0.0000), C 3->3, exact recovery 0.000, copies together 0.0938 +2026-09-08 14:00:48 UTC — random_gpt2 l2: splitting [matched_C_collinear_collapsed] ARI 1.0000 (core atoms 1.0000), C 3->3, exact recovery 1.000, copies together 0.0000 +2026-09-08 14:00:48 UTC — random_gpt2 l2: naive covariance baseline ARI under scaling 0.8119 +2026-09-08 14:00:57 UTC — random_gpt2 fisher: identity replay ARI 1.0000, exact recovery 0.000 +2026-09-08 14:01:05 UTC — random_gpt2 fisher: scaling ARI 1.0000, exact recovery 0.000, Gram change 0.00e+00 +2026-09-08 14:01:11 UTC — random_gpt2 fisher: splitting [free_C] ARI 0.0000 (core atoms n/a), C 2->30, exact recovery 0.000, copies together 0.9688 +2026-09-08 14:01:14 UTC — random_gpt2 fisher: splitting [matched_C] ARI 0.0000 (core atoms n/a), C 2->2, exact recovery 0.000, copies together 0.0312 +2026-09-08 14:01:17 UTC — random_gpt2 fisher: splitting [matched_C_collinear_collapsed] ARI 1.0000 (core atoms n/a), C 2->2, exact recovery 0.000, copies together 0.0000 +2026-09-08 14:01:17 UTC — random_gpt2 fisher: naive covariance baseline ARI under scaling 0.8119 +2026-09-08 14:01:17 UTC — random_gpt2: reparameterization stress test complete diff --git a/results/modules/semantics_gpt2.log b/results/modules/semantics_gpt2.log new file mode 100644 index 0000000000000000000000000000000000000000..d06de0a78724be440de237c54f91b198fd473312 --- /dev/null +++ b/results/modules/semantics_gpt2.log @@ -0,0 +1,8 @@ +2026-09-08 13:52:15 UTC — gpt2: semantic evaluation, glm-5.2, 6 records +2026-09-08 13:52:48 UTC — semantics gpt2/module_l2_m0: test AUC 0.499 +2026-09-08 13:52:51 UTC — semantics gpt2/module_fisher_m0: test AUC 0.179 +2026-09-08 13:52:53 UTC — semantics gpt2/module_fisher_m1: test AUC 0.476 +2026-09-08 13:53:11 UTC — semantics gpt2/module_l2_m2: test AUC 0.500 +2026-09-08 13:53:20 UTC — semantics gpt2/module_l2_m3: test AUC 0.520 +2026-09-08 13:53:24 UTC — semantics gpt2/module_l2_m6: test AUC 0.625 +2026-09-08 13:53:24 UTC — gpt2: semantic evaluation finished (6/6 scored) diff --git a/results/modules/semantics_qwen.log b/results/modules/semantics_qwen.log new file mode 100644 index 0000000000000000000000000000000000000000..c7912757dded30bee7b3e30cc4e40795888c2d5b --- /dev/null +++ b/results/modules/semantics_qwen.log @@ -0,0 +1,4 @@ +2026-09-08 13:56:48 UTC — qwen: semantic evaluation, glm-5.2, 2 records +2026-09-08 13:57:24 UTC — semantics qwen/module_l2_m1: test AUC 0.624 +2026-09-08 13:57:26 UTC — semantics qwen/module_l2_m0: test AUC 0.250 +2026-09-08 13:57:26 UTC — qwen: semantic evaluation finished (2/2 scored) diff --git a/results/modules/semantics_random_gpt2.log b/results/modules/semantics_random_gpt2.log new file mode 100644 index 0000000000000000000000000000000000000000..7f614280112b3d0177db67575987472052b528a8 --- /dev/null +++ b/results/modules/semantics_random_gpt2.log @@ -0,0 +1,5 @@ +2026-09-08 14:01:42 UTC — random_gpt2: semantic evaluation, glm-5.2, 3 records +2026-09-08 14:02:19 UTC — semantics random_gpt2/module_l2_m2: test AUC 0.503 +2026-09-08 14:02:19 UTC — semantics random_gpt2/module_l2_m0: test AUC 0.625 +2026-09-08 14:02:29 UTC — semantics random_gpt2/module_l2_m1: test AUC 0.455 +2026-09-08 14:02:29 UTC — random_gpt2: semantic evaluation finished (3/3 scored) diff --git a/results/modules/semantics_records_gpt2.log b/results/modules/semantics_records_gpt2.log new file mode 100644 index 0000000000000000000000000000000000000000..883b4b648da8f75c29c11199044d0527a663297c --- /dev/null +++ b/results/modules/semantics_records_gpt2.log @@ -0,0 +1 @@ +2026-09-08 13:51:41 UTC — gpt2: 6 semantic records prepared diff --git a/results/modules/semantics_records_qwen.log b/results/modules/semantics_records_qwen.log new file mode 100644 index 0000000000000000000000000000000000000000..a7758437acaa6d6b105fc678a4f5661a540de415 --- /dev/null +++ b/results/modules/semantics_records_qwen.log @@ -0,0 +1 @@ +2026-09-08 13:56:27 UTC — qwen: 2 semantic records prepared diff --git a/results/modules/semantics_records_random_gpt2.log b/results/modules/semantics_records_random_gpt2.log new file mode 100644 index 0000000000000000000000000000000000000000..75c928674318235798ee0f6c9b4bfac16314e062 --- /dev/null +++ b/results/modules/semantics_records_random_gpt2.log @@ -0,0 +1 @@ +2026-09-08 14:00:22 UTC — random_gpt2: 3 semantic records prepared diff --git a/results/modules/sweep_gpt2.log b/results/modules/sweep_gpt2.log new file mode 100644 index 0000000000000000000000000000000000000000..e8128adfb534549f1769e774c1ea437e8b800849 --- /dev/null +++ b/results/modules/sweep_gpt2.log @@ -0,0 +1,20 @@ +2026-09-08 14:05:09 UTC — gpt2: partition sensitivity sweep, metrics=['l2', 'fisher'] +2026-09-08 14:05:18 UTC — gpt2 l2: affinity=square knn=0 C=10 -> best core energy share 1.1496 (67 modules at threshold 1.0, cap 1.0); at threshold 0.8 median effective-rank ratio 0.81 over 11 modules, 0% below 0.5 +2026-09-08 14:05:23 UTC — gpt2 l2: affinity=square knn=10 C=22 -> best core energy share 1.1663 (47 modules at threshold 0.8, cap 1.0); at threshold 0.8 median effective-rank ratio 0.92 over 46 modules, 0% below 0.5 +2026-09-08 14:05:28 UTC — gpt2 l2: affinity=square knn=30 C=19 -> best core energy share 1.1627 (40 modules at threshold 0.8, cap 1.0); at threshold 0.8 median effective-rank ratio 0.93 over 39 modules, 0% below 0.5 +2026-09-08 14:05:36 UTC — gpt2 l2: affinity=abs knn=0 C=2 -> best core energy share 1.0364 (24 modules at threshold 1.0, cap 1.0) +2026-09-08 14:05:42 UTC — gpt2 l2: affinity=abs knn=10 C=3 -> best core energy share 1.0434 (49 modules at threshold 1.0, cap 1.0); at threshold 0.8 median effective-rank ratio 0.84 over 1 modules, 0% below 0.5 +2026-09-08 14:05:50 UTC — gpt2 l2: affinity=abs knn=30 C=2 -> best core energy share 1.0131 (2 modules at threshold 0.7, cap 1.0); at threshold 0.8 median effective-rank ratio 0.81 over 2 modules, 0% below 0.5 +2026-09-08 14:05:55 UTC — gpt2 l2: affinity=square knn=0 C=16 -> best core energy share 1.3221 (222 modules at threshold 1.0, cap 0.5); at threshold 0.8 median effective-rank ratio 0.84 over 23 modules, 0% below 0.5 +2026-09-08 14:06:02 UTC — gpt2 l2: affinity=square knn=0 C=64 -> best core energy share 1.3103 (112 modules at threshold 0.8, cap 0.5); at threshold 0.8 median effective-rank ratio 0.90 over 111 modules, 0% below 0.5 +2026-09-08 14:06:02 UTC — gpt2 l2: sweep complete in 53.3s +2026-09-08 14:06:13 UTC — gpt2 fisher: affinity=square knn=0 C=4 -> best core energy share 1.5879 (125 modules at threshold 1.0, cap 0.5); at threshold 0.8 median effective-rank ratio 0.62 over 2 modules, 0% below 0.5 +2026-09-08 14:06:18 UTC — gpt2 fisher: affinity=square knn=10 C=14 -> best core energy share 1.1148 (49 modules at threshold 0.7, cap 0.5); at threshold 0.8 median effective-rank ratio 0.88 over 66 modules, 0% below 0.5 +2026-09-08 14:06:25 UTC — gpt2 fisher: affinity=square knn=30 C=2 -> best core energy share 1.4501 (28 modules at threshold 1.0, cap 1.0) +2026-09-08 14:06:31 UTC — gpt2 fisher: affinity=abs knn=0 C=2 -> best core energy share 1.3932 (72 modules at threshold 1.0, cap 1.0) +2026-09-08 14:06:35 UTC — gpt2 fisher: affinity=abs knn=10 C=4 -> best core energy share 1.1980 (229 modules at threshold 1.0, cap 0.5); at threshold 0.8 median effective-rank ratio 0.95 over 16 modules, 0% below 0.5 +2026-09-08 14:06:40 UTC — gpt2 fisher: affinity=abs knn=30 C=3 -> best core energy share 1.3942 (177 modules at threshold 1.0, cap 0.5); at threshold 0.8 median effective-rank ratio 0.98 over 4 modules, 0% below 0.5 +2026-09-08 14:06:45 UTC — gpt2 fisher: affinity=square knn=0 C=16 -> best core energy share 1.1465 (45 modules at threshold 0.8, cap 1.0); at threshold 0.8 median effective-rank ratio 0.87 over 44 modules, 5% below 0.5 +2026-09-08 14:06:51 UTC — gpt2 fisher: affinity=square knn=0 C=64 -> best core energy share 1.0741 (91 modules at threshold 0.6, cap 0.5); at threshold 0.8 median effective-rank ratio 0.83 over 79 modules, 3% below 0.5 +2026-09-08 14:06:51 UTC — gpt2 fisher: sweep complete in 48.9s +2026-09-08 14:06:51 UTC — gpt2: partition sensitivity sweep complete diff --git a/results/modules/sweep_qwen.log b/results/modules/sweep_qwen.log new file mode 100644 index 0000000000000000000000000000000000000000..49e0e4b5bc3976fbb94aa785cdad4f542483c4c2 --- /dev/null +++ b/results/modules/sweep_qwen.log @@ -0,0 +1,20 @@ +2026-09-08 14:06:55 UTC — qwen: partition sensitivity sweep, metrics=['l2', 'fisher'] +2026-09-08 14:07:12 UTC — qwen l2: affinity=square knn=0 C=6 -> best core energy share 1.0007 (3 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.83 over 2 modules, 0% below 0.5 +2026-09-08 14:07:25 UTC — qwen l2: affinity=square knn=10 C=12 -> best core energy share 0.9958 (4 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.89 over 13 modules, 0% below 0.5 +2026-09-08 14:07:38 UTC — qwen l2: affinity=square knn=30 C=12 -> best core energy share 0.9954 (5 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.88 over 14 modules, 0% below 0.5 +2026-09-08 14:07:54 UTC — qwen l2: affinity=abs knn=0 C=2 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0) +2026-09-08 14:08:07 UTC — qwen l2: affinity=abs knn=10 C=3 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0) +2026-09-08 14:08:23 UTC — qwen l2: affinity=abs knn=30 C=2 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0) +2026-09-08 14:08:36 UTC — qwen l2: affinity=square knn=0 C=16 -> best core energy share 0.9966 (3 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.94 over 20 modules, 0% below 0.5 +2026-09-08 14:08:51 UTC — qwen l2: affinity=square knn=0 C=64 -> best core energy share 0.9707 (23 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.95 over 153 modules, 1% below 0.5 +2026-09-08 14:08:51 UTC — qwen l2: sweep complete in 116.3s +2026-09-08 14:09:31 UTC — qwen fisher: affinity=square knn=0 C=2 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0) +2026-09-08 14:09:59 UTC — qwen fisher: affinity=square knn=10 C=2 -> best core energy share 1.0159 (32 modules at threshold 1.0, cap 1.0) +2026-09-08 14:10:22 UTC — qwen fisher: affinity=square knn=30 C=2 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0) +2026-09-08 14:10:38 UTC — qwen fisher: affinity=abs knn=0 C=2 -> best core energy share 1.0144 (16 modules at threshold 0.9, cap 1.0) +2026-09-08 14:10:54 UTC — qwen fisher: affinity=abs knn=10 C=2 -> best core energy share 1.0293 (78 modules at threshold 1.0, cap 0.5) +2026-09-08 14:11:05 UTC — qwen fisher: affinity=abs knn=30 C=6 -> best core energy share 0.9925 (2 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.98 over 23 modules, 4% below 0.5 +2026-09-08 14:11:17 UTC — qwen fisher: affinity=square knn=0 C=16 -> best core energy share 0.9528 (18 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.93 over 84 modules, 2% below 0.5 +2026-09-08 14:11:30 UTC — qwen fisher: affinity=square knn=0 C=64 -> best core energy share 0.6211 (113 modules at threshold 0.6, cap 0.5); at threshold 0.8 median effective-rank ratio 0.87 over 92 modules, 8% below 0.5 +2026-09-08 14:11:30 UTC — qwen fisher: sweep complete in 159.4s +2026-09-08 14:11:30 UTC — qwen: partition sensitivity sweep complete diff --git a/results/modules/sweep_random_gpt2.log b/results/modules/sweep_random_gpt2.log new file mode 100644 index 0000000000000000000000000000000000000000..3b17f9411ab60e442556a04ab703095c55e5fef8 --- /dev/null +++ b/results/modules/sweep_random_gpt2.log @@ -0,0 +1,20 @@ +2026-09-08 14:11:34 UTC — random_gpt2: partition sensitivity sweep, metrics=['l2', 'fisher'] +2026-09-08 14:11:41 UTC — random_gpt2 l2: affinity=square knn=0 C=3 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.83 over 3 modules, 0% below 0.5 +2026-09-08 14:11:45 UTC — random_gpt2 l2: affinity=square knn=10 C=3 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.91 over 7 modules, 0% below 0.5 +2026-09-08 14:11:50 UTC — random_gpt2 l2: affinity=square knn=30 C=3 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.79 over 6 modules, 0% below 0.5 +2026-09-08 14:11:55 UTC — random_gpt2 l2: affinity=abs knn=0 C=3 -> best core energy share 0.9989 (1 modules at threshold 0.6, cap 1.0) +2026-09-08 14:11:59 UTC — random_gpt2 l2: affinity=abs knn=10 C=3 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.96 over 6 modules, 0% below 0.5 +2026-09-08 14:12:03 UTC — random_gpt2 l2: affinity=abs knn=30 C=3 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.95 over 6 modules, 0% below 0.5 +2026-09-08 14:12:09 UTC — random_gpt2 l2: affinity=square knn=0 C=16 -> best core energy share 0.8646 (26 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.94 over 110 modules, 0% below 0.5 +2026-09-08 14:12:15 UTC — random_gpt2 l2: affinity=square knn=0 C=64 -> best core energy share 0.3387 (195 modules at threshold 0.6, cap 0.5); at threshold 0.8 median effective-rank ratio 0.94 over 58 modules, 0% below 0.5 +2026-09-08 14:12:15 UTC — random_gpt2 l2: sweep complete in 41.1s +2026-09-08 14:12:26 UTC — random_gpt2 fisher: affinity=square knn=0 C=2 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0) +2026-09-08 14:12:30 UTC — random_gpt2 fisher: affinity=square knn=10 C=3 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.94 over 25 modules, 0% below 0.5 +2026-09-08 14:12:34 UTC — random_gpt2 fisher: affinity=square knn=30 C=3 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.92 over 11 modules, 0% below 0.5 +2026-09-08 14:12:39 UTC — random_gpt2 fisher: affinity=abs knn=0 C=2 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0) +2026-09-08 14:12:43 UTC — random_gpt2 fisher: affinity=abs knn=10 C=3 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.97 over 29 modules, 0% below 0.5 +2026-09-08 14:12:47 UTC — random_gpt2 fisher: affinity=abs knn=30 C=3 -> best core energy share 1.0000 (1 modules at threshold 0.6, cap 1.0); at threshold 0.8 median effective-rank ratio 0.95 over 10 modules, 0% below 0.5 +2026-09-08 14:12:53 UTC — random_gpt2 fisher: affinity=square knn=0 C=16 -> best core energy share 0.6003 (82 modules at threshold 0.6, cap 0.5); at threshold 0.8 median effective-rank ratio 0.95 over 67 modules, 0% below 0.5 +2026-09-08 14:13:00 UTC — random_gpt2 fisher: affinity=square knn=0 C=64 -> best core energy share 0.1091 (69 modules at threshold 0.6, cap 0.5); at threshold 0.8 median effective-rank ratio 0.90 over 25 modules, 0% below 0.5 +2026-09-08 14:13:00 UTC — random_gpt2 fisher: sweep complete in 44.1s +2026-09-08 14:13:00 UTC — random_gpt2: partition sensitivity sweep complete diff --git a/results/state_fidelity.log b/results/state_fidelity.log new file mode 100644 index 0000000000000000000000000000000000000000..5a726ed69ab420ef2f79b47fc2b754351c3b8bf2 --- /dev/null +++ b/results/state_fidelity.log @@ -0,0 +1,14 @@ + Loading weights: 0%| | 0/338 [00:00