Add training splits, clustering and supplementary data

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+data/Data_availability/clustering/swissprot_cdhit_40.clstr filter=lfs diff=lfs merge=lfs -text
+data/Data_availability/stage1_pairs/train_set.json filter=lfs diff=lfs merge=lfs -text
+data/Data_availability/stage2_swissprot/train_set.json filter=lfs diff=lfs merge=lfs -text

data/Data_availability/README.md

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+# Data availability — ProtTale
+
+Training data, dataset splits and the clusterings that define them. Together
+with the code, these files reproduce ProtTale from scratch: Stage 1 alignment,
+Stage 2 generation, the reliability head, and the held-out benchmark.
+
+Figure and table source data are in the sibling `Supplementary_Data` folder.
+
+```
+Data_availability/
+  stage1_pairs/        Stage 1, functionally similar protein pairs
+  stage2_swissprot/    Stage 2, proteins paired with curated function text
+  clustering/          the clustering that defines every split
+```
+
+All `.json` files are **JSON Lines**: one JSON record per line, not a single
+JSON array. Read them line by line.
+
+---
+
+## stage1_pairs/ — pairwise functional alignment
+
+Protein pairs labelled by MIPS clustering, used to train the Q-Former and shift
+the sequence pathway into the text-derived functional space.
+
+| File | Records |
+|---|---|
+| `train_set.json` | 119,546 |
+| `valid_set.json` | 388 |
+| `test_set.json` | 534 |
+
+Each record is a 5-element list:
+
+| Position | Content |
+|---|---|
+| 0 | Amino acid sequence of protein A |
+| 1 | Amino acid sequence of protein B |
+| 2 | Curated Swiss-Prot `FUNCTION` description of protein A |
+| 3 | Curated Swiss-Prot `FUNCTION` description of protein B |
+| 4 | MIPS label as a string, `"1"` if both proteins fall in the same MIPS cluster (functionally similar), `"0"` otherwise |
+
+Positives and negatives are balanced by construction in every split: 267 / 267
+in the test set, 194 / 194 in the validation set, and equal counts in training.
+
+Pairs were drawn independently within each split, so no pair crosses a split
+boundary. The pairing is derived without curation; the two descriptions
+attached to each pair are curated Swiss-Prot text.
+
+The 534 test pairs are also the pairs used to validate the LLM-as-judge. That
+analysis, with UniProt identifiers and judge labels attached, is
+`Supplementary_Data_3_stage1_test_pairs.csv`.
+
+---
+
+## stage2_swissprot/ — sequence to function text
+
+Swiss-Prot proteins paired with their curated `FUNCTION` descriptions. The source
+was the full Swiss-Prot database, 574,627 entries, of which 473,996 carry a
+curated `FUNCTION` description.
+
+| File | Records | Role |
+|---|---|---|
+| `train_set.json` | 207,197 | Trains the generator |
+| `valid_set.json` | 1,000 | Tunes the generator; its judge labels train the reliability head |
+| `test_set.json` | 1,000 | Tunes the reliability head |
+| `unseen_set.json` | 1,031 | Held-out benchmark, all final reporting |
+
+Each record is a 6-element list:
+
+| Position | Content |
+|---|---|
+| 0 | Amino acid sequence |
+| 1 | Curated Swiss-Prot `FUNCTION` description, the generation target |
+| 2 | LLM-as-judge label for ProtTale's own prediction, scored `1` = Yes, `0` = No, `0.5` = Maybe, `-1` = Vague. This is the reliability head's training target. Set to `-1` throughout `unseen_set.json`, where it is a placeholder rather than a label |
+| 3 | Experimentally validated Molecular Function GO terms |
+| 4 | Enzyme Commission numbers |
+| 5 | Placeholder, not used |
+
+In `valid_set.json` the judge labels split 328 Yes, 510 No, 90 Maybe and 72
+Vague, which is the class balance the reliability head is fitted against.
+
+Validation and test proteins are the CD-HIT representatives of their 40%
+identity clusters, one per cluster.
+
+Every protein in `unseen_set.json` is a post-2022 Swiss-Prot deposition and
+comes from a different 40% identity cluster, one protein per cluster, none of
+whose members appears in training, validation or testing. Because the protein
+was chosen by deposition date and annotation rather than by length, it is not
+always the cluster's CD-HIT representative (683 of the 1,031 are). Each carries
+at least one experimentally validated MF GO term and at least one EC number, and
+does not appear in the released training data of any baseline method compared
+in the paper. The EC requirement means the benchmark consists entirely of
+enzymes.
+
+---
+
+## clustering/ — how the splits were defined
+
+| File | Content |
+|---|---|
+| `swissprot_cdhit_40.clstr` | CD-HIT output at 40% sequence identity over Swiss-Prot, 93,610 clusters covering 573,398 sequences |
+
+**CD-HIT clusters define the splits.** Train, validation, test and unseen are
+partitioned at the level of these clusters, so no protein in one split shares a
+40% identity cluster with a protein in another, and each unseen protein comes
+from a cluster none of whose members the model saw.
+The split files themselves carry sequences rather than cluster assignments, so
+this file is what makes that separation checkable: map the sequences of any two
+splits back to their clusters and confirm the clusters do not intersect.
+
+**MIPS clusters define the Stage 1 labels.** They were built by Louvain
+community detection over a 5.5 billion edge similarity network spanning 5.2
+million UniRef90 representatives, yielding about 1.2 million communities. A
+Swiss-Prot protein inherits the MIPS cluster of its UniRef90 representative;
+proteins longer than 1,200 residues, and those whose UniRef90 cluster has fewer
+than 10 members, have no MIPS cluster and contribute no Stage 1 pairs. The
+resulting same-cluster / different-cluster label is recorded per pair in
+`stage1_pairs/`, position 4 of each record.
+
+---
+
+## Reproducing the paper's counts
+
+The record counts above are the dataset sizes reported in the main text:
+119,546 / 388 / 534 Stage 1 pairs and 207,197 / 1,000 / 1,000 / 1,031 Stage 2
+proteins.

data/Data_availability/clustering/swissprot_cdhit_40.clstr

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data/Data_availability/stage1_pairs/train_set.json

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data/Data_availability/stage2_swissprot/train_set.json

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data/Supplementary_Data/README.md

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+# Supplementary Data — ProtTale
+
+Source data for the figures and tables in the main text and Extended Data:
+five CSV files (UTF-8, comma-separated, first row is the header), one Excel
+workbook and one JSON metadata file.
+
+The reliability score is written `p_JAY` throughout, the predicted probability
+of a Judge-Assigned *Yes*, as in the paper.
+
+Protein identifiers appear in two forms throughout: `UniProt_ID` is the
+Swiss-Prot entry name (e.g. `ENG1_RHIMI`) and `UniProt_accession` is the
+Swiss-Prot accession (e.g. `A0A023I7E1`). Entry names are unique across
+Swiss-Prot, so the two map one to one.
+
+| File | Rows | Covers |
+|---|---|---|
+| `Supplementary_Data_1_expert_curation.csv` | 232 | Blinded expert curation of ProtTale predictions |
+| `Supplementary_Data_2_unseen_per_method.csv` | 6,186 | Unseen evaluation set, per protein, per method, per judge/extraction run |
+| `Supplementary_Data_3_stage1_test_pairs.csv` | 534 | Stage 1 test pairs used to validate the LLM-as-judge |
+| `Supplementary_Data_4_shuffled_baseline.csv` | 1,031 | Reliability scores for real versus residue-shuffled sequences |
+| `Supplementary_Data_5_mips_validation.xlsx` | 1,000 | Sequence, structure and EC agreement within MIPS clusters |
+| `Supplementary_Data_6_training_curves.csv` | 8,904 | Training and validation curves for both stages and the reliability head |
+
+---
+
+## Supplementary Data 1 — expert curation
+
+One row per curated prediction, 232 in total, in the order the predictions were
+shown to the curator. Sampling, blinding and the rating scale are described in
+Methods, "Expert Curation".
+
+| Column | Meaning |
+|---|---|
+| `Entry` | Identifier shown to the curator, 1–232 |
+| `UniProt_ID` | Swiss-Prot entry name |
+| `Length_aa` | Sequence length in residues |
+| `p_JAY` | Reliability score from ProtTale's head |
+| `Retained` | Whether the prediction passes the discrete filter `p_JAY > 0.537` |
+| `Judge_label` | LLM-as-judge verdict, `Yes` or `not-Yes`, majority over five runs |
+| `Curator_score` | Expert rating, 1 (unrelated or contradictory) to 5 (equivalent) |
+| `Specificity` | Prediction more / equally / less specific than the Swiss-Prot description |
+| `Information` | Prediction more / equally / less informative than the Swiss-Prot description |
+| `Truncated` | Whether the prediction does not end in terminal punctuation, used as a proxy for generation having reached the 128-token cap. On the unseen set, 93% of predictions flagged this way did reach the cap |
+| `SwissProt_description` | Reference text shown to the curator |
+| `ProtTale_prediction` | Generated text shown to the curator |
+| `Curator_notes` | Free-text justification, present for 175 of the 232 items |
+
+Neither `p_JAY` nor `Judge_label` was visible to the curator at scoring time.
+Both are included so that human and automated ratings can be compared directly.
+
+Regenerates the expert-curation results in the main text and the Extended Data
+judge-versus-curator agreement table.
+
+---
+
+## Supplementary Data 2 — unseen evaluation set, per method
+
+One row per (protein, method), 1,031 proteins x 6 methods = 6,186 rows. The
+sixth "method" is `Reference`, the curated Swiss-Prot description put through
+the same GO-extraction pipeline; it is the curated-text reference line in the
+GO-extraction figure and carries no judge labels.
+
+| Column | Meaning |
+|---|---|
+| `UniProt_ID`, `UniProt_accession` | Protein identifiers |
+| `Method` | `ESM2Text`, `Prot2Text`, `ProtT3`, `ProtTale`, `Protein2Text`, or `Reference` |
+| `Kingdom` | Taxonomic group, `Eukaryota` / `Bacteria` / `Archaea` / `Viruses` |
+| `Length_aa` | Sequence length. The short subset is `<= 1024` (n = 852), the long subset `> 1024` (n = 179) |
+| `judge_run1` … `judge_run5` | LLM-as-judge label from each of the five independent runs: `Yes`, `No`, `Maybe`, or `Vague`. Blank for `Reference` |
+| `judge_majority` | `Yes` when at least three of the five runs said `Yes`, otherwise `not-Yes`. This is the label used for every statistical comparison in the paper |
+| `reference_GO` | The protein's curated, experimentally validated Molecular Function GO terms, semicolon separated. The target every extraction is scored against. Identical on all six rows of a protein |
+| `extracted_GO_run1` … `extracted_GO_run5` | GO terms the extraction model returned from this method's text in each run, semicolon separated. **A blank cell is an empty extraction** |
+| `wang_run1` … `wang_run5` | Wang semantic similarity between `extracted_GO_run*` and `reference_GO`, without ancestor expansion, as plotted in the main text. Blank where the extraction was empty |
+| `n_nonempty_runs` | How many of the five runs returned at least one GO term, 0–5 |
+| `wang_mean_nonempty` | Mean of the non-blank `wang_run*` values for this protein and method. Blank when all five runs were empty |
+| `p_JAY` | ProtTale's reliability score for this protein. A property of the protein, so the same value repeats on all six rows of that protein |
+| `Retained` | Whether `p_JAY > 0.537` |
+
+Because the extracted terms themselves are included, the metrics reported in
+Extended Data can be recomputed from this file as well: Wang similarity with
+ancestor expansion, Jaccard similarity, and information accretion all take the
+same two term sets as input.
+
+### Two aggregation conventions
+
+The paper aggregates Wang similarity in two different ways, both derivable from
+the per-run columns above. They are not interchangeable and the difference
+matters when reproducing specific numbers.
+
+- **Figures.** Within each run, average over the non-empty rows only, then
+  average those five per-run means. Empty extractions are excluded from the
+  average and reported separately as the empty rate printed above each bar.
+- **Wilcoxon tests.** Empty extractions are scored zero and kept, so that all
+  1,031 proteins remain paired across methods. Dropping rows per method would
+  break the pairing the test requires.
+
+Reproduced under the figure convention, the per-method Wang means are
+Reference 0.669, ESM2Text 0.569, Prot2Text 0.556, ProtT3 0.605, ProtTale 0.561,
+Protein2Text 0.443, with empty rates of 4.79%, 31.81%, 22.00%, 10.03%, 14.28%
+and 28.07% respectively.
+
+Regenerates the GO-extraction figure, the method-comparison figure, the
+reliability-filter figure, the per-taxonomic-group figure, the five-way Venn,
+and the judge run-agreement panel.
+
+---
+
+## Supplementary Data 3 — Stage 1 test pairs
+
+One row per pair, 534 in total. These pairs validate the LLM-as-judge against
+an independent ground truth: the MIPS cluster label comes from embedding
+clustering, the judge label from reading the two curated descriptions. Neither
+is derived from the other, which is what makes their agreement informative.
+
+| Column | Meaning |
+|---|---|
+| `Pair_index` | Row index, 0–533 |
+| `UniProt_ID_1`, `UniProt_accession_1` | First protein of the pair |
+| `UniProt_ID_2`, `UniProt_accession_2` | Second protein of the pair |
+| `MIPS_pair` | `within` if both proteins fall in the same MIPS cluster, `across` otherwise. 267 of each |
+| `Judge_label` | LLM-as-judge label for the two descriptions: `Yes`, `No`, `Maybe`, or `Vague` |
+| `Description_1`, `Description_2` | The two curated Swiss-Prot function descriptions given to the judge. No model prediction is involved |
+
+Identifiers for these pairs were recovered by exact sequence match against
+Swiss-Prot, since the stored pairs carry sequences rather than accessions. 15
+of the 1,068 proteins have a sequence that is shared by more than one
+Swiss-Prot entry; for those the identifier given here is one of several entries
+with that exact sequence. The descriptions, labels and analysis are unaffected.
+
+**The judge was run once on these pairs**, not five times. This analysis tests
+the judge's accuracy against the cluster ground truth, not its run-to-run
+reproducibility; the five-run reproducibility analysis was carried out on the
+1,031 unseen predictions and lives in Supplementary Data 2.
+
+Regenerates the within-versus-across cluster panel of the Extended Data judge
+figure and the accompanying Fisher exact test.
+
+---
+
+## Supplementary Data 4 — shuffled-sequence baseline
+
+One row per unseen protein, 1,031 in total. The discrete reliability threshold
+used throughout the paper is derived from this file.
+
+| Column | Meaning |
+|---|---|
+| `UniProt_ID`, `UniProt_accession` | Protein identifiers |
+| `Length_aa` | Sequence length |
+| `p_JAY_real` | Reliability score for the real sequence |
+| `p_JAY_shuffled` | Reliability score for the same sequence with its residues randomly permuted |
+
+A Fisher exact sweep over thresholds on `p_JAY`, contrasting the real and
+shuffled distributions, peaks at `tau* = 0.537`. That value is adopted as the
+discrete reliability filter everywhere in the paper, so this file is what makes
+the threshold checkable.
+
+Regenerates both panels of the Extended Data shuffled-baseline figure.
+
+---
+
+## Supplementary Data 5 — MIPS cluster validation
+
+Excel workbook, three sheets. Backs the opening result that embedding proximity
+is a usable proxy for shared function.
+
+- **README** — sampling, metric definitions, and how each confidence interval was computed.
+- **Per-pair data** — 1,000 rows, one per protein pair: `cluster_id`, the two
+  UniProt accessions, `seq_identity_pct`, `tm_score`, `tm_score_ge_0.7`, the two
+  EC numbers, `ec_match_level` (0–4) and `ec_match_level4`.
+- **Summary statistics** — the three reported figures with their intervals. The
+  TM-score and EC proportions recompute live from the per-pair sheet.
+
+Pairs are same-cluster pairs drawn from reviewed UniProtKB entries that carry an
+EC number and an AlphaFold structure, capped at five pairs per cluster, covering
+244 of the 2,204 eligible clusters.
+
+**Note on the sequence-identity figure.** 37.5% is the **mean** pairwise
+sequence identity over the 1,000 pairs, with a bootstrap confidence interval on
+that mean. It is not a proportion, and no "sequence similar" threshold is used
+or implied. The other two figures, 58.8% and 63.1%, are proportions with Wilson
+intervals.
+
+---
+
+## Supplementary Data 6 — training curves
+
+`Supplementary_Data_6_training_curves.csv`, 8,904 rows, long format: `panel`,
+`series`, `step`, `epoch`, `x_epoch`, `value`.
+`Supplementary_Data_6_training_curves_meta.json` records, for each panel, the
+source metrics file, which logged columns were used, the number of epochs,
+steps per epoch, and the best validation value with its epoch.
+
+| Panel | Stage | Training series | Validation series | Epochs |
+|---|---|---|---|---|
+| A | Stage 1 alignment | `train_loss_step`, `train_loss_epoch` | `val_auc`, best 0.866 at epoch 4 | 10 |
+| B | Stage 2 generation | `train_loss_step` | `val_bleu2`, best 17.51 at epoch 39 | 50 |
+| C | Reliability head | `train_loss_step` | `val_pos_f1`, best 0.547 at epoch 12 | 20 |
+
+Panel C's random baselines, 0.384 at q = 0.5 and 0.476 at q = 1.0, follow from a
+positive prevalence of 312 in 1,000 on the head's validation split.
+
+Regenerates the Extended Data training-dynamics figure.
+
+---
+
+## Not covered here
+
+Two display items have no tabular source data: the model architecture figure is
+a schematic, and the two prompt figures reproduce the prompts themselves.
+
+Training data, dataset splits and clustering files are in the sibling
+`Data_availability` folder.

data/Supplementary_Data/Supplementary_Data_4_shuffled_baseline.csv

@@ -0,0 +1,1032 @@
+UniProt_ID,UniProt_accession,Length_aa,p_JAY_real,p_JAY_shuffled
+ENG1_RHIMI,A0A023I7E1,796,0.3762,0.4063
+ARSI_BACX0,A0A059WI14,161,0.2874,0.3355
+PGMA1_PHLG1,A0A0C3RR82,556,0.4122,0.3753
+DAO_UNKP,A0A0E3JXD9,357,0.4036,0.2155
+TACT3_SALT1,A0A0F6B5X4,175,0.3247,0.3397
+NAMPT_XANC8,A0A0H2X5R2,468,0.3296,0.4019
+HMPK_ACIB6,A0A0J9X285,255,0.3517,0.2855
+AMDH_STRVG,A0A0L8M630,360,0.1925,0.3885
+GQQA_KOMEU,A0A0M0ELU2,281,0.1549,0.4659
+UBP48_DANRE,A0A0R4IB93,1047,0.6648,0.5205
+MCR2_ECOLX,A0A1C3NEV1,538,0.3497,0.2767
+PIGK_MONRU,A0A1C8AX29,2014,0.1585,0.4536
+PIGJ_MONRU,A0A1D5AG16,1842,0.2132,0.3979
+VPS34_CANAL,A0A1D8PDV7,1020,0.5434,0.3341
+CYT1_CANAL,A0A1D8PHA3,288,0.4606,0.3651
+AIF1_CANAL,A0A1D8PI22,367,0.4352,0.2477
+PMA1_CANAL,A0A1D8PJ01,895,0.5156,0.3596
+TOS1_CANAL,A0A1D8PJA8,467,0.2486,0.3511
+PHYA_CANAL,A0A1D8PS71,461,0.2057,0.2153
+AAL_LOKAT,A0A1M4XLJ3,548,0.4851,0.4113
+CBPB1_AEDAE,A0A1S4F020,412,0.3381,0.3595
+TS4FN_CANSA,A0A1V0QSG6,567,0.1808,0.4466
+IUTB_VIBVL,A0A1W7HCY1,244,0.3656,0.3524
+VINHY_RAUSE,A0A218NGS0,480,0.3899,0.3487
+RUFO_STRAR,A0A224AU14,394,0.1225,0.3823
+TYR1_HAHS4,A0A261GRE4,466,0.3228,0.3756
+HYLA_CUTAC,A0A2B7ICX0,826,0.2184,0.3004
+C1GLT_BIOGL,A0A2C9JXL4,388,0.3388,0.3412
+CFOL_ASPCN,A0A2I2F2I5,694,0.2617,0.3947
+PRAF_MARPO,A0A2R6XIK6,1680,0.7325,0.2444
+MARTX_VIBVL,A0A2S3R7M0,5206,0.3125,0.4614
+AMPP1_LOTJA,A0A2Z5GDY5,658,0.3767,0.4307
+CRH1_BOTFB,A0A384J6C4,391,0.3365,0.3105
+CRH3_BOTFB,A0A384K116,444,0.3335,0.2852
+FUMC_SCHMA,A0A3Q0KQY7,488,0.2159,0.3821
+NPRS_SCHMA,A0A3Q0KR05,1227,0.2906,0.3713
+OXDA_RASEM,A0A499UB99,368,0.2575,0.4241
+AAAD_PSICU,A0A4P1LYB6,1013,0.4265,0.312
+EIK2_PLABA,A0A509AH51,2724,0.6946,0.1874
+PK4_PLABA,A0A509AMC3,2591,0.6779,0.5271
+PMP1_PARBF,A0A5P3XKQ1,1260,0.3245,0.3255
+BLC14_ECOLX,A0A649V088,291,0.2286,0.3895
+FAE1_PENPR,A0A6C0PI29,280,0.2713,0.2607
+SDSL_RAT,A0A6N3IN21,329,0.6557,0.3666
+OXDD_OCTVU,A0A7E6FSU6,336,0.2611,0.3031
+PGK1_ASPFN,A0A7G5KET3,417,0.3728,0.3447
+GCNE_ASPFN,A0A7U2QYM2,402,0.5158,0.4239
+ESDPD_PENSH,A0A8D5M3Y5,383,0.3012,0.3065
+CUBA_PSICU,A0A8H8CMW1,343,0.1666,0.3167
+SHIP2_CANLF,A0A8I3NFE2,1264,0.4762,0.265
+OXSR1_RAT,A0A8I5ZNK2,528,0.5623,0.328
+KDM1A_RAT,A0A8I6G2K8,852,0.2623,0.3736
+HDAC6_RAT,A0A8I6GM68,1183,0.4054,0.2876
+AL7A1_DANRE,A0A8M1PLI9,540,0.1852,0.3464
+KSAMS_ACAKO,A0A9E8G339,393,0.5063,0.3145
+HPAAL_RHIWR,A0A9J9HGY6,251,0.3723,0.3961
+MRP4_BOVIN,A0AAA9T569,1315,0.5516,0.5282
+E2609_XANAC,A0AAI7ZGC7,431,0.2612,0.4285
+ASP5_TOXGO,A0MQA3,969,0.3586,0.4267
+END8B_MYCS2,A0QT90,252,0.489,0.3654
+LHR_MYCS2,A0QT91,1507,0.4814,0.382
+GUAB1_MYCS2,A0QYE8,478,0.166,0.3356
+MIRO1_RAT,A1L1L6,631,0.587,0.3316
+PTPM1_DANRE,A1L293,183,0.4635,0.2953
+TFP_LEPSV,A1XLE2,337,0.4386,0.2499
+TGFR1_DROME,A1Z7L8,622,0.5817,0.4909
+DCR2_DROME,A1ZAW0,1722,0.6256,0.3625
+DUOX1_MOUSE,A2AQ92,1551,0.5121,0.4038
+AGLTA_ASPNC,A2QTS4,555,0.1926,0.3347
+AGLTB_ASPNC,A2QYT9,549,0.2245,0.3182
+L_RVFV,A2SZS3,2092,0.6708,0.3881
+PGMI_PYRCJ,A3MTR6,299,0.4675,0.375
+DMDC_ROSNI,A3SI50,591,0.2788,0.2737
+CPSKS_PHYPA,A5A8G0,881,0.1576,0.319
+E2AK3_BOVIN,A5D791,1115,0.6877,0.2616
+RPB2_BOVIN,A5PJW8,1174,0.5664,0.3933
+DING_MYCTA,A5U229,664,0.5655,0.4403
+CHIP_RAT,A6HD62,304,0.8626,0.3707
+PRIA_KLEP7,A6TGC5,731,0.7157,0.3811
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data/Supplementary_Data/Supplementary_Data_6_training_curves_meta.json

@@ -0,0 +1,67 @@
+{
+  "description": "Source data for the ProtTale three-panel training-curve figure.",
+  "columns": {
+    "panel": "A | B | C",
+    "series": "name of the plotted curve",
+    "step": "optimizer step as logged",
+    "epoch": "0-indexed epoch as logged",
+    "x_epoch": "x coordinate used in the figure (epochs; per-epoch series are epoch+1)",
+    "value": "plotted y value"
+  },
+  "panels": {
+    "A": {
+      "source_metrics_csv": "all_checkpoints/stage1_ckpt_run1/lightning_logs/version_0/metrics.csv",
+      "columns_used": {
+        "train_loss_step": "train/loss_total_step",
+        "train_loss_epoch": "train/loss_total_epoch",
+        "val_auc": "val/seqseq_AUC_VAL"
+      },
+      "n_epochs": 10,
+      "max_step": 37399,
+      "steps_per_epoch": 3739.9,
+      "best": {
+        "series": "val_auc",
+        "value": 0.8664242029190063,
+        "epoch": 4
+      }
+    },
+    "B": {
+      "source_metrics_csv": "all_checkpoints/ProtTale/lightning_logs/version_0/metrics.csv",
+      "columns_used": {
+        "train_loss_step": "loss",
+        "val_bleu2": "val/bleu2"
+      },
+      "n_epochs": 50,
+      "max_step": 400799,
+      "steps_per_epoch": 8015.98,
+      "best": {
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+        "value": 17.513992309570312,
+        "epoch": 39
+      }
+    },
+    "C": {
+      "source_metrics_csv": "all_checkpoints/ProtTale_binary_reliability_ft/lightning_logs/version_0/metrics.csv",
+      "columns_used": {
+        "train_loss_step": "reliability_loss",
+        "val_pos_f1": "val/reliability_pos_f1"
+      },
+      "n_epochs": 20,
+      "max_step": 2499,
+      "steps_per_epoch": 124.95,
+      "positive_prevalence": 0.312,
+      "positive_count": 312,
+      "total_count": 1000,
+      "random_baselines": {
+        "q=0.5": 0.3842364532019704,
+        "q=1.0": 0.47560975609756095
+      },
+      "best": {
+        "series": "val_pos_f1",
+        "value": 0.5469613075256348,
+        "epoch": 12
+      }
+    }
+  },
+  "reliability_valid_json": "data/SwissProtV3/test_set.json"
+}