ej 0.0.1: model.ejpack, model card, NOTICE, licences, SHA256SUMS

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model.ejpack filter=lfs diff=lfs merge=lfs -text

LICENSES/CC-BY-SA-4.0.txt

@@ -0,0 +1,428 @@
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LICENSES/MIT-e5-small-v2.txt

@@ -0,0 +1,27 @@
+The base model intfloat/e5-small-v2 (https://huggingface.co/intfloat/e5-small-v2, revision
+ffb93f3bd4047442299a41ebb6fa998a38507c52) is licensed under the MIT License (declared as "license: mit" in its model card metadata; the
+repository ships no LICENSE file). The modified encoder weights in
+encoder/w23.safetensors (with encoder/w23.json) are derived from it.
+
+MIT License
+
+Copyright (c) the authors of intfloat/e5-small-v2 (Wang et al., 2022, 'Text Embeddings by Weakly-Supervised
+Contrastive Pre-training')
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
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+
+The above copyright notice and this permission notice shall be included in all
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+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

NOTICE

@@ -0,0 +1,62 @@
+ej
+Copyright 2026 Saket Bhushan.
+
+Code (this repository: ej/, benchmarks/, docs/, examples/, tests/, scripts/) is licensed under the Apache License, Version
+2.0 (see LICENSE). The model weights (distributed separately from this repository, at https://huggingface.co/5ak3t/ej)
+are licensed under CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/; full text in LICENSES/CC-BY-SA-4.0.txt).
+
+ej model weights 0.0.1
+----------------------
+This model is a modified version of intfloat/e5-small-v2 (MIT License; Wang et al., arXiv:2212.03533; revision
+ffb93f3bd4047442299a41ebb6fa998a38507c52): vocabulary trimmed, weights compressed and re-trained by
+distillation. MIT licence text: LICENSES/MIT-e5-small-v2.txt. The upstream repository declares "license: mit" in its model
+card metadata but ships no LICENSE file, so the copyright line there names the authors of intfloat/e5-small-v2.
+
+Training data. Every dataset below was converted to typed questions and re-sampled: modified.
+- Typed Decisions. Creator: the publishers of the Hugging Face dataset LocalLLaMA/typed-decisions.
+  Link: https://huggingface.co/datasets/LocalLLaMA/typed-decisions
+  Licence: Apache-2.0, https://www.apache.org/licenses/LICENSE-2.0. Modified: yes (converted; gold teacher distributions kept).
+- Banking77. Creator: PolyAI (Casanueva et al., 2020, "Efficient Intent Detection with Dual Sentence Encoders").
+  Link: https://huggingface.co/datasets/PolyAI/banking77
+  Licence: CC BY 4.0, https://creativecommons.org/licenses/by/4.0/. Modified: yes (choice questions, leak-free distractors).
+- CLINC150 (config "plus"). Creator: Clinc (Larson et al., 2019, "An Evaluation Dataset for Intent Classification and
+  Out-of-Scope Prediction"). Link: https://huggingface.co/datasets/clinc/clinc_oos
+  Licence: CC BY 3.0, https://creativecommons.org/licenses/by/3.0/. Modified: yes (choice questions, leak-free distractors).
+- GoEmotions (simplified). Creator: Google Research (Demszky et al., 2020, "GoEmotions: A Dataset of Fine-Grained
+  Emotions"). Link: https://huggingface.co/datasets/google-research-datasets/go_emotions
+  Licence: Apache-2.0, https://www.apache.org/licenses/LICENSE-2.0. Modified: yes (single-label rows, balanced, choice
+  questions).
+- In-house support-ticket corpus written with Claude Haiku (Anthropic, claude-haiku-4-5-20251001); not released.
+Distillation signals (fit time only; no teacher weights are included): a decision encoder fine-tuned on the same pool,
+and the NLI cross-encoder cross-encoder/nli-deberta-v3-xsmall (Apache-2.0; Creator: the sentence-transformers project,
+Hugging Face organisation "cross-encoder"; Link: https://huggingface.co/cross-encoder/nli-deberta-v3-xsmall; trained on
+SNLI (CC BY-SA 4.0) and MultiNLI), whose outputs on pool texts are distilled into the model.
+Not used: Amazon counterfactual (amazon-research/amazon-multilingual-counterfactual-dataset), whose upstream licence is
+CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/), although the mteb/amazon_counterfactual mirror declares CC BY 4.0.
+
+Evaluation data (benchmark suites; not training data)
+-----------------------------------------------------
+MASSIVE (Amazon, FitzGerald et al., 2022; https://huggingface.co/datasets/AmazonScience/massive; CC BY 4.0,
+https://creativecommons.org/licenses/by/4.0/; modified: leak-free option sets). Typed Decisions test split (as above).
+zs_wide: Super-NaturalInstructions permissive tasks, Schema-Guided Dialogue (CC BY-SA 4.0), ABCD (MIT) and GLM-synthetic
+workflows generated with glm-4.7; suite files are not distributed.
+
+Open licence questions (disclosed, not resolved; the owners decide)
+-------------------------------------------------------------------
+Q1  Claude Haiku outputs (780 training records): whether the provider terms that applied when the tickets were generated
+    allow training openly released weights on them, and what licence the corpus itself carries, are not recorded.
+Q2  Typed Decisions gold distributions: which model produced the dataset's teacher distributions used as gold
+    probabilities, and whether its output terms impose conditions, is not recorded.
+Q3  MultiNLI licence split (which genres are CC BY-SA 3.0 and which OANC terms) and whether distillation counts as an
+    adaptation; relevant to 0.0.1 (part of it is distilled from a cross-encoder trained on MultiNLI).
+Q4  GoEmotions source texts are Reddit comments: the dataset is Apache-2.0, but the platform terms for the comment texts
+    were not reviewed.
+
+Adaptations of the weights must be shared under CC BY-SA 4.0 or a compatible licence, with attribution.
+
+Benchmark adapters
+------------------
+benchmarks/rivals/ calls third-party models through their own packages, servers or APIs; no third-party source code is
+copied into this repository. Those models keep their own licences and terms (benchmarks/METHOD.md).
+
+"Jev" and "System One" are names used by TypeSafe.ai; ej is not affiliated with or endorsed by TypeSafe.ai.

README.md

@@ -0,0 +1,195 @@
+---
+license: cc-by-sa-4.0
+base_model: intfloat/e5-small-v2
+library_name: ej
+language:
+- en
+pipeline_tag: text-classification
+tags:
+- text-classification
+- zero-shot-classification
+- calibration
+- typed-decisions
+---
+
+# ej model card (0.0.1)
+
+ej 0.0.1 is a small calibrated decision model; technical report forthcoming.
+
+> **Weights:** https://huggingface.co/5ak3t/ej, revision `v0.0.1`, one file `model.ejpack` (11,384,312 bytes, SHA-256
+> `e990e1846cba43f8405a969c606057f2fd6e2076595f4a34d202e8fc531891b0`). Code, training and evaluation:
+> https://github.com/5ak3t/ej (file paths below refer to that repository). Release notes: `docs/releases/0.0.1.md`.
+
+| Field | Value |
+|---|---|
+| Model | ej 0.0.1 (2026-10-08); the default fit seed, fixed before fitting, not a selected seed |
+| State key | `3b3e66d28fb423f98b734bd0c1d324cdc92bb8e10eda0e7ddf9a8e54de3f3fe2` (recorded in the pack header; reproduced by `scripts/state_key.py --git <model-code repo> f46cf7c <training pool> --ck-dir lowbit-b3b010513f948ceb`) |
+| Code | runtime: `ej/_runtime` of the GitHub repository (39 sha256-pinned modules; `ej.integrity.RELEASE_PROVENANCE`); fitted with model-code commit `f46cf7c` of the maintainers' development repository (not public) and encoder checkpoint `lowbit-b3b010513f948ceb`; the same recipe is `python -m ej.train` |
+| Size | counted 10.87 MiB (11.40 MB; a bit-level bound on the stored parameters); on disk 11.4 MB: one file `model.ejpack` of 11,384,312 bytes, the whole download (no base model is fetched); resident: peak live model tensors while predicting 128 MB by default, 35 MB with `low_memory=True`; process RSS during predict about 558 / 455 MB, of which about 204 MB is `import torch` (README "Memory and latency") |
+| Latency (CPU, Python reference, 1 thread, one record per call) | packed model, first 50 td development records, warm: 214 ms per record (317 ms with `low_memory=True`); cold 1,061 / 1,385 ms (2026-10-09, Intel Xeon @ 2.80GHz, 4 logical cores, contended). Benchmark protocol on the td development suite (weights-directory format, 2026-10-08): warm 356.6 ms mean / 363.1 ms median, cold 1,922.0 / 1,413.7 ms. The two measurements are not comparable (README "Latency") |
+| Language | English only (§6) |
+| Licence | weights **CC BY-SA 4.0**; code Apache-2.0 |
+| Base model | `intfloat/e5-small-v2` (MIT), revision `ffb93f3bd4047442299a41ebb6fa998a38507c52` |
+| Weights / code | https://huggingface.co/5ak3t/ej (revision `v0.0.1`, file `model.ejpack`) / https://github.com/5ak3t/ej |
+
+## 1. What the model does
+
+A **System-1 decision model for devices**: given a `state` (text, or a JSON object as text) and typed questions, it returns
+one probability distribution per question in **one pass**, without decoding tokens. Usage: Quickstart below and the
+repository README.
+
+| Question type | Input | Output |
+|---|---|---|
+| `choice` | instructions + a runtime list of option texts | a probability per option |
+| `noul` | a yes/no statement | P(false), P(true) |
+| `score` | instructions + an ordered list of level texts | a distribution over the levels |
+
+- **No LLM at inference**; teachers are used at fit time only and their weights do not ship.
+- **Calibration is fitted, then checked per suite**: whether the result is calibrated is measured on each suite against a
+  perfect-calibration ECE floor (§5).
+- **Record independence.** Zero-shot `predict` is record-independent: each record's output depends only on that record (up
+  to the float noise of batching). `adapt` (and `AdaptedModel.observe`) is an opt-in, per-workflow transductive mode: it
+  pools option statistics across that workflow's labelled records, so use it only for one workflow with a fixed option set
+  (the same question ids and option keys).
+- Option texts are read as text, so new label spaces can be asked zero-shot; quality on unseen workflows is limited (§6).
+
+## 2. Quickstart
+
+Architecture and method: technical report forthcoming.
+
+```bash
+git clone https://github.com/5ak3t/ej && cd ej
+pip install -e .
+```
+
+```python
+import ej
+
+model = ej.load('5ak3t/ej', revision='v0.0.1')   # downloads model.ejpack only, checks its sha256, nothing else is fetched
+(probs,) = model.predict([ej.EXAMPLE_RECORD])    # {qid: [p for each option, in option order]}
+```
+
+`ej.load` also takes a local `model.ejpack` (or a directory holding it). `python examples/quickstart.py --weights 5ak3t/ej
+--revision v0.0.1` prints the distributions for `ej.EXAMPLE_RECORD`. Input and output format: the repository README.
+
+## 3. Training data
+
+One fit on one training pool (9,719 records, 7 source groups; sha256
+`ce1c1a6fecfd62a90317f6efc4f90fd5f9261becb7081fd00235a6f4d5ee9dbe`). Details and counts: `docs/data-card.md`.
+
+| Source (groups) | Records | Licence |
+|---|---|---|
+| Typed Decisions, workflows `agent_trace_observability`, `customer_service`, `invoice_processing` (3) | 736 | Apache-2.0 |
+| **Support tickets written by Claude Haiku** for this project, train + calibration splits (1) | 780 | in-house; **not released** |
+| Banking77 / CLINC150 `plus` / GoEmotions (3) | 3,000 / 3,000 / 2,203 | CC BY 4.0 / CC BY 3.0 / Apache-2.0 |
+| **Total** | 9,719 records, 7 groups | |
+
+Amazon counterfactual is not used: its upstream licence is CC BY-NC 4.0 (one mirror declares CC BY 4.0).
+
+**Disclosure: Claude Haiku.** The in-domain support tickets were written by Claude Haiku (labels by construction from the
+generation spec, no human review). They are part of training; the corpus itself is **not released**, so the training pool
+cannot be rebuilt from public data alone. Open licence questions on this and other inputs: `NOTICE` (Q1-Q4).
+Not in the training data: Typed Decisions `security_incidents` (the `zs_td` suite), MASSIVE and the zs_wide workflows
+(evaluation only).
+
+## 4. Evaluation
+
+- **Suites** (`benchmarks/README.md`; never trained on):
+  - `td`: Typed Decisions test split, the 3 training workflows.
+  - `zs_td`: zs_td is ONE held-out workflow (security_incidents; dev 300 and final 100 records of the same workflow).
+    The model code was kept on its dev accuracy after about 55 logged comparisons on it, so final zs_td estimates accuracy on
+    further records of a workflow the model was selected on: it is neither unbiased nor unseen-workflow evidence. Its
+    fit-to-fit SD (same code, other RNG) is ~.022 accuracy per fit (SD of a difference .031, 4 pairs), twice its
+    record-cluster SE (.011); one workflow gives no between-workflow variance. Unseen-workflow evidence = zs_wide final
+    macro_real.
+  - `zs_massive`: MASSIVE (en) intents with leak-free option sets. MASSIVE was never trained on, but its label space is
+    not new: about 22% of its intent names (13 of 59 development option texts: 1 identical, 12 near) overlap CLINC150 /
+    Banking77 option texts in the pool, and 4 final records are exact duplicates of pool records (removed at the next
+    benchmark run).
+  - `zs_wide`: workflows never trained on: dev 154 workflows (111 from public sources: SNI tasks, SGD services, ABCD; 43
+    GLM-synthetic), final 147. The split is by task, not by dataset family: 53 of the 147 final workflows share a family
+    with a dev workflow; pool-source tasks were dropped from the final set.
+  - `tickets` (in-domain, private) and `tickets_ood` (held-out writing styles of the same generator: a style shift).
+- **Metrics**: per-suite mean NLL of the gold option (headline: the mean over td, zs_td, zs_massive, tickets), micro
+  accuracy (zs_wide: group-macro over the real sources), ECE over 15 bins with its perfect-calibration floor, certified
+  automation (risk .10, δ .10; **this suite only**). Each cell has a 95% record-cluster CI. Scoring: `ej.eval`
+  (`benchmarks/scoring.py` for rivals).
+- **Selection.** Development numbers are selected: more than 55 logged comparisons were made on the development suites, so
+  their numbers are optimistic. The final suites are read once per release; zs_td final is not unseen-workflow evidence
+  (above).
+- **Leak-free option sets**: for sampled option sets every option is equally likely to be the gold, so option frequency
+  reveals nothing (a frequency rule reached .631 micro accuracy against chance .240 on a non-leak-free MASSIVE suite).
+- **Rivals** and contamination flags: `benchmarks/METHOD.md`.
+
+## 5. Results
+
+**ej 0.0.1** (final suites, scored once, 2026-10-08: `benchmarks/results/README.md`). 95% record-cluster CIs; calibrated
+= observed ECE15 at or below the 95th percentile of a perfectly calibrated model's ECE15 on that suite.
+
+| Suite | NLL [95% CI] | Micro accuracy [95% CI] | ECE15 [95% CI] | Calibrated on this suite | Certified automation, this suite only (coverage / error) |
+|---|---|---|---|---|---|
+| td (seen workflows) | .663 [.613, .716] | .721 [.695, .746] | .071 [.050, .094] | no (floor q95 .034) | .300 / .056 |
+| zs_td (`security_incidents`, selected on) | 1.145 [1.113, 1.180] | .422 [.388, .452] | .094 [.068, .135] | no (.059) | 0 |
+| zs_massive (leak-free) | .481 [.430, .530] | .832 [.808, .857] | .051 [.041, .073] | no (.040) | .798 / .080 |
+| tickets | .629 [.583, .680] | .712 [.679, .746] | .033 [.028, .080] | yes (.061) | 0 |
+| tickets_ood | .715 [.650, .787] | .683 [.639, .726] | .050 [.039, .099] | yes (.072) | 0 |
+| zs_wide (147 unseen workflows; micro, all sources) | 1.196 [1.176, 1.220] | .422 [.405, .439] | .121 [.108, .137] | no (.027) | 0 |
+
+zs_wide final macro_real (group-macro accuracy over the real sources SNI, SGD and ABCD, 105 workflows; t interval over
+workflows within sources) **.419 [.379, .458]**: the unseen-workflow number. GLM-synthetic workflows separately: .358
+[.323, .394] (42 workflows). Mean NLL over td, zs_td, zs_massive and tickets: .7297. Seed variability of this code and
+data (6 fits, development suites): between-seed SD .0105 in that metric, .019 in zs_wide macro_real, .037 in zs_td micro
+accuracy; differences of that size between fits are not evidence of a change.
+Against the rivals' sealed runs (paired CIs in `benchmarks/results/README.md`), ej 0.0.1 has lower micro accuracy than the
+144M-0.8B rivals and the hosted Jev on zs_td and zs_massive (except Julia-1 on zs_massive) and than laya on td; no
+calibration, latency or size ranking is claimed.
+
+## 6. Limitations
+
+- **Unseen workflows: low accuracy.** Expect weak decisions on a new workflow until you measure it on labelled cases of
+  your own. A few labelled records help mainly by teaching the label prior (`docs/adaptation.md`).
+- **Fit-to-fit noise.** Two fits of the same code that differ only in their random seed differ by about .03 zs_td micro
+  accuracy (SD of a difference); single-fit differences of that size are not evidence (§4).
+- **Option-text tilt**: on new workflows the option texts alone still tilt the predictions.
+- **More data did not simply help**: adding broad text and dialogue data made td, tickets and zs_massive worse (zs_td
+  unchanged within noise), and synthetic workflow data did not improve unseen-workflow accuracy; neither is in the
+  training data.
+- **English only** (base encoder and all data). **Synthetic in-domain data**: the tickets come from one model family
+  (Claude Haiku) and have no human labels.
+- **Certified automation is per suite**: a threshold certified on one workflow can fail on another (a td-certified
+  threshold had error .771 on zs_wide). Certify on labelled rows of the workflow you automate.
+- **Python runtime only** (torch + transformers, CPU). Latency is the Python reference on a 4-core CPU, not a phone.
+- **Float noise**: batch composition, chunk size and thread count move probabilities by at most about 6e-7 (measured).
+- **One download**: `ej.load('5ak3t/ej', revision='v0.0.1')` fetches only `model.ejpack`, which holds the tokenizer and
+  the encoder config; a local pack needs no network. A weights directory (the secondary format) instead fetches the base
+  model from the Hugging Face Hub at revision `ffb93f3b` on first use.
+- **Reproducibility**: a release is tied to its runtime (`ej/_runtime`, sha256 manifest checked at every load); a known
+  pack is checked against its content digest in `ej.integrity.KNOWN_PACKS` (and, when downloaded, its file sha256 in
+  `ej.integrity.KNOWN_PACK_FILES`).
+
+## 7. Intended use
+
+- On-device triage and routing decisions (support tickets, intents, workflow checks) where a probability distribution is
+  needed per question and uncertain cases are **escalated** to a slower System 2 (an on-device LLM or a cloud model).
+- Ranking, gating and abstention using the returned probabilities, with thresholds certified on your own labelled data.
+
+## 8. Out-of-scope use
+
+- Sole decision-maker for consequential decisions about people (credit, employment, health, legal, safety) without human
+  review.
+- Non-English text; generative tasks; free-text answers.
+- New structured workflows without first measuring quality on labelled examples of that workflow (§6).
+- Security-incident triage as a zero-shot claim (that workflow drove model selection).
+- Transductive use across workflows, or on option sets that change between records: `adapt` / `observe` are for one
+  workflow with a fixed option set (§1).
+
+## 9. Licence and attribution
+
+The weights are licensed under **Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)**. Adaptations
+must be shared under CC BY-SA 4.0 or a compatible licence. Suggested attribution:
+
+> ej 0.0.1 by Saket Bhushan, licensed under CC BY-SA 4.0
+> (https://creativecommons.org/licenses/by-sa/4.0/). Derived from intfloat/e5-small-v2 (MIT; Wang et al., arXiv:2212.03533)
+> and trained on Typed Decisions (Apache-2.0), Banking77 (CC BY 4.0), CLINC150 (CC BY 3.0), GoEmotions (Apache-2.0) and an
+> in-house support-ticket corpus written with Claude Haiku (not released), with distillation from
+> cross-encoder/nli-deberta-v3-xsmall (Apache-2.0). Full notices, creators and open questions: NOTICE.

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