EASE-Delta Reader: transformers-native claim-vs-evidence classifier

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README.md

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+---
+license: cc-by-sa-4.0
+language:
+- en
+library_name: transformers
+pipeline_tag: text-classification
+base_model: answerdotai/ModernBERT-large
+datasets:
+- tals/vitaminc
+- nyu-mll/multi_nli
+- alisawuffles/WANLI
+tags:
+- natural-language-inference
+- nli
+- fact-verification
+- fact-checking
+- cross-encoder
+- modernbert
+- rag
+- evidence
+metrics:
+- accuracy
+model-index:
+- name: ease-delta-reader
+  results:
+  - task:
+      type: text-classification
+      name: Fact verification
+    dataset:
+      name: VitaminC
+      type: tals/vitaminc
+      split: test
+    metrics:
+    - type: accuracy
+      value: 91.53
+      name: Accuracy
+  - task:
+      type: text-classification
+      name: Natural language inference
+    dataset:
+      name: MultiNLI (mismatched)
+      type: nyu-mll/multi_nli
+      split: validation_mismatched
+    metrics:
+    - type: accuracy
+      value: 90.21
+      name: Accuracy
+  - task:
+      type: text-classification
+      name: Natural language inference
+    dataset:
+      name: WANLI
+      type: alisawuffles/WANLI
+      split: test
+    metrics:
+    - type: accuracy
+      value: 77.42
+      name: Accuracy
+  - task:
+      type: text-classification
+      name: Natural language inference
+    dataset:
+      name: SNLI
+      type: stanfordnlp/snli
+      split: test
+    metrics:
+    - type: accuracy
+      value: 84.39
+      name: Accuracy
+---
+
+# EASE-Delta Reader (large)
+
+**Does this passage support the claim, refute it, or not settle it at all?**
+
+A 395M-parameter cross-encoder (`answerdotai/ModernBERT-large` fine-tuned) that reads one claim against one passage and answers `SUPPORTS`, `REFUTES` or `NOT_ENOUGH_INFO`. It was trained to say `NOT_ENOUGH_INFO` when a passage is about something else, which is where general NLI models most often guess: on passages taken from unrelated documents it gives a decisive answer 0.58% of the time; a widely used public NLI model does so 45.07% of the time.
+
+It is the reader inside [EASE-Delta](https://github.com/jithinsaireddy/ease-delta), a system that keeps a task's decisions current as its messages and documents change. Try both in the [demo](https://huggingface.co/spaces/jithinpothireddy21/ease-delta-demo).
+
+## Use it
+
+Claim first, evidence second.
+
+```python
+from transformers import pipeline
+
+reader = pipeline("text-classification", model="jithinpothireddy21/ease-delta-reader", top_k=None)
+claim = "The client has approved the final design."
+passages = [
+    "Email from the client: we approve the final design, please go ahead.",
+    "Email from the client: we cannot approve the design yet; the colours are wrong.",
+    "The office is closed on the first Monday of the month for staff training.",
+]
+for passage in passages:
+    print(reader({"text": claim, "text_pair": passage}, truncation=True)[0])
+
+# {'label': 'SUPPORTS', 'score': 0.935}
+# {'label': 'REFUTES', 'score': 0.991}
+# {'label': 'NOT_ENOUGH_INFO', 'score': 0.999}
+```
+
+With sentence-transformers, continuing from above:
+
+```python
+from sentence_transformers import CrossEncoder
+
+model = CrossEncoder("jithinpothireddy21/ease-delta-reader")
+probs = model.predict([(claim, passage) for passage in passages], apply_softmax=True)
+# one row per pair; columns SUPPORTS, REFUTES, NOT_ENOUGH_INFO
+```
+
+Inputs were at most 256 tokens in training (`truncation=True` cuts there). Split long documents into passages and read each one; say who is speaking in the passage itself ("Email from the client: ..."), because the reader sees only the text.
+
+Probabilities are best calibrated after dividing the logits by **1.203** (fitted on development data) before the softmax.
+
+## Results
+
+Test sets, accuracy. One evaluation harness for all three models; brackets and paired tests are in the [results report](https://github.com/jithinsaireddy/ease-delta/blob/main/docs/RESULTS.md).
+
+| | **This model** (large) | base reader | `tasksource/ModernBERT-base-nli` |
+|---|---:|---:|---:|
+| Passages from unrelated documents read as decisive (lower is better) | 0.58% | 0.82% | 45.07% |
+| VitaminC: claims against Wikipedia revisions | 91.53% | 90.20% | 68.30% |
+| MultiNLI, mismatched genres | 90.21% | 88.56% | 89.84% |
+| WANLI | 77.42% | 74.74% | 66.34% |
+| SNLI (not trained on) | 84.39% | 80.33% | 89.15% |
+| ANLI round 1 (not trained on) | 53.70% | 46.40% | 63.40% |
+| ANLI round 2 (not trained on) | 38.10% | 33.30% | 48.20% |
+| ANLI round 3 (not trained on) | 36.00% | 34.75% | 42.25% |
+
+`tasksource/ModernBERT-base-nli` is a base-size multi-task NLI model whose training includes SNLI and ANLI; it is the better choice for adversarial NLI of the ANLI kind. This reader is built for evidence: contrastive Wikipedia revisions (VitaminC), and knowing when a passage does not bear on the claim.
+
+On VitaminC, real revisions: 89.90%; synthetic: 94.25%.
+
+## Labels
+
+| Label | Meaning | NLI equivalent |
+|---|---|---|
+| `SUPPORTS` | the passage establishes the claim | entailment |
+| `REFUTES` | the passage establishes that the claim is false | contradiction |
+| `NOT_ENOUGH_INFO` | the passage does not settle the claim, including when it is about something else | neutral |
+
+## Training
+
+900,000 examples streamed from the Hugging Face Hub: VitaminC (cc-by-sa-3.0), MultiNLI and WANLI (cc-by-4.0), plus 20% synthetic unrelated pairs (the claim of one example with the evidence of another from a different topic, labelled `NOT_ENOUGH_INFO`). SNLI was left out because its annotators labelled unrelated content as contradiction; ANLI because its licence is non-commercial. One run, on one Apple M4 Max. The evaluation plan was written and hashed before training: [`docs/PREREGISTRATION_LARGE.md`](https://github.com/jithinsaireddy/ease-delta/blob/main/docs/PREREGISTRATION_LARGE.md).
+
+## Limitations
+
+- English only.
+- Passages on the claim's own subject that do not settle it are still read as decisive about one time in five (VitaminC test, not-enough-info items).
+- Conflicts that follow only from a consequence ("broke a leg" against "cycles to work") are mostly missed.
+- Numbers and dates are read as text; compare them with code when they matter.
+- Not a judge of truth: it reports what the passage says, not whether the passage is right.
+
+## How this checkpoint was made
+
+Converted exactly from the EASE-Delta edge model (`ease/export.py` in the code repository explains why the conversion is exact). On 1,200 development pairs the largest logit difference between the two was 9.5e-07 and the predicted label never differed (0 disagreements). The full system, with its calibrated rules and regression suites, is [`jithinpothireddy21/ease-delta`](https://huggingface.co/jithinpothireddy21/ease-delta).
+
+Other size: [`jithinpothireddy21/ease-delta-reader-base`](https://huggingface.co/jithinpothireddy21/ease-delta-reader-base).
+
+## Licence
+
+CC BY-SA 4.0 for the weights, because VitaminC is share-alike (Creative Commons' guidance on AI training describes this as the cautious course). The code is Apache-2.0. Credit for the training data: VitaminC (Schuster, Fisch and Barzilay, NAACL 2021), MultiNLI (Williams, Nangia and Bowman, NAACL 2018), WANLI (Liu, Swayamdipta, Smith and Choi, EMNLP 2022). Backbone: ModernBERT (Warner et al., 2024).
+
+## Citation
+
+```bibtex
+@software{pothireddy2026easedelta,
+  author = {Pothireddy, Jithin},
+  title = {EASE-Delta: revision-aware decision computation},
+  year = {2026},
+  url = {https://github.com/jithinsaireddy/ease-delta}
+}
+```

config.json

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+{
+  "architectures": [
+    "ModernBertForSequenceClassification"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
+  "classifier_activation": "gelu",
+  "classifier_bias": true,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "cls",
+  "cls_token_id": 50281,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "dtype": "float32",
+  "ease_max_length": 256,
+  "embedding_dropout": 0.0,
+  "eos_token_id": 50282,
+  "global_attn_every_n_layers": 3,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "SUPPORTS",
+    "1": "REFUTES",
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export_check.json

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+}

model.safetensors

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+oid sha256:7f8e51d8da318ebc3d2768cf267844480664d34bb8752f7e3ae4a93eb2db6e19
+size 1583359908

tokenizer_config.json

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+  "unk_token": "[UNK]"
+}