| --- |
| license: apache-2.0 |
| base_model: Qwen/Qwen3-4B |
| language: |
| - en |
| pipeline_tag: text-generation |
| tags: |
| - text-classification |
| - natural-language-inference |
| - legal |
| - reinforcement-learning |
| - grpo |
| - qwen3 |
| --- |
| |
| # eXTC — ContractNLI (3-class legal NLI) |
|
|
| > **Anonymized artifact for a paper under double-blind review.** |
| > Author identity and institution will be revealed at camera-ready. |
|
|
| This is the final-stage checkpoint of **eXTC** (eXplainable Text Classifier) for |
| **3-way natural language inference** over non-disclosure-agreement (NDA) clauses, |
| from the [ContractNLI](https://stanfordnlp.github.io/contract-nli/) benchmark. |
|
|
| - **Input**: a contract clause paired with a hypothesis. |
| - **Label**: `entailment`, `contradiction`, or `not_mentioned`. |
| - **Output**: a free-text reasoning trace followed by a final `LABEL: <label>` line — |
| the reasoning serves as a local, inspectable explanation of the prediction. |
|
|
| ## eXTC pipeline |
|
|
| eXTC is a three-stage explainable classifier. This checkpoint is the output of all three stages: |
|
|
| ``` |
| Qwen3-4B (base) |
| │ |
| ├─ Stage I — SOP Learning (structured prompt optimization) |
| │ A natural-language rulebook (Standard Operating Procedure) is learned |
| │ via a structured prompt-optimization algorithm; used only to ground the |
| │ teacher in Stage II (not present at inference). |
| │ |
| ├─ Stage II — SOP-Grounded Reasoning Distillation (R-SFT) |
| │ Teacher: gpt-4.1-mini, prompted with <SOP, input>, rejection sampling |
| │ (M=4 traces/example, keep first trace whose label is correct). |
| │ Student: Qwen3-4B fine-tuned with LoRA (r=64, alpha=128, 2 epochs) on the |
| │ accepted reasoning+label traces, with class-balanced upsampling. |
| │ |
| └─ Stage III — Beyond SOP via RL (BD-GRPO) |
| Balanced Dynamic GRPO: per-class oversampling, then drop zero-advantage |
| (homogeneous-rollout) groups and keep a class-balanced batch of |
| informative groups, with a binary label-correctness reward. |
| ``` |
|
|
| The released checkpoint is the one with the best validation macro-F1 over the |
| RL training trajectory, evaluated on the held-out test set under that selection. |
|
|
| ## Test metrics |
|
|
| ContractNLI 3-class test set (n=2091), greedy decoding (temperature=0): |
|
|
| | Metric | Value | |
| |---|---| |
| | Balanced accuracy | 0.8824 | |
| | Macro F1 | 0.8494 | |
| | Accuracy | 0.8871 | |
| | Invalid output rate | 0.001 | |
|
|
| ## Usage |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| import torch |
| |
| repo = "extc-anon/extc-contractnli" |
| tok = AutoTokenizer.from_pretrained(repo) |
| model = AutoModelForCausalLM.from_pretrained(repo, dtype=torch.bfloat16, device_map="auto") |
| |
| prompt = ( |
| "Premise: The Receiving Party shall not disclose Confidential Information to " |
| "any third party without prior written consent.\n" |
| "Hypothesis: The Receiving Party may share Confidential Information with its " |
| "external auditors without consent.\n\n" |
| "Classify the hypothesis as entailment, contradiction, or not_mentioned. " |
| "Provide your reasoning and then the label." |
| ) |
| text = tok.apply_chat_template( |
| [{"role": "user", "content": prompt}], |
| add_generation_prompt=True, tokenize=False, |
| ) |
| ids = tok(text, return_tensors="pt").input_ids.to(model.device) |
| out = model.generate(ids, max_new_tokens=1024, do_sample=False) |
| print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True)) |
| ``` |
|
|
| ## Format |
|
|
| - Standard HuggingFace `transformers` (safetensors, bfloat16, ~7.5 GB). |
| - Architecture: `Qwen3ForCausalLM`, 4.02B parameters. |
| - Test numbers above use greedy decoding (`do_sample=False`). |
|
|
| ## License |
|
|
| Apache 2.0 (matches the [Qwen3 base model](https://huggingface.co/Qwen/Qwen3-4B)). |
|
|
| ## Citation |
|
|
| Anonymous paper citation will be added at camera-ready. |
|
|
