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README.md

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+---
+license: mit
+library_name: onnx
+tags:
+  - alignscore
+  - onnx
+  - text-classification
+  - natural-language-inference
+  - roberta
+base_model: yzha/AlignScore
+pipeline_tag: text-classification
+---
+
+# AlignScore-large (ONNX)
+
+An ONNX export of [**AlignScore-large**](https://huggingface.co/yzha/AlignScore)
+(RoBERTa-large with AlignScore's 3-way and regression heads), for in-process
+inference without a Python/PyTorch runtime.
+
+## Why this exists
+
+Upstream AlignScore ships only PyTorch Lightning checkpoints built from a custom
+`BERTAlignModel` module - no `config.json`, no `safetensors` - so
+`optimum-cli export onnx` cannot consume it, and no ONNX build existed. This is
+that build, so anyone who wants to experiment with AlignScore-large can, without
+standing up a PyTorch runtime or writing a custom export pathway.
+
+It reflects a Familiar Tools belief: a specialized, right-sized model that runs
+efficiently and in-process beats reaching for a large, general, resource-hungry
+one. Exporting a focused model to ONNX is part of that - it makes the model
+cheap to run, easy to embed, and light on dependencies. Custom, deliberately
+engineered solutions tend to be more efficient and more resource-aware than
+general-purpose defaults.
+
+## What this is
+
+This is a faithful ONNX export of the encoder + pooler + the two heads used for
+alignment scoring:
+
+- a RoBERTa-large encoder with pooling layer (`pooler_output = tanh(dense(h[:,0]))`)
+- `tri_layer`: `Linear(hidden, 3)` -> raw 3-way logits
+- `reg_layer`: `Linear(hidden, 1)` -> raw regression logit
+
+Activations are **not** baked into the graph: it emits raw logits, and the
+caller applies `softmax` to the 3-way head and `sigmoid` to the regression head.
+
+### Graph I/O
+
+| Tensor | Direction | Type | Shape |
+|--------|-----------|------|-------|
+| `input_ids` | input | int64 | `[batch, seq]` (dynamic) |
+| `attention_mask` | input | int64 | `[batch, seq]` (dynamic) |
+| `tri_logits` | output | float32 | `[batch, 3]` -> softmax -> `[p_aligned, p_neutral, p_contradict]` |
+| `reg_logit` | output | float32 | `[batch, 1]` -> sigmoid -> `p_aligned_reg` |
+
+There is no `token_type_ids` input: a sentence pair is encoded into a single
+`input_ids` sequence with `</s></s>` separators, exactly as
+`AutoTokenizer("roberta-large")(context, claim)` produces. The bundled
+`tokenizer.json` is the matching fast tokenizer. Use `max_length=512`.
+
+Opset 17. Exported with PyTorch (TorchScript exporter).
+
+## Files
+
+- `alignscore-large.onnx` - the model (~1.36 GB)
+- `tokenizer.json` - roberta-large fast tokenizer with the pair post-processor
+
+## Parity
+
+Verified against the original PyTorch model's scores on a 136-pair corpus:
+**max absolute difference 1.18e-07** across both directions and all four
+probabilities. The source checkpoint SHA-256 is asserted equal to the reference
+before export, so these are provably the same weights. A high-confidence
+contradiction pair scores `p_contradict_3way` 0.982 / 0.977 (forward / reverse).
+
+## Usage (ONNX Runtime, Python)
+
+```python
+import numpy as np, onnxruntime as ort
+from tokenizers import Tokenizer
+
+tok = Tokenizer.from_file("tokenizer.json")
+sess = ort.InferenceSession("alignscore-large.onnx")
+
+def score(context, claim):
+    enc = tok.encode(context, claim)
+    ids = np.array([enc.ids], dtype=np.int64)
+    mask = np.array([enc.attention_mask], dtype=np.int64)
+    tri, reg = sess.run(["tri_logits", "reg_logit"],
+                        {"input_ids": ids, "attention_mask": mask})
+    e = np.exp(tri[0] - tri[0].max()); tri_p = e / e.sum()
+    return {
+        "p_aligned_3way": float(tri_p[0]),
+        "p_neutral_3way": float(tri_p[1]),
+        "p_contradict_3way": float(tri_p[2]),
+        "p_aligned_reg": float(1 / (1 + np.exp(-reg[0][0]))),
+    }
+```
+
+## License and attribution
+
+Released under the **MIT License**, matching upstream.
+
+- AlignScore: Zha et al., *AlignScore: Evaluating Factual Consistency with a
+  Unified Alignment Function*, ACL 2023
+  ([arXiv:2305.16739](https://arxiv.org/abs/2305.16739),
+  [code](https://github.com/yuh-zha/AlignScore)).
+- Original weights: [`yzha/AlignScore`](https://huggingface.co/yzha/AlignScore)
+  (revision `8509e78d25bb914939fc585c626500c9b2944249`).
+- Base encoder: RoBERTa-large (Liu et al., 2019).
+
+This repo redistributes a derivative (ONNX export) of the above under the same
+MIT terms. No weights were retrained or modified; only the inference graph was
+re-expressed.

alignscore-large.onnx

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