Add ONNX export (onnx/model.onnx) and model-card section

README.md

@@ -31,3 +31,16 @@ python -m src.deep_impact.index \
 ```
 
 It distributes the inference across multiple GPUs in the machine. To manually set the GPUs, use `CUDA_VISIBLE_DEVICES` environment variable.
+
+## ONNX
+
+An ONNX export is available at [`onnx/model.onnx`](onnx/model.onnx) for inference with [ONNX Runtime](https://onnxruntime.ai/) — e.g. from Rust/C++/JS, or from Python without PyTorch.
+
+|        | names                                          | dtype   | shape          |
+|--------|------------------------------------------------|---------|----------------|
+| inputs | `input_ids`, `attention_mask`, `token_type_ids` | int64   | `[batch, seq]` |
+| output | `impact_scores`                                | float32 | `[batch, seq]` |
+
+`impact_scores` is a per-subword-token score. A term's impact is the score at its **first** subword token — the same indexing as `DeepImpact.compute_term_impacts` (`##` continuation subwords are skipped; punctuation and terms past the 512-token window are dropped). Batch and sequence axes are dynamic.
+
+The file was exported with [`src/deep_impact/scripts/export_onnx.py`](https://github.com/basnetsoyuj/DeeperImpact/blob/master/src/deep_impact/scripts/export_onnx.py) and matches the PyTorch model within `max |diff| ~ 6e-6`.

onnx/model.onnx

@@ -0,0 +1,3 @@
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+size 435818763