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

Adds a single-file ONNX export of the model under `onnx/model.onnx` (~436 MB) for inference with ONNX Runtime, plus an 'ONNX' section on the model card documenting the input/output contract and how a term's impact is recovered.

The file was produced with the export script proposed in the companion PR basnetsoyuj/DeeperImpact#17 (`src/deep_impact/scripts/export_onnx.py`) and verified to match the PyTorch model within max |diff| ~ 6e-6 over a padded batch of documents.

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 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:38cb884a1b7bb3f2dbf969c22219cb5b4eba64b824f0291ea8249baa616fc8a5
+size 435818763