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README.md

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+---
+license: other
+license_name: framenet-academic
+license_link: https://framenet.icsi.berkeley.edu/framenet_data
+language:
+- en
+library_name: transformers
+pipeline_tag: text-classification
+tags:
+- frame-semantics
+- framenet
+- semantic-parsing
+- srl
+- english
+base_model: microsoft/deberta-v3-large
+---
+
+# texture-frames · frame-classification head
+
+The **frame-classification** stage of
+[`texture-frames`](https://github.com/texturejc/Texture_Frames), a fast FrameNet
+semantic-frame parser. Given a sentence with a marked trigger, it predicts which
+of ~1,221 FrameNet frames the trigger evokes.
+
+It fine-tunes [`microsoft/deberta-v3-large`](https://huggingface.co/microsoft/deberta-v3-large)
+on **FrameNet 1.7** and uses **marker-token pooling**: the trigger is wrapped in
+entity markers (`… <t> gave </t> …`) and the frame representation is the
+concatenation of the two marker tokens' hidden states (not `[CLS]`), focusing the
+classifier on the predicate. A single forward pass — no beam search.
+
+> This is one of three stages. Use it through the package rather than alone.
+
+## Usage
+
+```bash
+pip install git+https://github.com/texturejc/Texture_Frames
+```
+
+```python
+from texture_frames import FrameParser
+parser = FrameParser()
+for ann in parser.parse("The chef gave food to the customer ."):
+    print(ann.trigger, "->", ann.frame)
+# gave -> Giving
+```
+
+At inference the logits are **soft-masked** toward the trigger's candidate frames
+(from the FrameNet lexicon) so a confident non-candidate can still win.
+
+## Files
+
+| File | What |
+| ---- | ---- |
+| `frame2_model.pt` | model `state_dict` (backbone + marker-pooling classifier) |
+| `frame2id.json` | `{frame name → id}` label map + `base_model` |
+| tokenizer files | DeBERTa-v3 tokenizer with the `<t>` / `</t>` markers added |
+
+Loading is handled by `texture_frames.weights.load_frame`.
+
+## Results
+
+Open-Sesame test split:
+
+| Metric | This head | T5 baseline |
+| ------ | --------- | ----------- |
+| Frame accuracy | **0.863–0.868** | 0.887 |
+| Speed | single forward pass (~50–60 ms) | 3 beam-search passes |
+
+Competitive (~−0.02); the residual gap is largely a candidate-lexicon coverage
+ceiling (2.2% of gold frames fall outside the candidate set), not discrimination.
+
+## Training
+
+`microsoft/deberta-v3-large`, AdamW lr 1e-5, warmup 0.06, weight decay 0.01,
+batch 16, max length 320, bf16, 5 epochs. Data: FrameNet 1.7 (NLTK), Open-Sesame
+splits.
+
+## Licence
+
+**Code (the package): MIT.** **Weights:** trained on **FrameNet 1.7**, which
+carries its own academic-use terms — review them before redistributing.
+
+## Citation
+
+```bibtex
+@software{texture_frames,
+  author = {Carney, James},
+  title  = {texture-frames: a fast DeBERTa encoder FrameNet parser},
+  url    = {https://github.com/texturejc/Texture_Frames},
+  year   = {2026}
+}
+```
+
+Builds on David Chanin's
+[`frame-semantic-transformer`](https://github.com/chanind/frame-semantic-transformer);
+thanks to the Berkeley FrameNet and Open-Sesame projects.