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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: token-classification
+tags:
+- frame-semantics
+- framenet
+- semantic-parsing
+- srl
+- english
+base_model: microsoft/deberta-v3-large
+---
+
+# texture-frames · trigger-identification head
+
+The **trigger-identification** stage of
+[`texture-frames`](https://github.com/texturejc/Texture_Frames), a fast FrameNet
+semantic-frame parser. A per-token classifier (`O` / `TRIGGER`) that finds the
+words in a sentence that evoke a frame.
+
+It fine-tunes [`microsoft/deberta-v3-large`](https://huggingface.co/microsoft/deberta-v3-large)
+on **FrameNet 1.7** (via NLTK) with the **Open-Sesame** document splits, scored
+with the upstream word-level F1 so it is directly comparable to prior work.
+
+> This is one of three stages. Use it through the package rather than alone; the
+> pipeline chains trigger → frame → arguments.
+
+## Usage
+
+```bash
+pip install git+https://github.com/texturejc/Texture_Frames
+```
+
+```python
+from texture_frames import FrameParser
+parser = FrameParser()   # downloads all three heads on first use
+for ann in parser.parse("The chef gave food to the customer ."):
+    print(ann.trigger, "->", ann.frame)
+# gave -> Giving
+```
+
+A standard `AutoModelForTokenClassification`, so it also loads directly with
+`transformers` — but the package handles the word-level alignment (a
+`trigger_bias` lever trades precision for recall).
+
+## Results
+
+Open-Sesame test split, word-level F1 (same metric as the T5 baseline):
+
+| Metric | This head | T5 baseline |
+| ------ | --------- | ----------- |
+| Trigger F1 | **0.750** | 0.735 |
+| Speed | single forward pass (~50–60 ms) | 3 beam-search passes |
+
+Ahead of the baseline, and ~3–4× faster (no autoregressive decoding).
+
+## 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. See the [repo](https://github.com/texturejc/Texture_Frames) for details.
+
+## 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.