texturejc/texture-frames-frame

texture-frames · frame-classification head

The frame-classification stage of `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` 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

pip install git+https://github.com/texturejc/Texture_Frames

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

Loading is handled by texture_frames.weights.load_frame.

Results

Open-Sesame test split:

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

@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`; thanks to the Berkeley FrameNet and Open-Sesame projects.