texturejc/texture-frames-trigger

texture-frames · trigger-identification head

The trigger-identification stage of `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` 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

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

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):

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 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

@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.