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):
| 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 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.