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
| 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
@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.