Token Classification
Transformers
Safetensors
English
deberta-v2
frame-semantics
framenet
semantic-parsing
srl
english
Instructions to use texturejc/texture-frames-trigger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use texturejc/texture-frames-trigger with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="texturejc/texture-frames-trigger")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("texturejc/texture-frames-trigger") model = AutoModelForTokenClassification.from_pretrained("texturejc/texture-frames-trigger", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2cddcdc76043ce5e9abbc1618c73df342350b42bfcf1b9531ff40f3022638257
- Size of remote file:
- 1.74 GB
- SHA256:
- 725fdb789a8fd22ad6c8d711eaec78b7e568694ee45fc31ab593063f133b7aa0
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