Token Classification
Transformers
German
frame-semantics
framenet
salsa
german
semantic-parsing
srl
argument-extraction
Instructions to use texturejc/texture-frames-de-args with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use texturejc/texture-frames-de-args with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="texturejc/texture-frames-de-args")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("texturejc/texture-frames-de-args", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 01c7ac381c60995279d6b5541c7c23b10f4bd7ddaa2c3e8fc568c87d9c96188c
- Size of remote file:
- 1.36 GB
- SHA256:
- 1f9f1ab3a5b28205a026ee689e842994cbe8f2c30da2392ed8fbee578fb03fc6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.