Instructions to use noahjadallah/cause-effect-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use noahjadallah/cause-effect-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="noahjadallah/cause-effect-detection")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("noahjadallah/cause-effect-detection") model = AutoModelForTokenClassification.from_pretrained("noahjadallah/cause-effect-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from noahjadallah/cause-effect-detection: direct link, hf CLI and curl.
- Browser
- Download file 2.16 kB
-
https://huggingface.co/noahjadallah/cause-effect-detection/resolve/main/training_args.bin
- Command line
-
hf download hf://noahjadallah/cause-effect-detection/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/noahjadallah/cause-effect-detection/resolve/main/training_args.bin
2.16 kB
- Xet hash:
- a2898a83a1158bdeaeada1ed738e0aeab6f6147d2a20f9d23d2b9ecfbc3e0485
- Size of remote file:
- 2.16 kB
- SHA256:
- 8940abe19d6f5a3849b095d57e937784f174f81fa251e45a24642a7fc3d14b8a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.