Token Classification
Transformers
Safetensors
PyTorch
English
joint_causal
feature-extraction
causal-extraction
relation-extraction
bert
causality
custom_code
Instructions to use rasoultilburg/SocioCausaNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rasoultilburg/SocioCausaNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="rasoultilburg/SocioCausaNet", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rasoultilburg/SocioCausaNet", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from rasoultilburg/SocioCausaNet: direct link, hf CLI and curl.
- Browser
- Download file 711 kB
-
https://huggingface.co/rasoultilburg/SocioCausaNet/resolve/main/tokenizer.json
- Command line
-
hf download hf://rasoultilburg/SocioCausaNet/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/rasoultilburg/SocioCausaNet/resolve/main/tokenizer.json
711 kB
File too large to display, you can check the raw version instead.