Instructions to use SALT-NLP/CultureBank-Relevance-Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SALT-NLP/CultureBank-Relevance-Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SALT-NLP/CultureBank-Relevance-Classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SALT-NLP/CultureBank-Relevance-Classifier") model = AutoModelForSequenceClassification.from_pretrained("SALT-NLP/CultureBank-Relevance-Classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download rng_state.pth from SALT-NLP/CultureBank-Relevance-Classifier: direct link, hf CLI and curl.
- Browser
- Download file 14.2 kB
-
https://huggingface.co/SALT-NLP/CultureBank-Relevance-Classifier/resolve/main/rng_state.pth
- Command line
-
hf download hf://SALT-NLP/CultureBank-Relevance-Classifier/rng_state.pth
-
curl -L -o rng_state.pth https://huggingface.co/SALT-NLP/CultureBank-Relevance-Classifier/resolve/main/rng_state.pth
14.2 kB
- Xet hash:
- 35acebc47c3e6f6a6f1c3ff0abe89bafeb40085fd6b200e2f423ba636d216a98
- Size of remote file:
- 14.2 kB
- SHA256:
- 3c03fed2d779015265efb19e7f1fa3a58fcd50b77055a4348cc7ee0b0c378469
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