Instructions to use gsarti/scibert-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gsarti/scibert-nli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="gsarti/scibert-nli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("gsarti/scibert-nli") model = AutoModel.from_pretrained("gsarti/scibert-nli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from gsarti/scibert-nli: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/gsarti/scibert-nli/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://gsarti/scibert-nli/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/gsarti/scibert-nli/resolve/main/flax_model.msgpack
440 MB
- Xet hash:
- 86f36c402771fe6f9ba25001b5f58c8f9865170c22f25d6a1b8b516dadbebd42
- Size of remote file:
- 440 MB
- SHA256:
- 09963dfb5ee0fd354c889b1e83ed6744cce290c27d4384b5cf587dc1ed02bf4d
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