Instructions to use textattack/bert-base-uncased-snli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/bert-base-uncased-snli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/bert-base-uncased-snli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-snli") model = AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-uncased-snli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from textattack/bert-base-uncased-snli: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/textattack/bert-base-uncased-snli/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://textattack/bert-base-uncased-snli/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/textattack/bert-base-uncased-snli/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- b696d9a244df9e0a8e3c791761ac01007bf80586166b85741c1d03bcc240dcd5
- Size of remote file:
- 438 MB
- SHA256:
- 7ffbed4f5e42c47db2b76d3f957c1daba3885063d2d8118c3d7b520fdf0d50a8
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