Instructions to use textattack/bert-base-uncased-yelp-polarity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/bert-base-uncased-yelp-polarity with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/bert-base-uncased-yelp-polarity")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-yelp-polarity") model = AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-uncased-yelp-polarity", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from textattack/bert-base-uncased-yelp-polarity: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/textattack/bert-base-uncased-yelp-polarity/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://textattack/bert-base-uncased-yelp-polarity/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/textattack/bert-base-uncased-yelp-polarity/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 5ff578aa51d9ce5a5037c119b917b21f29631d06b8f7a5c8ca334cd446d4faf3
- Size of remote file:
- 438 MB
- SHA256:
- 934989a6c6465c39311acbb466e8e27e4a715539c3baa95c7c66b424a6c27a66
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