Instructions to use textattack/bert-base-uncased-SST-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/bert-base-uncased-SST-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/bert-base-uncased-SST-2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-SST-2") model = AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-uncased-SST-2", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from textattack/bert-base-uncased-SST-2: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/textattack/bert-base-uncased-SST-2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://textattack/bert-base-uncased-SST-2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/textattack/bert-base-uncased-SST-2/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 49de3e3200ad8b135c1a53ff2b64f34cb16bd42fd7f023e595c7d1d9691f29e2
- Size of remote file:
- 438 MB
- SHA256:
- a5f7d1b5618ba58907379af830ee895c8800e3b381286b13d07d90aaf204dc40
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