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")# 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") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 49de3e3200ad8b135c1a53ff2b64f34cb16bd42fd7f023e595c7d1d9691f29e2
- Size of remote file:
- 438 MB
- SHA256:
- a5f7d1b5618ba58907379af830ee895c8800e3b381286b13d07d90aaf204dc40
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