Instructions to use textattack/distilbert-base-cased-SST-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/distilbert-base-cased-SST-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/distilbert-base-cased-SST-2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/distilbert-base-cased-SST-2") model = AutoModelForSequenceClassification.from_pretrained("textattack/distilbert-base-cased-SST-2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f516ed699352ba5997343f484c94d1bf55601045a3f84437409f3b466e0b4ca0
- Size of remote file:
- 263 MB
- SHA256:
- 1febf6b0114bc961272e6531ff508205094b5cf5b22934aed79e223750064c5f
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