Instructions to use textattack/bert-base-uncased-rotten-tomatoes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/bert-base-uncased-rotten-tomatoes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/bert-base-uncased-rotten-tomatoes")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-rotten-tomatoes") model = AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-uncased-rotten-tomatoes", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 95d622cd2e916325078269f39d9d7c132ead6997fa3de50f09029d1320cc12c9
- Size of remote file:
- 438 MB
- SHA256:
- 0fedd630c05b94824e639d7be57f6761611bfe620870249e419cd086d2292c9b
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