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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4ad85b01004bc16292191ab5048f497bcd92cedcb8c79f41f1e0f6d3e5e530af
- Size of remote file:
- 438 MB
- SHA256:
- 7ea41714f440a138d145495a0c8f5d9e7c1a4d4af745b39d8109fc78761e94c9
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