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("fill-mask", model="textattack/bert-base-uncased-rotten_tomatoes")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-rotten_tomatoes") model = AutoModelForMaskedLM.from_pretrained("textattack/bert-base-uncased-rotten_tomatoes") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- da71ebbff29ac81f61dc1c166ed3919d785295cd8b940c79af897a24a8a46bce
- Size of remote file:
- 438 MB
- SHA256:
- c31c2aa729a6d0558b500b342042c4d233e2a8e2e57c6ad2bb9734bfbb549704
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