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:
- bddb3f23e94399c82861a993af1a529cb2d831f3897d2e691e6c87db0b8ee155
- Size of remote file:
- 438 MB
- SHA256:
- 804de184b90a17e4b3626d47f54ec9d187062ff860f605f6a45d6cfb670f04d2
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