Instructions to use mohsenfayyaz/bert-base-uncased-offenseval2019-downsample with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mohsenfayyaz/bert-base-uncased-offenseval2019-downsample with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mohsenfayyaz/bert-base-uncased-offenseval2019-downsample")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mohsenfayyaz/bert-base-uncased-offenseval2019-downsample") model = AutoModelForSequenceClassification.from_pretrained("mohsenfayyaz/bert-base-uncased-offenseval2019-downsample") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 69d84160762ade556c521e2558be39c3d5beb05ed2905203046ecce0019643fb
- Size of remote file:
- 438 MB
- SHA256:
- be67fbbc64ba2c95f75fe4d016ad902c94bbe6005774f1832402c40defef852a
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