Instructions to use edwardgowsmith/bert-base-cased-best with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use edwardgowsmith/bert-base-cased-best with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="edwardgowsmith/bert-base-cased-best")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("edwardgowsmith/bert-base-cased-best") model = AutoModelForSequenceClassification.from_pretrained("edwardgowsmith/bert-base-cased-best") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7dbbe36c274d2b36fa041b3a0703f173e7ede066fb44e07909fbc1d0a41eb9f9
- Size of remote file:
- 433 MB
- SHA256:
- 51b7a3cb0be42d15a2baf66908d95bcdccd99b3da1653e784925a837dd5da829
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