Instructions to use TransWiC/bert-large-CLS-B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TransWiC/bert-large-CLS-B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TransWiC/bert-large-CLS-B")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TransWiC/bert-large-CLS-B") model = AutoModelForSequenceClassification.from_pretrained("TransWiC/bert-large-CLS-B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6c7d7345b9b180f5ad178b55bdc7e400cca01735c251c6837bae389c110fcfd3
- Size of remote file:
- 2.88 kB
- SHA256:
- 6aef620af9dc323d460035f21adf09d622e29f6318e2d4e8a295dba21903b850
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