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:
- 5988502195fc195ee599dade285181263304b95fa1e74a8cbb909634b8cc9c3b
- Size of remote file:
- 1.33 GB
- SHA256:
- 273495400adebec39210e2045cc842bf59e90fdd3e95e8faa19e9f2f7594663e
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