Instructions to use TransWiC/bert-large-CLS-E with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TransWiC/bert-large-CLS-E with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TransWiC/bert-large-CLS-E")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TransWiC/bert-large-CLS-E") model = AutoModelForSequenceClassification.from_pretrained("TransWiC/bert-large-CLS-E", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5c120d0521be04b841530c89bde3f8a5978f2ab8915298a632146b5f60051dc4
- Size of remote file:
- 1.33 GB
- SHA256:
- 572ccfb372c0426abba7275fcae4666cbb96ef4d0c9566843927813be9ae2dbc
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