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:
- 42e813e277ee747bb4b2b69d356786e7b6bbfd99e2637ce6c3d17f87ab35ca8b
- Size of remote file:
- 2.66 GB
- SHA256:
- cf00eba93b3ba212439bbd208a194d6a5b23821bd35fe348828f9ff7f3bcbb84
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