Instructions to use TransWiC/bert-large-CLS-P with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TransWiC/bert-large-CLS-P with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TransWiC/bert-large-CLS-P")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TransWiC/bert-large-CLS-P") model = AutoModelForSequenceClassification.from_pretrained("TransWiC/bert-large-CLS-P", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 04223effe5a3b34e3c19743be52b97fcfedaa119404c8b0cb5683c0f4acb3ad5
- Size of remote file:
- 1.33 GB
- SHA256:
- 02bf048f2e0fd83bf084c2d4c9f3b88b7f1807d668b680c68275215fdf5ea221
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