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:
- 2c2eb9ba30b8e24391d32c45ea88307409382c41f91ea2131d25d700f0dcf997
- Size of remote file:
- 2.66 GB
- SHA256:
- 2c086ac47708aa17be4cca2a2d8c022fce20f71859d09ad2ba28a9517369e04d
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