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:
- 7892d22a439dc6accfe4ed3f2cd851fc1ca735f52b54386be76203fa92a99f93
- Size of remote file:
- 2.88 kB
- SHA256:
- ec875c654c49200aac96a8d509eb83cc383d912bfc3e16266a1e9cbefa2aa11d
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