Instructions to use TransWiC/bert-large-ET with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TransWiC/bert-large-ET with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TransWiC/bert-large-ET")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TransWiC/bert-large-ET") model = AutoModelForSequenceClassification.from_pretrained("TransWiC/bert-large-ET", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3e91be8c31cbf4f6ce1da2f41d25a081e63a39fb1cf8bac36caf72e837a1be5e
- Size of remote file:
- 1.33 GB
- SHA256:
- 681108625ac7ad064a35cc4b0f6ae03e86e5795be38f8749788e929351e69096
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