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:
- 826525b9fd3c282338e181cf4f250feda916211d9e629cbbdc0fd9328f940f69
- Size of remote file:
- 2.66 GB
- SHA256:
- a09b9a8930584ab49297ea6372707323e1bfd9ed534c672c8825a9ac6959aff9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.