Instructions to use TransWiC/bert-large-CLS-ET with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TransWiC/bert-large-CLS-ET with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TransWiC/bert-large-CLS-ET")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TransWiC/bert-large-CLS-ET") model = AutoModelForSequenceClassification.from_pretrained("TransWiC/bert-large-CLS-ET", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f16470735af137ea3ff5fd8857c15ee09f306ceb5aa9d3d7e42b3636a8782235
- Size of remote file:
- 2.66 GB
- SHA256:
- 9fb3b6a63ec3c7c2ce60e28672296d765e1c6edf8e253906836be11256a8f483
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.