Instructions to use TransWiC/bert-large-CLS-E with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TransWiC/bert-large-CLS-E with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TransWiC/bert-large-CLS-E")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TransWiC/bert-large-CLS-E") model = AutoModelForSequenceClassification.from_pretrained("TransWiC/bert-large-CLS-E") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 720b570e865d4c1e9979a1dfe2bb62cd774806d5fc0a939405260a6d62cb113e
- Size of remote file:
- 2.88 kB
- SHA256:
- 0416909a6d8487b42748217725f88380780304bf7a3bee004c0bda668e75b447
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.