Instructions to use TransWiC/bert-large-CLS-B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TransWiC/bert-large-CLS-B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TransWiC/bert-large-CLS-B")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TransWiC/bert-large-CLS-B") model = AutoModelForSequenceClassification.from_pretrained("TransWiC/bert-large-CLS-B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ba6d73a582df5649d705d43b331e7cdda10883c3bb9715b5a9ed22c2a35fc98f
- Size of remote file:
- 2.66 GB
- SHA256:
- 62052a2c62d0dd2b9f868be1f4eb71859e1e8dd8524de6ee1a180463600cdc59
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