Instructions to use SSUMedInfo/TeaBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SSUMedInfo/TeaBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SSUMedInfo/TeaBERT")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SSUMedInfo/TeaBERT") model = AutoModel.from_pretrained("SSUMedInfo/TeaBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 41f59d36cd40a24312456590c4c09ac88880f5a3ac279740789cf2ddddff1482
- Size of remote file:
- 1.11 GB
- SHA256:
- a613063487b580b38d2d67c1a22b17a8fa4b7537398d8a11513dc854a92641cb
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