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
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:c66314cd89801d850ce03c2b1120b0e7463ceea36974169ec311a8ec7afe055b
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size 1112201288
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