Instructions to use dmis-lab/biobert-v1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dmis-lab/biobert-v1.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dmis-lab/biobert-v1.1")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dmis-lab/biobert-v1.1") model = AutoModel.from_pretrained("dmis-lab/biobert-v1.1", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Joao Gante commited on
Commit ·
90e3da2
1
Parent(s): 551ca18
Add TF weights
Browse filesModel converted by the `transformers`' `pt_to_tf` CLI -- all converted model outputs and hidden layers were validated against its Pytorch counterpart. Maximum crossload output difference=5.722e-06; Maximum converted output difference=5.722e-06.
- tf_model.h5 +3 -0
tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:03852acaefd6cd1eb8112a68b0f348d608ea307ed1088192432f1db95c83a90b
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size 433508328
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