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")# pip install -U transformers accelerate # 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
Download flax_model.msgpack from dmis-lab/biobert-v1.1: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/dmis-lab/biobert-v1.1/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://dmis-lab/biobert-v1.1/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/dmis-lab/biobert-v1.1/resolve/main/flax_model.msgpack
433 MB
- Xet hash:
- 020afcc0b1921f435dc91ea5faf46693bf61d46fbe4ec66bcac66739dad258cd
- Size of remote file:
- 433 MB
- SHA256:
- a9d5321cba59a777561395e7794d404088d9c494bd906c40c3aaedec4f88ccb9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.