Instructions to use allenai/scibert_scivocab_cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use allenai/scibert_scivocab_cased with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("allenai/scibert_scivocab_cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from allenai/scibert_scivocab_cased: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/allenai/scibert_scivocab_cased/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://allenai/scibert_scivocab_cased/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/allenai/scibert_scivocab_cased/resolve/main/flax_model.msgpack
440 MB
- Xet hash:
- 86f2afbf21b9cfb08303714db07467145043f8efd4963720c8f539e6eb88cada
- Size of remote file:
- 440 MB
- SHA256:
- 4db5764e37b90c7fcb6f1bb413eb3d31f72e7f50f1288512a9ef17fa0ef8d889
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.