Instructions to use SpanBERT/spanbert-base-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SpanBERT/spanbert-base-cased with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SpanBERT/spanbert-base-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from SpanBERT/spanbert-base-cased: direct link, hf CLI and curl.
- Browser
- Download file 218 MB
-
https://huggingface.co/SpanBERT/spanbert-base-cased/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://SpanBERT/spanbert-base-cased/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/SpanBERT/spanbert-base-cased/resolve/main/flax_model.msgpack
218 MB
- Xet hash:
- b4b817c9a802ce5ab5909bea6c419bfc1c0b86734a324583127acd3aad587d39
- Size of remote file:
- 218 MB
- SHA256:
- a94ff41e14f1b7f9a8e3f4ffec613d493e34e99495fa3755f0f52af6e893c1a6
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