Instructions to use SpanBERT/spanbert-large-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SpanBERT/spanbert-large-cased with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SpanBERT/spanbert-large-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from SpanBERT/spanbert-large-cased: direct link, hf CLI and curl.
- Browser
- Download file 669 MB
-
https://huggingface.co/SpanBERT/spanbert-large-cased/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://SpanBERT/spanbert-large-cased/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/SpanBERT/spanbert-large-cased/resolve/main/flax_model.msgpack
669 MB
- Xet hash:
- 238d835442b42bbd9484ce6e9db5488ef1f4c393d4f63826c71c534675f59870
- Size of remote file:
- 669 MB
- SHA256:
- bafbca9bc2e60e903863f2ee107795fde0e500ab1a391206596ce6a6dc77a862
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