Instructions to use splitner/spanclass-qa-dice-bionlp13cg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use splitner/spanclass-qa-dice-bionlp13cg with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("splitner/spanclass-qa-dice-bionlp13cg") model = AutoModel.from_pretrained("splitner/spanclass-qa-dice-bionlp13cg", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from splitner/spanclass-qa-dice-bionlp13cg: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/splitner/spanclass-qa-dice-bionlp13cg/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://splitner/spanclass-qa-dice-bionlp13cg/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/splitner/spanclass-qa-dice-bionlp13cg/resolve/main/pytorch_model.bin
440 MB
- Xet hash:
- b3f99685425d4f2b21326953381070dd2e18d0c826eba9aac3fb9ce973b5f00b
- Size of remote file:
- 440 MB
- SHA256:
- 2fce980c90bc65349ec16ecc0d8281e2a7d3c807e857dfa31f1248b4a1dee473
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.