Instructions to use splitner/spanclass-qa-dice-wnut17 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use splitner/spanclass-qa-dice-wnut17 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("splitner/spanclass-qa-dice-wnut17") model = AutoModel.from_pretrained("splitner/spanclass-qa-dice-wnut17", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from splitner/spanclass-qa-dice-wnut17: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/splitner/spanclass-qa-dice-wnut17/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://splitner/spanclass-qa-dice-wnut17/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/splitner/spanclass-qa-dice-wnut17/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 4119802eafe70df8bca9403ff128f8a051b2aa86ed6c25261338dbe7aeca08d5
- Size of remote file:
- 438 MB
- SHA256:
- 174b2585670d4c5086482cbbc9247a8be33c686fb733bf1ac39cc98049476fe3
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