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 scheduler.pt from splitner/spanclass-qa-dice-wnut17: direct link, hf CLI and curl.
- Browser
- Download file 623 Bytes
-
https://huggingface.co/splitner/spanclass-qa-dice-wnut17/resolve/main/scheduler.pt
- Command line
-
hf download hf://splitner/spanclass-qa-dice-wnut17/scheduler.pt
-
curl -L -o scheduler.pt https://huggingface.co/splitner/spanclass-qa-dice-wnut17/resolve/main/scheduler.pt
623 Bytes
- Xet hash:
- 4f9e68b256bdadd966b459ced3c06e192905b803b5c81679548b3ccb4882a4b7
- Size of remote file:
- 623 Bytes
- SHA256:
- d4a57015fc33b143acb9f645d0c49d40adb41bd61cb34740f283dee1a6713133
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