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 optimizer.pt from splitner/spanclass-qa-dice-wnut17: direct link, hf CLI and curl.
- Browser
- Download file 876 MB
-
https://huggingface.co/splitner/spanclass-qa-dice-wnut17/resolve/refs%2Fpr%2F1/optimizer.pt
- Command line
-
hf download hf://splitner/spanclass-qa-dice-wnut17@refs/pr/1/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/splitner/spanclass-qa-dice-wnut17/resolve/refs%2Fpr%2F1/optimizer.pt
876 MB
- Xet hash:
- baad049559bb90bca04f643233854725b5150be1953a574239fea9eb3b0be572
- Size of remote file:
- 876 MB
- SHA256:
- 07b4464f91712e4b32916594cd5c6e9b246a29e88441512ded7eba6b39822907
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