Instructions to use pheinisch/ConclusionValidityNoveltyClassifier-Augmentation-in_750 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pheinisch/ConclusionValidityNoveltyClassifier-Augmentation-in_750 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, RobertaForValNovRegression tokenizer = AutoTokenizer.from_pretrained("pheinisch/ConclusionValidityNoveltyClassifier-Augmentation-in_750") model = RobertaForValNovRegression.from_pretrained("pheinisch/ConclusionValidityNoveltyClassifier-Augmentation-in_750", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from pheinisch/ConclusionValidityNoveltyClassifier-Augmentation-in_750: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/pheinisch/ConclusionValidityNoveltyClassifier-Augmentation-in_750/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://pheinisch/ConclusionValidityNoveltyClassifier-Augmentation-in_750/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/pheinisch/ConclusionValidityNoveltyClassifier-Augmentation-in_750/resolve/main/pytorch_model.bin
1.42 GB
- Xet hash:
- 38dfbaa9dc6edb10f4e7ba26a7e5882713843c402bca9fb7c696d9cfbd330b6f
- Size of remote file:
- 1.42 GB
- SHA256:
- ce79a736a1cab98a183f8b23397c505db43ef20aa6a404f31a622f7353c15687
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