Instructions to use LSimmons/FragZONFactMetaRecommendationHyperParamSearch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LSimmons/FragZONFactMetaRecommendationHyperParamSearch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LSimmons/FragZONFactMetaRecommendationHyperParamSearch")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LSimmons/FragZONFactMetaRecommendationHyperParamSearch") model = AutoModelForSequenceClassification.from_pretrained("LSimmons/FragZONFactMetaRecommendationHyperParamSearch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from LSimmons/FragZONFactMetaRecommendationHyperParamSearch: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/LSimmons/FragZONFactMetaRecommendationHyperParamSearch/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://LSimmons/FragZONFactMetaRecommendationHyperParamSearch/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/LSimmons/FragZONFactMetaRecommendationHyperParamSearch/resolve/main/pytorch_model.bin
1.34 GB
- Xet hash:
- 50889e787a60b55d2dbe366ca5894d56142abf5fecf45930a7a87bafd5cb6705
- Size of remote file:
- 1.34 GB
- SHA256:
- 0f280f13c5c787f4364a96b6c6e007e6353eb1360ba95607b0ad1943a2b2f106
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