Instructions to use matteogauthier/distilbert-base-uncased-finetuned-cola with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use matteogauthier/distilbert-base-uncased-finetuned-cola with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="matteogauthier/distilbert-base-uncased-finetuned-cola")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("matteogauthier/distilbert-base-uncased-finetuned-cola") model = AutoModelForSequenceClassification.from_pretrained("matteogauthier/distilbert-base-uncased-finetuned-cola", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from matteogauthier/distilbert-base-uncased-finetuned-cola: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/matteogauthier/distilbert-base-uncased-finetuned-cola/resolve/main/training_args.bin
- Command line
-
hf download hf://matteogauthier/distilbert-base-uncased-finetuned-cola/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/matteogauthier/distilbert-base-uncased-finetuned-cola/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- 8575902a6a38454fbec084e6ad6c57d5da4298927b948bbc020a53bc2df1b4f6
- Size of remote file:
- 5.18 kB
- SHA256:
- 206f6570efdcdd55b5cface1cee79a574a4eab5c906e2e228d68a04c1eac8d24
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