Instructions to use AnonymousCS/populism_classifier_bsample_396 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AnonymousCS/populism_classifier_bsample_396 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AnonymousCS/populism_classifier_bsample_396")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AnonymousCS/populism_classifier_bsample_396") model = AutoModelForSequenceClassification.from_pretrained("AnonymousCS/populism_classifier_bsample_396", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from AnonymousCS/populism_classifier_bsample_396: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/AnonymousCS/populism_classifier_bsample_396/resolve/main/training_args.bin
- Command line
-
hf download hf://AnonymousCS/populism_classifier_bsample_396/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/AnonymousCS/populism_classifier_bsample_396/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- 6629bcb41c7ef118c99c8a27d11bc4ff6e27f8c015acc9e9bc211d60354d2861
- Size of remote file:
- 5.43 kB
- SHA256:
- f72c1e65b16acb03955d87b3a93e2c12b683806c6ab13e84daa6bdec56163a2d
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