Instructions to use AnonymousCS/populism_classifier_bsample_383 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AnonymousCS/populism_classifier_bsample_383 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AnonymousCS/populism_classifier_bsample_383")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AnonymousCS/populism_classifier_bsample_383") model = AutoModelForSequenceClassification.from_pretrained("AnonymousCS/populism_classifier_bsample_383", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c8882ccc76d859ef556811f4e5b660581af26e7187f383bc0665521dd3194bd2
- Size of remote file:
- 1.34 GB
- SHA256:
- b0b630012e293d80e652f30e79e41d515ded42b732b24be0bea77e2af25dcbc7
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