Instructions to use dpavlis/distilbert-base-uncased-finetuned-emotions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dpavlis/distilbert-base-uncased-finetuned-emotions with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dpavlis/distilbert-base-uncased-finetuned-emotions")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dpavlis/distilbert-base-uncased-finetuned-emotions") model = AutoModelForSequenceClassification.from_pretrained("dpavlis/distilbert-base-uncased-finetuned-emotions") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8afc33e2061d0c29d90fcdc3dc8fb18a2b99ed6b5641653332896e27be6c23c9
- Size of remote file:
- 5.37 kB
- SHA256:
- 24abd1509b4e0c1c8cdd61e1dfe9fd23b21ab6dc53f8b638d39a312b404a45f0
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