Instructions to use pysentimiento/bert-base-uncased-emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pysentimiento/bert-base-uncased-emotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pysentimiento/bert-base-uncased-emotion")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pysentimiento/bert-base-uncased-emotion") model = AutoModelForSequenceClassification.from_pretrained("pysentimiento/bert-base-uncased-emotion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 898182e62af18632bfabefaee3b392ac093d1d939a2bee51117e54ad5861b486
- Size of remote file:
- 438 MB
- SHA256:
- 1f270934557e7ed2c9f80625962062eb8f5623816896a2a7638da4fa6b6330fb
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