Instructions to use okovtun/bert-emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use okovtun/bert-emotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="okovtun/bert-emotion")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("okovtun/bert-emotion") model = AutoModelForSequenceClassification.from_pretrained("okovtun/bert-emotion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5cfaa07e0d21f3189ab5c07b2da8f85ba893bafb5f164f3bc69090e30bb08687
- Size of remote file:
- 263 MB
- SHA256:
- 0cb26287af5b13f3994a58023d575f460dcde4eed52a56f71db5aa475649b60c
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