Instructions to use Songnguyen263/MultiLabel-Emotion-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Songnguyen263/MultiLabel-Emotion-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Songnguyen263/MultiLabel-Emotion-Classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Songnguyen263/MultiLabel-Emotion-Classification") model = AutoModelForSequenceClassification.from_pretrained("Songnguyen263/MultiLabel-Emotion-Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ab3bf1b5a9d65db9f1a2fce0092faa8cf57d493f9b40527bd1417a86a69a2bd2
- Size of remote file:
- 268 MB
- SHA256:
- 07443f42b93010686db7019f7e4f76ca4b05c20fc99589859ae2a07457cabcca
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