Text Classification
Transformers
Safetensors
PyTorch
English
emotion-classification
multilabel-classification
distilbert
Eval Results (legacy)
Instructions to use Gandhiert/emotion-multilabel-distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gandhiert/emotion-multilabel-distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Gandhiert/emotion-multilabel-distilbert")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Gandhiert/emotion-multilabel-distilbert", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 35a0a27f66ba15d472cf0c260c91b3abfb2de3787d4665bdbb5d7b269a021fe4
- Size of remote file:
- 5.24 kB
- SHA256:
- 9e38d25711d9f5db36dcf49424bfe839241ddde877b14698f4be55754fb865c3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.