Text Classification
Transformers
ONNX
Safetensors
PyTorch
English
multi-label-classification
multi-class-classification
emotion
bert
go_emotions
emotion-classification
sentiment-analysis
tensorflow
Eval Results (legacy)
Instructions to use logasanjeev/bert-emotion-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use logasanjeev/bert-emotion-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="logasanjeev/bert-emotion-classifier")# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("logasanjeev/bert-emotion-classifier", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 147bc751f337f5a3f85172f0bd0a124974b83200c2b4a314596cf4f66b852fe1
- Size of remote file:
- 438 MB
- SHA256:
- 50df8103f1100490223e193457d0c1e13371dc5893d511805baebe5a99799d0f
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