Text Classification
Transformers
ONNX
Safetensors
PyTorch
English
bert
multi-label-classification
multi-class-classification
emotion
go_emotions
emotion-classification
sentiment-analysis
tensorflow
Eval Results (legacy)
text-embeddings-inference
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 AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("logasanjeev/bert-emotion-classifier") model = AutoModelForSequenceClassification.from_pretrained("logasanjeev/bert-emotion-classifier", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
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- The model performs best on Reddit-style comments with similar preprocessing.
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- Rare emotions (e.g., `grief`, support=6) have lower F1 scores due to limited data.
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- ONNX inference requires `onnxruntime` and compatible hardware (opset 14).
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## Inference Providers
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This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
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- The model performs best on Reddit-style comments with similar preprocessing.
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- Rare emotions (e.g., `grief`, support=6) have lower F1 scores due to limited data.
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- ONNX inference requires `onnxruntime` and compatible hardware (opset 14).
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