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
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https://www.kaggle.com/code/ravindranlogasanjeev/evaluation-logasanjeev-goemotions-bert/notebook
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Fine-tuned [BERT-base-uncased](https://huggingface.co/bert-base-uncased) on [GoEmotions](https://huggingface.co/datasets/go_emotions) for multi-label classification (28 emotions). This updated version includes improved Macro F1, ONNX support for efficient inference, and visualizations for better interpretability.
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https://www.kaggle.com/code/ravindranlogasanjeev/evaluation-logasanjeev-goemotions-bert/notebook
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# Emotion Analyzer Bert
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Fine-tuned [BERT-base-uncased](https://huggingface.co/bert-base-uncased) on [GoEmotions](https://huggingface.co/datasets/go_emotions) for multi-label classification (28 emotions). This updated version includes improved Macro F1, ONNX support for efficient inference, and visualizations for better interpretability.
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