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
Upload optimized_thresholds.json for updated GoEmotions BERT model
Browse files
optimized_thresholds.json
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[0.45000000000000007, 0.45000000000000007, 0.45000000000000007, 0.3500000000000001, 0.3500000000000001, 0.45000000000000007, 0.45000000000000007, 0.40000000000000013, 0.40000000000000013, 0.3500000000000001, 0.3500000000000001, 0.5500000000000002, 0.5000000000000001, 0.40000000000000013, 0.30000000000000004, 0.5500000000000002, 0.30000000000000004, 0.45000000000000007, 0.5000000000000001, 0.40000000000000013, 0.40000000000000013, 0.40000000000000013, 0.45000000000000007, 0.30000000000000004, 0.3500000000000001, 0.45000000000000007, 0.3500000000000001, 0.40000000000000013]
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