Text Classification
Transformers
PyTorch
TensorFlow
JAX
Safetensors
English
bert
emotion
Eval Results (legacy)
text-embeddings-inference
Instructions to use bhadresh-savani/bert-base-uncased-emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bhadresh-savani/bert-base-uncased-emotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bhadresh-savani/bert-base-uncased-emotion")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bhadresh-savani/bert-base-uncased-emotion") model = AutoModelForSequenceClassification.from_pretrained("bhadresh-savani/bert-base-uncased-emotion", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
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README.md
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follow the above notebook by changing the model name from distilbert to bert
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## Eval results
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```
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{
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'test_accuracy': 0.9405,
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'test_f1': 0.9405920712282673,
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follow the above notebook by changing the model name from distilbert to bert
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## Eval results
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```json
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{
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'test_accuracy': 0.9405,
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'test_f1': 0.9405920712282673,
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