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
Commit ·
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Parent(s): 305e9fa
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README.md
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## Eval results
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```
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{
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'test_accuracy': 0.
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'test_f1': 0.
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'test_loss': 0.
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'test_runtime':
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'test_samples_per_second':
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'test_steps_per_second':
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}
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```
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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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'test_loss': 0.15769127011299133,
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'test_runtime': 10.5179,
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'test_samples_per_second': 190.152,
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'test_steps_per_second': 3.042
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}
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```
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