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--- |
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language: |
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- en |
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thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4 |
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tags: |
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- text-classification |
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- emotion |
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- pytorch |
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license: apache-2.0 |
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datasets: |
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- emotion |
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metrics: |
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- Accuracy, F1 Score |
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--- |
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# bert-base-uncased-emotion |
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## Model description: |
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`bert-base-uncased` finetuned on the emotion dataset using HuggingFace Trainer. |
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``` |
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learning rate 2e-5, |
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batch size 64, |
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num_train_epochs=8, |
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``` |
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## How to Use the model: |
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```python |
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from transformers import pipeline |
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classifier = pipeline("sentiment-analysis",model='bhadresh-savani/bert-base-uncased-emotion') |
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prediction = classifier("I love using transformers. The best part is wide range of support and its easy to use") |
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``` |
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## Dataset: |
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[Twitter-Sentiment-Analysis](https://huggingface.co/nlp/viewer/?dataset=emotion). |
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## Training procedure |
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[Colab Notebook](https://github.com/bhadreshpsavani/ExploringSentimentalAnalysis/blob/main/SentimentalAnalysisWithDistilbert.ipynb) |
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## Eval results |
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``` |
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{ |
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'test_accuracy': 0.9355, |
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'test_f1': 0.9354074792391709, |
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'test_loss': 0.18557891249656677, |
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'test_runtime': 11.0092, |
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'test_samples_per_second': 181.666, |
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'test_steps_per_second': 2.907 |
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} |
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``` |
