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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 with below training parameters |
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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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## Model Performance Comparision on Emotion Dataset from Twitter: |
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| Model | Accuracy | F1 Score | Test Loss | Test Sample per Second | |
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| --- | --- | --- | --- | --- | |
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| Distilbert-base-uncased | 93.8 | 93.79 | 0.1472 | 398.69 | |
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| Bert-base-uncased | 94.05 | 94.06 | 0.1577 | 190.152 | |
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| Roberta-base | 93.95 | 93.97 | 0.1437 | 195.639 | |
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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("text-classification",model='bhadresh-savani/bert-base-uncased-emotion', return_all_scores=True) |
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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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print(prediction) |
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""" |
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output: |
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[[ |
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{'label': 'sadness', 'score': 0.0005138228880241513}, |
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{'label': 'joy', 'score': 0.9972520470619202}, |
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{'label': 'love', 'score': 0.0007443308713845909}, |
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{'label': 'anger', 'score': 0.0007404946954920888}, |
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{'label': 'fear', 'score': 0.00032938539516180754}, |
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{'label': 'surprise', 'score': 0.0004197491507511586} |
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]] |
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""" |
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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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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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'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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## Reference: |
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* [Natural Language Processing with Transformer By Lewis Tunstall, Leandro von Werra, Thomas Wolf](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/) |
