|
--- |
|
language: |
|
- en |
|
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4 |
|
tags: |
|
- text-classification |
|
- emotion |
|
- pytorch |
|
license: apache-2.0 |
|
datasets: |
|
- emotion |
|
metrics: |
|
- Accuracy, F1 Score |
|
--- |
|
# bert-base-uncased-emotion |
|
|
|
## Model description: |
|
`bert-base-uncased` finetuned on the emotion dataset using HuggingFace Trainer with below training parameters |
|
``` |
|
learning rate 2e-5, |
|
batch size 64, |
|
num_train_epochs=8, |
|
``` |
|
|
|
## Model Performance Comparision on Emotion Dataset from Twitter: |
|
|
|
| Model | Accuracy | F1 Score | Test Sample per Second | |
|
| --- | --- | --- | --- | |
|
| [Distilbert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion) | 93.8 | 93.79 | 398.69 | |
|
| [Bert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/bert-base-uncased-emotion) | 94.05 | 94.06 | 190.152 | |
|
| [Roberta-base-emotion](https://huggingface.co/bhadresh-savani/roberta-base-emotion) | 93.95 | 93.97| 195.639 | |
|
| [Albert-base-v2-emotion](https://huggingface.co/bhadresh-savani/albert-base-v2-emotion) | 93.6 | 93.65 | 182.794 | |
|
|
|
## How to Use the model: |
|
```python |
|
from transformers import pipeline |
|
classifier = pipeline("text-classification",model='bhadresh-savani/bert-base-uncased-emotion', return_all_scores=True) |
|
prediction = classifier("I love using transformers. The best part is wide range of support and its easy to use", ) |
|
print(prediction) |
|
|
|
""" |
|
output: |
|
[[ |
|
{'label': 'sadness', 'score': 0.0005138228880241513}, |
|
{'label': 'joy', 'score': 0.9972520470619202}, |
|
{'label': 'love', 'score': 0.0007443308713845909}, |
|
{'label': 'anger', 'score': 0.0007404946954920888}, |
|
{'label': 'fear', 'score': 0.00032938539516180754}, |
|
{'label': 'surprise', 'score': 0.0004197491507511586} |
|
]] |
|
""" |
|
``` |
|
|
|
## Dataset: |
|
[Twitter-Sentiment-Analysis](https://huggingface.co/nlp/viewer/?dataset=emotion). |
|
|
|
## Training procedure |
|
[Colab Notebook](https://github.com/bhadreshpsavani/ExploringSentimentalAnalysis/blob/main/SentimentalAnalysisWithDistilbert.ipynb) |
|
follow the above notebook by changing the model name from distilbert to bert |
|
|
|
## Eval results |
|
```json |
|
{ |
|
'test_accuracy': 0.9405, |
|
'test_f1': 0.9405920712282673, |
|
'test_loss': 0.15769127011299133, |
|
'test_runtime': 10.5179, |
|
'test_samples_per_second': 190.152, |
|
'test_steps_per_second': 3.042 |
|
} |
|
``` |
|
|
|
## Reference: |
|
* [Natural Language Processing with Transformer By Lewis Tunstall, Leandro von Werra, Thomas Wolf](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/) |
