metadata
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
Albert-base-v2-emotion
Model description:
Albert is A Lite BERT architecture that has significantly fewer parameters than a traditional BERT architecture.
[Albert-base-v2](https://huggingface.co/albert-base-v2) finetuned on the emotion dataset using HuggingFace Trainer with below Hyperparameters
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 | 93.8 | 93.79 | 398.69 |
| Bert-base-uncased-emotion | 94.05 | 94.06 | 190.152 |
| Roberta-base-emotion | 93.95 | 93.97 | 195.639 |
| Albert-base-v2-emotion | 93.6 | 93.65 | 182.794 |
How to Use the model:
from transformers import pipeline
classifier = pipeline("text-classification",model='bhadresh-savani/albert-base-v2-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': 0.0006792712374590337},
{'label': 'joy', 'score': 0.9959300756454468},
{'label': 'love', 'score': 0.0009452480007894337},
{'label': 'anger', 'score': 0.0018055217806249857},
{'label': 'fear', 'score': 0.00041110432357527316},
{'label': 'surprise', 'score': 0.0002288572577526793}
]]
"""
Dataset:
Training procedure
Eval results
{'test_accuracy': 0.936,
'test_f1': 0.9365658988006296,
'test_loss': 0.15278364717960358,
'test_runtime': 10.9413,
'test_samples_per_second': 182.794,
'test_steps_per_second': 2.925}