readme: ---
  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](https://arxiv.org/pdf/1909.11942v6.pdf) 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](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/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': 0.010403595864772797}, 
  {'label': 'joy', 'score': 0.8902180790901184}, 
  {'label': 'love', 'score': 0.042532723397016525}, 
  {'label': 'anger', 'score': 0.041297927498817444}, 
  {'label': 'fear', 'score': 0.011772023513913155}, 
  {'label': 'surprise', 'score': 0.0037756056990474463}
  ]]
  """
  ```
  
  ## Dataset:
  [Twitter-Sentiment-Analysis](https://huggingface.co/nlp/viewer/?dataset=emotion).
  
  ## Training procedure
  [Colab Notebook](https://github.com/bhadreshpsavani/ExploringSentimentalAnalysis/blob/main/SentimentalAnalysisWithDistilbert.ipynb)
  
  ## Eval results
  ```json
  {
  '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
   }
  ```
  
  ## Reference:
  * [Natural Language Processing with Transformer By Lewis Tunstall, Leandro von Werra, Thomas Wolf](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/)