readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: minilm-finetuned-emotionclassification
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # minilm-finetuned-emotionclassification
  
  This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.0554
  - F1 Score: 0.6732
  
  ## Model description
  
  The base model used is Microsoft MiniLM-L12-H384-uncased which is finetuned on [GoEmotions dataset](https://huggingface.co/datasets/go_emotions) available on huggingface.
  
  With this model, you can classify emotions in English text data. The model predicts 10 basic emotions:
  
  1) anger 🤬
  2) love ❤️
  3) fear 😨
  4) joy 😀
  5) excitement 😄
  6) sadness 😭
  7) surprise 😲
  8) gratitude 😊
  9) curiosity 🤔
  10 caring 
  
  ## Intended uses & limitations
  
  The model can be used to detect emotions from text/ documents which can be used for analysis contextual emotional analysis of the documents
  
  ## Training and evaluation data
  
  The dataset used for Training and Evaluation is [GoEmotions dataset](https://huggingface.co/datasets/go_emotions)
  and in this, we have used 10 emotion variables.
  
  {0:'sadness',1:'joy',2:'love',3:'anger',4:'fear',5:'surprise',6:'excitement',7:'gratitude',8:'curiosity',9:'caring'}
  
  
  ## How to use the model
  Here is how to use this model to extract the emotions from the given text in PyTorch:
  
  ```python
  >>> from transformers import pipeline
  >>> model_ckpt ="sid321axn/minilm-finetuned-emotionclassification"
  >>> pipe = pipeline("text-classification",model=model_ckpt)
  >>> pipe("I am really excited about second part of Brahmastra Movie")
  
  [{'label': 'excitement', 'score': 0.7849715352058411}]
  ```
  
  ## Training procedure
  The training we have done by following this [video](https://www.youtube.com/watch?v=u--UVvH-LIQ) on Youtube by huggingface
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 64
  - eval_batch_size: 64
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | F1 Score |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 1.1659        | 1.0   | 539  | 1.1419          | 0.6347   |
  | 1.0719        | 2.0   | 1078 | 1.0789          | 0.6589   |
  | 0.9893        | 3.0   | 1617 | 1.0537          | 0.6666   |
  | 0.9296        | 4.0   | 2156 | 1.0366          | 0.6729   |
  | 0.8763        | 5.0   | 2695 | 1.0359          | 0.6774   |
  | 0.8385        | 6.0   | 3234 | 1.0484          | 0.6693   |
  | 0.8085        | 7.0   | 3773 | 1.0478          | 0.6758   |
  | 0.7842        | 8.0   | 4312 | 1.0488          | 0.6741   |
  | 0.7608        | 9.0   | 4851 | 1.0538          | 0.6749   |
  | 0.7438        | 10.0  | 5390 | 1.0554          | 0.6732   |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.2
  