---
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