---
language:
- en
license: mit
tags:
- generated_from_keras_callback
datasets:
- emotion
metrics:
- f1
base_model: microsoft/MiniLM-L12-H384-uncased
model-index:
- name: laxsvips/minilm-finetuned-emotion
  results: []
---

<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->

# laxsvips/minilm-finetuned-emotion

This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the Hugging Face emotion (https://huggingface.co/datasets/emotion) dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1168
- Train Accuracy: 0.9446
- Validation Loss: 0.1709
- Validation Accuracy: 0.9350
- Epoch: 4

## Model description

# MiniLM: Small and Fast Pre-trained Models for Language Understanding and Generation

MiniLM is a distilled model from the paper "MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers".

## Intended uses & limitations

This model has been created as a learning guide on:
- How to implement a text classification model using Hugging Face Transformers in TensorFlow
- How to handle imbalanced class distribution

# How to use the model
```
from transformers import pipeline

model_cpt = "laxsvips/minilm-finetuned-emotion"
pipe = pipeline("text-classification", model=model_cpt)
predicted_scores = pipe("I am so glad you could help me")
print(predicted_scores)
````
The results:
```
[[{'label': 'sadness', 'score': 0.003758953418582678},
  {'label': 'joy', 'score': 0.9874302744865417},
  {'label': 'love', 'score': 0.00610917154699564},
  {'label': 'anger', 'score': 9.696640336187556e-05},
  {'label': 'fear', 'score': 0.0006420552381314337},
  {'label': 'surprise', 'score': 0.00196251692250371}]]
```

## Training and evaluation data

[Emotion](https://huggingface.co/datasets/emotion)

Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. 

## Training procedure

Refer to the [Colab](https://colab.research.google.com/github/laxmiharikumar/transformers/blob/main/TextClassification_Emotions_TF.ipynb) notebook

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: 'Adam', 
- learning_rate': 5e-05, 
- batch_size : 64
- num_epochs - 5

### Training results

| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.9485     | 0.5543         | 0.8404          | 0.6870              | 0     |
| 0.4192     | 0.8347         | 0.3450          | 0.9040              | 1     |
| 0.2132     | 0.9178         | 0.2288          | 0.9240              | 2     |
| 0.1465     | 0.9364         | 0.1838          | 0.9295              | 3     |
| 0.1168     | 0.9446         | 0.1709          | 0.9350              | 4     |

### Evaluation Metrics
```
{'accuracy': 0.935,
 'precision': 0.937365614416424,
 'recall': 0.935,
 'f1_score': 0.9355424419858925}
```

### Framework versions

- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.9.0
- Tokenizers 0.13.2

### References
1. https://www.youtube.com/watch?v=u--UVvH-LIQ
2. https://huggingface.co/docs/transformers
3. https://www.tensorflow.org/api_docs/python/tf