---
license: apache-2.0
base_model: distilbert/distilbert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-multilingual-cased-finetuned
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: emotone_ar 
      type: emotion
      config: split
      split: validation
      args: split
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6643
    - name: F1
      type: f1
      value: 0.6611
datasets:
- emotone-ar-cicling2017/emotone_ar
language:
- ar
pipeline_tag: text-classification
---

<!-- 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. -->

# distilbert-base-multilingual-cased-finetuned

This model is a fine-tuned version of [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) on Arabic tweets for Emotion detection  dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6740
- Accuracy: 0.6643
- F1: 0.6611

## Model description

The model has been trained to classify text inputs into distinct emotional categories based on the fine-tuned understanding of the emotions dataset. 
The fine-tuned model has demonstrated high accuracy and F1 scores on the evaluation set.

## Intended uses & limitations

#### Intended Uses
- Sentiment analysis
- Emotional classification in text
- Emotion-based recommendation systems

#### Limitations
- May show biases based on the training dataset
- Optimized for emotional classification and may not cover nuanced emotional subtleties

## Training and evaluation data

Emotions dataset with labeled emotional categories [here](https://huggingface.co/datasets/emotone-ar-cicling2017/emotone_ar).

#### The emotional categories are as follows:
  - LABEL_0 :  none
  - LABEL_1 :  anger
  - LABEL_2 :  joy
  - LABEL_3 :  sadness
  - LABEL_4 :  love
  - LABEL_5 :  sympathy
  - LABEL_6 :  surprise
  - LABEL_7 :  fear

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.4725        | 1.0   | 252  | 1.0892          | 0.6604   | 0.6625 |
| 0.3392        | 2.0   | 504  | 1.2096          | 0.6594   | 0.6649 |
| 0.2575        | 3.0   | 756  | 1.2745          | 0.6723   | 0.6706 |
| 0.1979        | 4.0   | 1008 | 1.3719          | 0.6713   | 0.6666 |
| 0.1757        | 5.0   | 1260 | 1.4239          | 0.6723   | 0.6652 |
| 0.1414        | 6.0   | 1512 | 1.5074          | 0.6663   | 0.6666 |
| 0.1073        | 7.0   | 1764 | 1.5703          | 0.6783   | 0.6722 |
| 0.0812        | 8.0   | 2016 | 1.6218          | 0.6673   | 0.6638 |
| 0.0615        | 9.0   | 2268 | 1.6676          | 0.6693   | 0.6642 |
| 0.0531        | 10.0  | 2520 | 1.6740          | 0.6643   | 0.6611 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1