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---
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 |