metadata

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

distilbert-base-multilingual-cased-finetuned

This model is a fine-tuned version of 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.

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

Esmail275
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distilbert-base-multilingual-cased-finetuned