distilbert-base-multilingual-cased-finetuned

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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.

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

Framework versions

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