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--- |
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license: apache-2.0 |
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base_model: distilbert/distilbert-base-multilingual-cased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: distilbert-base-multilingual-cased-finetuned |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: emotone_ar |
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type: emotion |
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config: split |
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split: validation |
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args: split |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.6643 |
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- name: F1 |
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type: f1 |
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value: 0.6611 |
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datasets: |
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- emotone-ar-cicling2017/emotone_ar |
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language: |
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- ar |
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pipeline_tag: text-classification |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# distilbert-base-multilingual-cased-finetuned |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6740 |
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- Accuracy: 0.6643 |
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- F1: 0.6611 |
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## Model description |
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The model has been trained to classify text inputs into distinct emotional categories based on the fine-tuned understanding of the emotions dataset. |
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The fine-tuned model has demonstrated high accuracy and F1 scores on the evaluation set. |
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## Intended uses & limitations |
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#### Intended Uses |
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- Sentiment analysis |
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- Emotional classification in text |
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- Emotion-based recommendation systems |
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#### Limitations |
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- May show biases based on the training dataset |
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- Optimized for emotional classification and may not cover nuanced emotional subtleties |
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## Training and evaluation data |
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Emotions dataset with labeled emotional categories [here](https://huggingface.co/datasets/emotone-ar-cicling2017/emotone_ar). |
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#### The emotional categories are as follows: |
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- LABEL_0 : none |
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- LABEL_1 : anger |
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- LABEL_2 : joy |
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- LABEL_3 : sadness |
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- LABEL_4 : love |
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- LABEL_5 : sympathy |
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- LABEL_6 : surprise |
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- LABEL_7 : fear |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| 0.4725 | 1.0 | 252 | 1.0892 | 0.6604 | 0.6625 | |
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| 0.3392 | 2.0 | 504 | 1.2096 | 0.6594 | 0.6649 | |
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| 0.2575 | 3.0 | 756 | 1.2745 | 0.6723 | 0.6706 | |
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| 0.1979 | 4.0 | 1008 | 1.3719 | 0.6713 | 0.6666 | |
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| 0.1757 | 5.0 | 1260 | 1.4239 | 0.6723 | 0.6652 | |
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| 0.1414 | 6.0 | 1512 | 1.5074 | 0.6663 | 0.6666 | |
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| 0.1073 | 7.0 | 1764 | 1.5703 | 0.6783 | 0.6722 | |
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| 0.0812 | 8.0 | 2016 | 1.6218 | 0.6673 | 0.6638 | |
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| 0.0615 | 9.0 | 2268 | 1.6676 | 0.6693 | 0.6642 | |
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| 0.0531 | 10.0 | 2520 | 1.6740 | 0.6643 | 0.6611 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
