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--- |
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language: |
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- ar |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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base_model: google-bert/bert-base-multilingual-uncased |
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datasets: |
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- labr |
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widget: |
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- text: كتاب يستحق القراءة |
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example_title: مثال 1 |
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- text: ما عجبني بنوب |
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example_title: مثال 2 |
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- text: لم يعجبني أبدا |
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example_title: مثال 3 |
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- text: أنصح وبشدة قراءة الكتاب خصوصا لمن لديه اهتمام في العلوم الاجتماعية |
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example_title: مثال 4 |
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- text: ماشي حالو بعطيه 4 من 10 |
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example_title: مثال 5 |
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model-index: |
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- name: Arabic-Book-Review-Sentiment-Assessment |
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results: [] |
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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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# Arabic-Book-Review-Sentiment-Assessment |
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This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) on [labr](https://huggingface.co/datasets/labr) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.5290 |
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## Model description |
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The purpose of this model is to analyze Arabic review texts and predict the appropriate rating for them, based on the sentiment and content of the review. |
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This can be particularly useful in tasks such as sentiment analysis, customer feedback analysis, or any application where understanding the sentiment conveyed in an Arabic textual review is important. |
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The labels associated with the ratings are `LABEL_0`, `LABEL_1`, `LABEL_2`, `LABEL_3`, and `LABEL_4`. These labels can be interpreted as follows: |
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- `LABEL_0`: Poor |
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- `LABEL_1`: Fair |
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- `LABEL_2`: Good |
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- `LABEL_3`: Very Good |
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- `LABEL_4`: Excellent |
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## Intended uses & limitations |
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While the model performs well with formal Arabic text (Examples 1, 3, and 4), it may struggle with slang or informal language, occasionally assigning higher ratings than expected (Example 2). |
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Additionally, the model is not capable of interpreting verbally given ratings (Example 5). |
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Users should be aware of these limitations and provide context-appropriate input for optimal performance. |
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## Training and evaluation data |
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More information needed |
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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: 8 |
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- eval_batch_size: 8 |
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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: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 1.0459 | 1.0 | 1470 | 1.5290 | |
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| 0.7622 | 2.0 | 2940 | 1.6278 | |
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| 0.8204 | 3.0 | 4410 | 1.5341 | |
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| 0.6592 | 4.0 | 5880 | 1.8030 | |
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| 0.4976 | 5.0 | 7350 | 1.9638 | |
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### Framework versions |
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- Transformers 4.39.1 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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