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language: |
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- ar |
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datasets: |
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- HARD |
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tags: |
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- HARD |
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widget: |
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- text: "جيد. المكان جميل وهاديء. كل شي جيد ونظيف" |
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- text: "استغرب تقييم الفندق كخمس نجوم”. لا شي. يستحق" |
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--- |
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# BERT-ASTD Balanced |
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Arabic version bert model fine tuned on Hotel Arabic Reviews dataset from booking.com (HARD) dataset balanced version to identify sentiments opinion in Arabic language. |
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## Data |
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The model were fine-tuned on ~93000 book reviews in arabic using bert large arabic |
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Dataset: |
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- Train 70% |
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- Validation: 10% |
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- Test: 20% |
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## Results |
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| class | precision | recall | f1-score | Support | |
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|----------|-----------|--------|----------|---------| |
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| 0 | 0.9733 | 0.9547 | 0.9639 | 10570 | |
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| 1 | 0.9555 | 0.9738 | 0.9646 | 10570 | |
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| Accuracy | | | 0.9642 | 21140 | |
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## How to use |
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You can use these models by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this: |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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model_name="mofawzy/Bert-hard-balanced" |
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model = AutoModelForSequenceClassification.from_pretrained(model_name,num_labels=2) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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``` |
