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--- |
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language: |
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- ar |
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datasets: |
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- ArSentD-LEV |
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tags: |
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- ArSentD-LEV |
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widget: |
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- text: "يهدي الله من يشاء" |
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- text: "الاسلوب قذر وقمامه" |
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--- |
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# bert-arsentd-lev |
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Arabic version bert model fine tuned on ArSentD-LEV dataset |
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## Data |
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The model were fine-tuned on ~4000 sentence from twitter multiple dialect and five classes we used 3 out of 5 int the experiment. |
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## Results |
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| class | precision | recall | f1-score | Support | |
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|----------|-----------|--------|----------|---------| |
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| 0 | 0.8211 | 0.8080 | 0.8145 | 125 | |
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| 1 | 0.7174 | 0.7857 | 0.7500 | 84 | |
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| 2 | 0.6867 | 0.6404 | 0.6628 | 89 | |
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| Accuracy | | | 0.7517 | 298 | |
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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-arsentd-lev" |
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model = AutoModelForSequenceClassification.from_pretrained(model_name,num_labels=3) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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``` |
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