Create README.md

README.md

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