LABEL_0 LABEL_1 LABEL_2
---
language:
- ar

datasets:
 - ArSentD-LEV

tags:
 - ArSentD-LEV


widget:
- text: "يهدي الله من يشاء"
- text: "الاسلوب قذر وقمامه"

---

# bert-arsentd-lev
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.

         

## 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)

```
