---
language:
- ar

datasets:
 - HARD

tags:
 - HARD

widget:
- text: "جيد. المكان جميل وهاديء. كل شي جيد ونظيف"
- text: "استغرب تقييم الفندق كخمس نجوم”. لا شي. يستحق"

---

# BERT-ASTD Balanced
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.

## Data
The model were fine-tuned on ~93000 book reviews in arabic using bert large arabic

Dataset:
- Train 70%
- Validation: 10%
- Test: 20%


## Results
| class    | precision | recall | f1-score | Support |
|----------|-----------|--------|----------|---------|
| 0        | 0.9733    | 0.9547 | 0.9639   | 10570   |
| 1        | 0.9555    | 0.9738 | 0.9646   | 10570   |
| Accuracy |           |        | 0.9642   | 21140   |





## 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-hard-balanced"
model = AutoModelForSequenceClassification.from_pretrained(model_name,num_labels=2)
tokenizer = AutoTokenizer.from_pretrained(model_name)

```