readme: ---
  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)
  
  ```