
# 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)

```
{"language": ["ar"], "tags": ["HARD"], "datasets": ["HARD"], "widget": [{"text": "\u062c\u064a\u062f. \u0627\u0644\u0645\u0643\u0627\u0646 \u062c\u0645\u064a\u0644 \u0648\u0647\u0627\u062f\u064a\u0621. \u0643\u0644 \u0634\u064a \u062c\u064a\u062f \u0648\u0646\u0638\u064a\u0641"}, {"text": "\u0627\u0633\u062a\u063a\u0631\u0628 \u062a\u0642\u064a\u064a\u0645 \u0627\u0644\u0641\u0646\u062f\u0642 \u0643\u062e\u0645\u0633 \u0646\u062c\u0648\u0645\u201d. \u0644\u0627 \u0634\u064a. \u064a\u0633\u062a\u062d\u0642"}]}
transformers pytorch bert text-classification HARD ar dataset:HARD autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #HARD #ar #dataset-HARD #autotrain_compatible #endpoints_compatible #region-us 

BERT-ASTD Balanced
==================


Arabic version bert model fine tuned on Hotel Arabic Reviews dataset from URL (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
-------



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:



TAGS
#transformers #pytorch #bert #text-classification #HARD #ar #dataset-HARD #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #bert #text-classification #HARD #ar #dataset-HARD #autotrain_compatible #endpoints_compatible #region-us 
