
# BERT-ASTD Balanced
Arabic version bert model fine tuned on ASTD dataset balanced version to identify twitter sentiments in Arabic language MSA dialect .

## Data
The model were fine-tuned on ~1330 tweet in Arabic language.


## Results
| class    | precision | recall | f1-score | Support |
|----------|-----------|--------|----------|---------|
| 0        | 0.9328    | 0.9398 | 0.9363   | 133     |
| 1        | 0.9394    | 0.9323 | 0.9358   | 133     |
| Accuracy |           |        | 0.9361   | 266     |





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

```

{"language": ["ar"], "tags": ["ASTD"], "datasets": ["ASTD"], "widget": [{"text": "\u0627\u0644\u0639\u0646\u0641 \u0648\u0627\u0644\u0642\u062a\u0644 \u0641\u064a \u0645\u062d\u064a\u0637 \u0627\u0644\u0639\u0627\u0644\u0645 \u0641\u064a \u0632\u064a\u0627\u062f\u0647 \u064a\u0648\u0645\u064a\u0627"}, {"text": "\u0627\u0644\u0635\u062f\u0627\u0642\u0647 \u062a\u0632\u0631\u0639 \u0627\u0644\u062d\u064a\u0627\u0647 \u0627\u0632\u0647\u0627\u0631\u0627"}]}
transformers pytorch tf bert text-classification ASTD ar dataset:ASTD autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #bert #text-classification #ASTD #ar #dataset-ASTD #autotrain_compatible #endpoints_compatible #region-us 

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


Arabic version bert model fine tuned on ASTD dataset balanced version to identify twitter sentiments in Arabic language MSA dialect .


Data
----


The model were fine-tuned on ~1330 tweet in Arabic language.


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 #tf #bert #text-classification #ASTD #ar #dataset-ASTD #autotrain_compatible #endpoints_compatible #region-us 

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