arabert-sentiment-model-MuhannedSh

Model Card for Arabic Sentiment Muhannedsh

Model Details

Model Description

This is a fine-tuned BERT-based Arabic sentiment analysis model, specifically adapted from the aubmindlab/bert-base-arabertv02 model. It has been fine-tuned for binary sentiment classification tasks (positive vs. negative sentiment) and achieves excellent performance on the validation set.

• Developed by: Muhanned Shaheen
• Model type: BERT-based model for sequence classification
• Language(s) (NLP): Arabic
• License: Apache 2.0
• Finetuned from model: aubmindlab/bert-base-arabertv02

Model Sources

• Repository: https://huggingface.co/Muhannedbsh/arabert-sentiment-model-MuhannedSh

Training Metrics

• Training Loss: 0.0315
• Training Accuracy: 99.31%
• Training F1-Score: 99.28%

Validation Metrics

• Validation Loss: 0.2464
• Validation Accuracy: 92.24%
• Validation F1-Score: 92.89%

Uses

Direct Use

This model is intended to be used for binary sentiment analysis tasks in Arabic. It can classify Arabic text into positive or negative sentiment.

Downstream Use

The model can be fine-tuned further for other tasks in Arabic text classification or sentiment analysis.

Out-of-Scope Use

The model is not recommended for tasks involving non-Arabic text or for sentiment analysis with more than two classes.

Bias, Risks, and Limitations

Recommendations

Users should be aware that the model's performance is tied to the data used during fine-tuning. Biases in the dataset could affect predictions.

How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_name = "muhannedshaheen/Arabic_Sentiment_Muhannedsh"

# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Example
text = "هذا المنتج رائع جدا"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
print(outputs.logits)