Arabartsummarization

🚀 arabartsummarization

This model is designed for Arabic text summarization, news title generation, and paraphrasing, leveraging AraBERT and BERT2BERT architectures.

🚀 Quick Start

The model can be used as follows:

Basic Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
from arabert.preprocess import ArabertPreprocessor

model_name="abdalrahmanshahrour/arabartsummarization"
preprocessor = ArabertPreprocessor(model_name="")

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
pipeline = pipeline("text2text-generation",model=model,tokenizer=tokenizer)

text = "شهدت مدينة طرابلس، مساء أمس الأربعاء، احتجاجات شعبية وأعمال شغب لليوم الثالث على التوالي، وذلك بسبب تردي الوضع المعيشي والاقتصادي. واندلعت مواجهات عنيفة وعمليات كر وفر ما بين الجيش اللبناني والمحتجين استمرت لساعات، إثر محاولة فتح الطرقات المقطوعة، ما أدى إلى إصابة العشرات من الطرفين."
text = preprocessor.preprocess(text)

result = pipeline(text,
            pad_token_id=tokenizer.eos_token_id,
            num_beams=3,
            repetition_penalty=3.0,
            max_length=200,
            length_penalty=1.0,
            no_repeat_ngram_size = 3)[0]['generated_text']
result
>>> "تجددت الاشتباكات بين الجيش اللبناني ومحتجين في مدينة طرابلس شمالي لبنان."

✨ Features

• Summarization: Capable of summarizing Arabic texts, including news articles.
• Title Generation: Can generate titles for Arabic news.
• Paraphrasing: Offers paraphrasing capabilities for Arabic text.

📚 Documentation

Validation Metrics

• Loss: 2.3394
• Rouge1: 1.142
• Rouge2: 0.227
• RougeL: 1.124
• RougeLsum: 1.234

Intended uses & limitations

More information needed

Training and evaluation data

42.21K row in total

• Training : 37.52K rows
• Evaluation : 4.69K rows

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Framework versions

• Transformers 4.25.1
• Pytorch 1.13.0+cu116
• Datasets 2.7.1
• Tokenizers 0.13.2

📄 License

This model is licensed under the Apache-2.0 license.