arabartsummarization
Model description
The model can be used as follows:
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."
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
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
2.784 | 1.0 | 9380 | 2.3820 |
2.4954 | 2.0 | 18760 | 2.3418 |
2.2223 | 3.0 | 28140 | 2.3394 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
- Downloads last month
- 831
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.