metadata
language:
- ar
pipeline_tag: text-generation
Model Card for Model ID
Model Details
Model Description
- Developed by: [More Information Needed]
- Shared by [optional]: [More Information Needed]
- Model type: [More Information Needed]
- Language(s) (NLP): [Arabic]
- License: [More Information Needed]
- Finetuned from model : aragpt2-mega
Uses
- The model can be helpful for the arabic langauge students/researchers, since it provide the full sentence anaylsis (اعراب الجملة ) in arabic language.
Out-of-Scope Use
- This model can't be use for grammar check, since it dail with high level of arabic correct sentence as input
- Don't use arabic dailects in input sentence.
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
from transformers import GPT2Tokenizer
from arabert.preprocess import ArabertPreprocessor
from arabert.aragpt2.grover.modeling_gpt2 import GPT2LMHeadModel
from pyarabic.araby import strip_tashkeel
import pyarabic.trans
model_name='alsubari/aragpt2-mega-pos-msa'
tokenizer = GPT2Tokenizer.from_pretrained('alsubari/aragpt2-mega-pos-msa')
model = GPT2LMHeadModel.from_pretrained('alsubari/aragpt2-mega-pos-msa').to("cuda")
arabert_prep = ArabertPreprocessor(model_name='aubmindlab/aragpt2-mega')
prml=['اعراب الجملة :', ' صنف الكلمات من الجملة :']
text='تعلَّمْ من أخطائِكَ'
text=arabert_prep.preprocess(strip_tashkeel(text))
generation_args = {
'pad_token_id':tokenizer.eos_token_id,
'max_length': 256,
'num_beams':20,
'no_repeat_ngram_size': 3,
'top_k': 20,
'top_p': 0.1, # Consider all tokens with non-zero probability
'do_sample': True,
'repetition_penalty':2.0
}
##Pose Tagging
input_text = f'<|startoftext|>Instruction: {prml[1]} {text}<|pad|>Answer:'
input_ids = tokenizer.encode(input_text, return_tensors='pt').to("cuda")
output_ids = model.generate(input_ids=input_ids,**generation_args)
output_text = tokenizer.decode(output_ids[0],skip_special_tokens=True).split('Answer:')[1]
answer_pose=pyarabic.trans.delimite_language(output_text, start="<token>", end="</token>")
print(answer_pose)
# <token>تعلم : تعلم</token> : Verb <token>من : من</token> : Relative pronoun <token>أخطائك : اخطا</token> : Noun <token>ك</token> : Personal pronunction
##Arabic Sentence Analysis
input_text = f'<|startoftext|>Instruction: {prml[0]} {text}<|pad|>Answer:'
input_ids = tokenizer.encode(input_text, return_tensors='pt').to("cuda")
output_ids = model.generate(input_ids=input_ids,**generation_args)
output_text = tokenizer.decode(output_ids[0],skip_special_tokens=True).split('Answer:')[1]
print(output_text)
#تعلم : تعلم : فعل ، مفرد المخاطب للمذكر ، فعل مضارع ، مرفوع من : من : حرف جر أخطائك : اخطا : اسم ، جمع المذكر ، مجرور ك : ضمير ، مفرد المتكلم
Training Details
Training Data
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Training Procedure
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Training Hyperparameters
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Evaluation
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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