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aragpt2-mega-pos-msa

Text Generation
Transformers
PyTorch
Arabic
gpt2
text-generation-inference
Inference Endpoints
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aragpt2-mega-pos-msa / README.md
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metadata
language:
  - ar
pipeline_tag: text-generation

Model Card for Model ID

This modelcard aims to be a base template for new models. It has been generated using this raw template.

Model Details

Model Description

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Uses

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Out-of-Scope Use

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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 } 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="", end="")

print(answer_pose)# تعلم : تعلم : Verb من : من : Relative pronoun أخطائك : اخطا : Noun ك : Personal pronunction

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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Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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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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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Software

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