---
language:
- ar
pipeline_tag: text-generation
---
# Model Card for Model ID

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This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## Model Details

### Model Description

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### Model Sources [optional]

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## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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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

Use the code below 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="<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

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
[More Information Needed]

## Training Details

### Training Data

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### Training Procedure 

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### 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

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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## Glossary [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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