README.md

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

<!-- Provide a quick summary of what the model is/does. -->


## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **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](https://huggingface.co/aubmindlab/aragpt2-mega)



## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
1. The model can be helpful for the arabic langauge students/researchers, since it provide the full sentence anaylsis (اعراب الجملة ) in arabic language.
2. 


### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
1. This model can't be use for grammar check, since it dail with high level of arabic correct sentence as input
2. Don't use arabic dailects in input sentence.
3. 
4. 

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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

```python
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

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

### Training Procedure 

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### 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]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Data Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact

[akram.alsubari87@gmail.com]