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---
language:
- ar
pipeline_tag: text-generation
---
# Model Card for Model ID
## Model Details
### Model Description
- **Language(s) (NLP):** [Arabic]
- **Finetuned from model :** [aragpt2-mega](https://huggingface.co/aubmindlab/aragpt2-mega)
## Uses
1. pose tagging for arabic language and it may use for other languages
2. The model can be helpful for the arabic langauge students/researchers, since it provide the sentence anaylsis (اعراب الجملة ) in the context.
3. arabic word toknizer
4. it may use for translate the arabic dailects to MSA
## Main Labels
{'حرف جر': 'preposition',
'اسم': 'noun',
'اسم علم': 'proper noun',
'لام التعريف': 'determiner',
'صفة': 'adjective',
'ضمير': 'personal pronoun',
'فعل': 'verb',
'حرف عطف': 'conjunction',
'اسم موصول': 'relative pronoun',
'حرف نفي': 'negative particle',
'حروف مقطعة': 'quranic initials',
'اسم اشارة': 'demonstrative pronoun',
'حرف استئنافية': 'resumption',
'حرف نصب': 'accusative particle',
'حرف تسوية': 'equalization particle',
'حرف حال': 'circumstantial particle',
'أداة حصر': 'restriction particle',
'ظرف زمان': 'time adverb',
'حرف نهي': 'prohibition particle',
'حرف كاف': 'preventive particle',
'حرف ابتداء': 'inceptive particle',
'حرف زائد': 'supplemental particle',
'حرف استدراك': 'amendment particle',
'حرف مصدري': 'subordinating conjunction',
'حرف استفهام': 'interrogative particle',
'ظرف مكان': 'location adverb',
'حرف شرط': 'conditional particle',
'لام التوكيد': 'emphatic',
'حرف نداء': 'vocative particle',
'حرف واقع في جواب الشرط': 'result particle',
'حرف تفصيل': 'explanation particle',
'أداة استثناء': 'exceptive particle',
'حرف سببية': 'particle of cause',
'التوكيد - النون الثقيلة': 'heavy noon emphesis',
'حرف استقبال': 'future particle',
'حرف تحقيق': 'particle of certainty',
'لام التعليل': 'purpose',
'حرف جواب': 'answer particle',
'حرف اضراب': 'retraction particle',
'حرف تحضيض': 'exhortation particle',
'حرف تفسير': 'particle of interpretation',
'لام الامر': 'imperative',
'واو المعية': 'comitative particle',
'حرف فجاءة': 'surprise particle',
'حرف ردع': 'aversion particle',
'اسم فعل أمر': 'imperative verbal noun'}
## How to Get Started with the Model
```python
from transformers import GPT2Tokenizer
from pyarabic.araby import strip_diacritics,strip_tatweel
from arabert.aragpt2.grover.modeling_gpt2 import GPT2LMHeadModel
from transformers import pipeline
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")
generator = pipeline("text-generation",model=model,tokenizer=tokenizer,device=0)
def generate(text):
prompt = f'<|startoftext|>Instruction: {text}<|pad|>Answer:'
pred_text= generator(prompt,
pad_token_id=tokenizer.eos_token_id,
num_beams=20,
max_length=256,
#min_length = 200,
do_sampling=False,
top_p=0.5,
top_k=1,
repetition_penalty = 3.0,
# temperature=0.8,
no_repeat_ngram_size = 3)[0]['generated_text']
try:
pred_sentiment = re.findall("Answer:(.*)", pred_text,re.S)[-1]
except:
pred_sentiment = "None"
return pred_sentiment
text='تعلَّمْ من أخطائِكَ'
generate(strip_tatweel(strip_diacritics(text)))
#' تعلم ( تعلم : فعل ) من ( من : حرف جر ) أخطائك ( اخطاء : اسم ، ك : ضمير )'
```
### Results
Epoch 1
Training Loss 0.108500
Validation Loss 0.082612
## Model Card Contact
[[email protected]] |