
# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)

This is a mT5-based model for multiple-choice question answering. 
Here is an example of how you can run this model: 

```python 
from transformers import MT5ForConditionalGeneration, MT5Tokenizer

model_size = "small"
model_name = f"persiannlp/mt5-{model_size}-parsinlu-arc-comqa-obqa-multiple-choice"
tokenizer = MT5Tokenizer.from_pretrained(model_name)
model = MT5ForConditionalGeneration.from_pretrained(model_name)


def run_model(input_string, **generator_args):
    input_ids = tokenizer.encode(input_string, return_tensors="pt")
    res = model.generate(input_ids, **generator_args)
    output = tokenizer.batch_decode(res, skip_special_tokens=True)
    print(output)
    return output


run_model("وسیع ترین کشور جهان کدام است؟ <sep> آمریکا <sep> کانادا <sep> روسیه <sep> چین")
run_model("طامع یعنی ؟ <sep> آزمند <sep> خوش شانس <sep> محتاج <sep> مطمئن")
run_model(
    "زمینی به ۳۱ قطعه متساوی مفروض شده است و هر روز مساحت آماده شده برای احداث، دو برابر مساحت روز قبل است.اگر پس از (۵ روز) تمام زمین آماده شده باشد، در چه روزی یک قطعه زمین آماده شده <sep> روز اول <sep> روز دوم <sep> روز سوم <sep> هیچکدام")

```


For more details, visit this page: https://github.com/persiannlp/parsinlu/ 

{"language": ["fa", "multilingual"], "license": "cc-by-nc-sa-4.0", "tags": ["multiple-choice", "mt5", "persian", "farsi"], "datasets": ["parsinlu", "commonsenseqa", "arc", "openbookqa"], "metrics": ["accuracy"], "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg"}
transformers pytorch jax t5 text2text-generation multiple-choice mt5 persian farsi fa multilingual dataset:parsinlu dataset:commonsenseqa dataset:arc dataset:openbookqa license:cc-by-nc-sa-4.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #jax #t5 #text2text-generation #multiple-choice #mt5 #persian #farsi #fa #multilingual #dataset-parsinlu #dataset-commonsenseqa #dataset-arc #dataset-openbookqa #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)

This is a mT5-based model for multiple-choice question answering. 
Here is an example of how you can run this model: 




For more details, visit this page: URL 

# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)

This is a mT5-based model for multiple-choice question answering. 
Here is an example of how you can run this model: 




For more details, visit this page: URL
TAGS
#transformers #pytorch #jax #t5 #text2text-generation #multiple-choice #mt5 #persian #farsi #fa #multilingual #dataset-parsinlu #dataset-commonsenseqa #dataset-arc #dataset-openbookqa #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)

This is a mT5-based model for multiple-choice question answering. 
Here is an example of how you can run this model: 




For more details, visit this page: URL
passage: TAGS
#transformers #pytorch #jax #t5 #text2text-generation #multiple-choice #mt5 #persian #farsi #fa #multilingual #dataset-parsinlu #dataset-commonsenseqa #dataset-arc #dataset-openbookqa #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی)

This is a mT5-based model for multiple-choice question answering. 
Here is an example of how you can run this model: 




For more details, visit this page: URL