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--- |
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language: |
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- fa |
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- multilingual |
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thumbnail: "https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg" |
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tags: |
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- multiple-choice |
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- wikibert |
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- persian |
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- farsi |
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pipeline_tag: text-classification |
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license: "CC BY-NC-SA 4.0" |
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datasets: |
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- parsinlu |
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metrics: |
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- accuracy |
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--- |
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# Multiple-Choice Question Answering (مدل برای پاسخ به سوالات چهار جوابی) |
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This is a wikibert-based model for multiple-choice question answering. |
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Here is an example of how you can run this model: |
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```python |
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from typing import List |
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import torch |
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from transformers import AutoConfig, AutoModelForMultipleChoice, AutoTokenizer |
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model_name = "persiannlp/wikibert-base-parsinlu-multiple-choice" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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config = AutoConfig.from_pretrained(model_name) |
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model = AutoModelForMultipleChoice.from_pretrained(model_name, config=config) |
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def run_model(question: str, candicates: List[str]): |
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assert len(candicates) == 4, "you need four candidates" |
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choices_inputs = [] |
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for c in candicates: |
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text_a = "" # empty context |
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text_b = question + " " + c |
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inputs = tokenizer( |
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text_a, |
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text_b, |
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add_special_tokens=True, |
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max_length=128, |
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padding="max_length", |
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truncation=True, |
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return_overflowing_tokens=True, |
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) |
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choices_inputs.append(inputs) |
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input_ids = torch.LongTensor([x["input_ids"] for x in choices_inputs]) |
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output = model(input_ids=input_ids) |
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print(output) |
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return output |
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run_model(question="وسیع ترین کشور جهان کدام است؟", candicates=["آمریکا", "کانادا", "روسیه", "چین"]) |
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run_model(question="طامع یعنی ؟", candicates=["آزمند", "خوش شانس", "محتاج", "مطمئن"]) |
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run_model( |
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question="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 ", |
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candicates=["روز اول", "روز دوم", "روز سوم", "هیچکدام"]) |
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``` |
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For more details, visit this page: https://github.com/persiannlp/parsinlu/ |