mbert-base-parsinlu-multiple-choice Open-source Model - Supports Answering Persian Multiple-choice Tasks

Mbert Base Parsinlu Multiple Choice

Developed by persiannlp

An mBERT-based multiple-choice question answering model specifically designed for Persian (Farsi), supporting multiple-choice tasks.

Question Answering System Other#Persian multiple choice questions #mBERT fine-tuning #Four-choice Q&A

Downloads 19

Release Time : 3/2/2022

Model Overview

This model is a multiple-choice question answering model based on the mBERT architecture, primarily used for handling Persian (Farsi) multiple-choice questions. It can select the most appropriate answer from four candidate options.

Model Features

Multilingual support

Based on the mBERT architecture, it supports processing multiple languages including Persian.

Multiple-choice task optimization

Specifically optimized and trained for multiple-choice question answering tasks.

Persian specialization

Specially trained and optimized for Persian (Farsi).

Model Capabilities

Persian text understanding

Multiple-choice question answering

Semantic similarity judgment

Use Cases

Education

Automatic scoring of Persian test questions

Used for automatically evaluating multiple-choice questions in Persian tests.

Language learning

Persian learning assistance

Helps Persian learners understand questions and select the correct answers.

🚀 Multiple-Choice Question Answering

This is a mbert-based model designed for multiple-choice question answering. It offers a practical solution for handling multiple-choice questions, leveraging the power of mbert architecture.

🚀 Quick Start

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

from typing import List
import torch
from transformers import AutoConfig, AutoModelForMultipleChoice, AutoTokenizer

model_name = "persiannlp/mbert-base-parsinlu-multiple-choice"
tokenizer = AutoTokenizer.from_pretrained(model_name)
config = AutoConfig.from_pretrained(model_name)
model = AutoModelForMultipleChoice.from_pretrained(model_name, config=config)


def run_model(question: str, candicates: List[str]):
    assert len(candicates) == 4, "you need four candidates"
    choices_inputs = []
    for c in candicates:
        text_a = ""  # empty context
        text_b = question + " " + c
        inputs = tokenizer(
            text_a,
            text_b,
            add_special_tokens=True,
            max_length=128,
            padding="max_length",
            truncation=True,
            return_overflowing_tokens=True,
        )
        choices_inputs.append(inputs)

    input_ids = torch.LongTensor([x["input_ids"] for x in choices_inputs])
    output = model(input_ids=input_ids)
    print(output)
    return output


run_model(question="وسیع ترین کشور جهان کدام است؟", candicates=["آمریکا", "کانادا", "روسیه", "چین"])
run_model(question="طامع یعنی ؟", candicates=["آزمند", "خوش شانس", "محتاج", "مطمئن"])
run_model(
    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 ",
    candicates=["روز اول", "روز دوم", "روز سوم", "هیچکدام"])

📚 Documentation

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

📄 License

This project is licensed under the cc-by-nc-sa-4.0 license.

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