README.md

---
license: apache-2.0
language:
- fa
pipeline_tag: question-answering
tags:
- persain
- persian_qa
- parsbert
metrics:
- accuracy
datasets:
- SajjadAyoubi/persian_qa
---
# Model Card for Model ID
# ParsBERT for Persian Question Answering

## Model Description

this  is a fine-tuned version of the ParsBERT model, specifically adapted for the task of question answering in Persian. ParsBERT is a BERT-based model pre-trained on a large Persian text corpus. This model has been fine-tuned on a Persian QA dataset to provide accurate and contextually relevant answers to questions posed in Persian.

## Model Architecture

- **Base Model**: ParsBERT
- **Task**: Question Answering
- **Language**: Persian
- **Number of Parameters**: 110M

## Intended Use

This model is intended for use in applications requiring natural language understanding and question answering in Persian, such as:

- Persian language chatbots
- Persian information retrieval systems
- Educational tools for Persian language learners

## Dataset

The model was fine-tuned on a Persian QA dataset. The dataset consists of question-answer pairs extracted from various Persian text sources, ensuring a diverse range of topics and contexts.


## Usage

To use this model for question answering in Persian, you can load it using the Hugging Face Transformers library. Here’s a quick example:

```python
from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("mansoorhamidzadeh/parsbert-persian-QA")
model = AutoModelForQuestionAnswering.from_pretrained("mansoorhamidzadeh/parsbert-persian-QA")

# Create a QA pipeline
qa_pipeline = pipeline("question-answering", model=model, tokenizer=tokenizer)

# Example usage
context = "متن زمینه که شامل اطلاعات مرتبط با سوال شما است."
question = "سوال شما چیست؟"
result = qa_pipeline(question=question, context=context)

print(f"Answer: {result['answer']}")