---
datasets:
- SajjadAyoubi/persian_qa
language:
- fa
pipeline_tag: question-answering
license: apache-2.0
library_name: transformers
tags:
- roberta
- question-answering
- Persian
---
# Tara-Roberta-Base-FA-QA

**Tara-Roberta-Base-FA-QA** is a fine-tuned version of the `facebookAI/roberta-base` model for question-answering tasks, trained on the [SajjadAyoubi/persian_qa](https://huggingface.co/datasets/SajjadAyoubi/persian_qa) dataset. This model is designed to understand and generate answers to questions posed in Persian.

## Model Description

This model was fine-tuned on a dataset containing Persian question-answering pairs. It leverages the `roberta-base` architecture to provide answers based on the context provided. The training process was performed with a focus on improving the model's ability to handle Persian text and answer questions effectively.

## Training Details

The model was trained for 3 epochs with the following training and validation losses:

- **Epoch 1**: 
  - Training Loss: 2.0713
  - Validation Loss: 2.1061

- **Epoch 2**: 
  - Training Loss: 2.1558
  - Validation Loss: 2.0121

- **Epoch 3**: 
  - Training Loss: 2.0951
  - Validation Loss: 2.0168

## Evaluation Results

The model achieved the following results:

- **Training Loss**: Decreased over the epochs, indicating effective learning.
- **Validation Loss**: Slight variations were observed, reflecting the model's performance on unseen data.

## Usage

To use this model for question-answering tasks, load it with the `transformers` library:

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

model = "hosseinhimself/tara-roberta-base-fa-qa"

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForQuestionAnswering.from_pretrained(model)

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

# Example usage
context = "CONTEXT"

question = "QUESTION"

# Modify the pipeline to return more answers
results = qa_pipeline(question=question, context=context, top_k=5)  # top_k specifies the number of answers

# Display the answers
for idx, result in enumerate(results):
    print(f"Answer {idx+1}: {result['answer']}")

```

## Datasets

The model was fine-tuned using the [SajjadAyoubi/persian_qa](https://huggingface.co/datasets/SajjadAyoubi/persian_qa) dataset.

## Languages

The model supports the Persian language.

## Additional Information

For more details on how to fine-tune similar models or to report issues, please visit the [Hugging Face documentation](https://huggingface.co/docs/transformers).