Bert Base Fa Qa

🚀 How to Use the BERT-based Persian QA Model

This project provides a way to perform question - answering tasks in Persian using a pre - trained BERT model. It offers different methods to interact with the model, including using pipelines and a manual approach.

🚀 Quick Start

✨ Features

• Supports both pipelines and a manual approach for question - answering.
• Can be used with both PyTorch and TensorFlow 2.X.
• Allows for the possibility of having no answer in the manual approach.

📦 Installation

Transformers require transformers and sentencepiece, both of which can be installed using pip.

pip install transformers sentencepiece

💻 Usage Examples

Basic Usage - Pipelines 🚀

In case you are not familiar with Transformers, you can use pipelines instead.

Note that, pipelines can't have no answer for the questions.

from transformers import pipeline

model_name = "SajjadAyoubi/bert-base-fa-qa"
qa_pipeline = pipeline("question-answering", model=model_name, tokenizer=model_name)

text = "سلام من سجاد ایوبی هستم ۲۰ سالمه و به پردازش زبان طبیعی علاقه دارم"
questions = ["اسمم چیه؟", "چند سالمه؟", "به چی علاقه دارم؟"]

for question in questions:
    print(qa_pipeline({"context": text, "question": question}))

>>> {'score': 0.4839823544025421, 'start': 8, 'end': 18, 'answer': 'سجاد ایوبی'}
>>> {'score': 0.3747948706150055, 'start': 24, 'end': 32, 'answer': '۲۰ سالمه'}
>>> {'score': 0.5945395827293396, 'start': 38, 'end': 55, 'answer': 'پردازش زبان طبیعی'}

Advanced Usage - Manual approach 🔥

Using the Manual approach, it is possible to have no answer with even better performance.

PyTorch

from transformers import AutoTokenizer, AutoModelForQuestionAnswering
from src.utils import AnswerPredictor

model_name = "SajjadAyoubi/bert-base-fa-qa"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForQuestionAnswering.from_pretrained(model_name)

text = "سلام من سجاد ایوبی هستم ۲۰ سالمه و به پردازش زبان طبیعی علاقه دارم"
questions = ["اسمم چیه؟", "چند سالمه؟", "به چی علاقه دارم؟"]

# this class is from src/utils.py and you can read more about it
predictor = AnswerPredictor(model, tokenizer, device="cpu", n_best=10)
preds = predictor(questions, [text] * 3, batch_size=3)

for k, v in preds.items():
    print(v)

Produces an output such below:

100%|██████████| 1/1 [00:00<00:00,  3.56it/s]
{'score': 8.040637016296387, 'text': 'سجاد ایوبی'}
{'score': 9.901972770690918, 'text': '۲۰'}
{'score': 12.117212295532227, 'text': 'پردازش زبان طبیعی'}

TensorFlow 2.X

from transformers import AutoTokenizer, TFAutoModelForQuestionAnswering
from src.utils import TFAnswerPredictor

model_name = "SajjadAyoubi/bert-base-fa-qa"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = TFAutoModelForQuestionAnswering.from_pretrained(model_name)

text = "سلام من سجاد ایوبی هستم ۲۰ سالمه و به پردازش زبان طبیعی علاقه دارم"
questions = ["اسمم چیه؟", "چند سالمه؟", "به چی علاقه دارم؟"]

# this class is from src/utils.py, you can read more about it
predictor = TFAnswerPredictor(model, tokenizer, n_best=10)
preds = predictor(questions, [text] * 3, batch_size=3)

for k, v in preds.items():
    print(v)

Produces an output such below:

100%|██████████| 1/1 [00:00<00:00,  3.56it/s]
{'score': 8.040637016296387, 'text': 'سجاد ایوبی'}
{'score': 9.901972770690918, 'text': '۲۰'}
{'score': 12.117212295532227, 'text': 'پردازش زبان طبیعی'}

Or you can access the whole demonstration using HowToUse iPython Notebook on Google Colab