metadata

datasets:
  - squad_v2
language:
  - en
metrics:
  - accuracy
library_name: transformers
pipeline_tag: question-answering
tags:
  - question-answering

QA-BERT

QA-BERT is a Question Answering Model. This model is a lighter version of any of the question-answering models out there.

Dataset

The Stanford Question Answering Dataset (SQuAD) is a widely used benchmark dataset for the task of machine reading comprehension. It consists of over 100,000 question-answer pairs based on a set of Wikipedia articles. The goal is to train models that can answer questions based on their understanding of the given text passages. SQuAD has played a significant role in advancing the state-of-the-art in this field and remains a popular choice for researchers and practitioners alike. Due to GPU limitations, this version is trained on 30k samples from the Stanford Question Answering Dataset.

Structure of the Data Dictonary

  {
  "data":[
      {
          "title":"Article Title",
          "paragraphs":[
              {
                  "context":"The context text of the paragraph",
                  "qas":[
                      {
                          "question":"The question asked about the context",
                          "id":"A unique identifier for the question",
                          "answers":[
                              {
                                  "text":"The answer to the question",
                                  "answer_start":"The starting index of the answer in the context"
                              }
                          ]
                      }
                  ]
              }
          ]
      }
  ],
  "version":"The version of the SQuAD dataset"
  }

Model

BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained transformer-based model for natural language processing tasks such as question answering. BERT is fine-tuned for question answering by adding a linear layer on top of the pre-trained BERT representations to predict the start and end of the answer in the input context. BERT has achieved state-of-the-art results on multiple benchmark datasets, including the Stanford Question Answering Dataset (SQuAD). The fine-tuning process allows BERT to effectively capture the relationships between questions and answers and generate accurate answers. For more detail about this read Understanding QABERT

Inference

Load model

from transformers import AutoTokenizer, AutoModelForQuestionAnswering

QAtokenizer = AutoTokenizer.from_pretrained("SRDdev/QABERT-small")

QAmodel = AutoModelForQuestionAnswering.from_pretrained("SRDdev/QABERT-small")

context

Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a
question-answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune
a model on a SQuAD task, you may leverage the examples/pytorch/question-answering/run_squad.py script.

Build Pipeline

from transformers import pipeline

ask = pipeline("question-answering", model= QAmodel , tokenizer = QAtokenizer)

result = ask(question="What is a good example of a question answering dataset?", context=context)

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

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. Please make sure to update tests as appropriate.

Citations

@citation{ QA-BERT-small,
  author = {Shreyas Dixit},
  year = {2023},
  url = {https://huggingface.co/SRDdev/QA-BERT-small}
}