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+ ---
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+ datasets:
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+ - squad_v2
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+ language:
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+ - en
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+ metrics:
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+ - accuracy
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+ library_name: transformers
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+ pipeline_tag: question-answering
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+ tags:
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+ - question-answering
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+ ---
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+ # AnswerMind
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+ AnswerMind is a Question Answering Model. This model is a lighter version of any of the question-answering models out there.
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+ The base architecture is BERT for Question-Answering.
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+
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+ ## Dataset
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+ 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.
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+ Due to GPU limitations, this version is trained on `30k samples` from the Stanford Question Answering Dataset.
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+
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+ <details>
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+ <summary><i>Structure of the Data Dictonary</i></summary>
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+ <!--All you need is a blank line-->
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+
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+ {
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+ "data":[
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+ {
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+ "title":"Article Title",
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+ "paragraphs":[
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+ {
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+ "context":"The context text of the paragraph",
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+ "qas":[
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+ {
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+ "question":"The question asked about the context",
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+ "id":"A unique identifier for the question",
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+ "answers":[
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+ {
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+ "text":"The answer to the question",
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+ "answer_start":"The starting index of the answer in the context"
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+ }
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+ ]
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+ }
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+ ]
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+ }
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+ ]
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+ }
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+ ],
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+ "version":"The version of the SQuAD dataset"
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+ }
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+ </details>
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+
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+ ## Model
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+ 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.
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+ <img src="https://imgs.search.brave.com/F8m-nwp6EIG5vq--OmJLrCDpIkuX6tEQ_kyFKQjlUTs/rs:fit:1200:1200:1/g:ce/aHR0cHM6Ly9ibG9n/LmdyaWRkeW5hbWlj/cy5jb20vY29udGVu/dC9pbWFnZXMvMjAy/MC8xMC9TbGljZS0x/OC5wbmc">
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+ For more detail about this read [Understanding QABERT]()
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+
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+
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+ ## Inference
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+ _Load model_
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForQuestionAnswering
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+
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+ QAtokenizer = AutoTokenizer.from_pretrained("SRDdev/QABERT-small")
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+
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+ QAmodel = AutoModelForQuestionAnswering.from_pretrained("SRDdev/QABERT-small")
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+ ```
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+
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+ _context_
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+ ```text
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+ Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a
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+ question-answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune
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+ a model on a SQuAD task, you may leverage the examples/pytorch/question-answering/run_squad.py script.
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+ ```
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+
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+
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+ _Build Pipeline_
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+ ```python
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+ from transformers import pipeline
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+
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+ ask = pipeline("question-answering", model= QAmodel , tokenizer = QAtokenizer)
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+
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+ result = ask(question="What is a good example of a question answering dataset?", context=context)
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+
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+ print(f"Answer: '{result['answer']}'")
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+ ```
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+
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+ ## Contributing
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+
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+ Pull requests are welcome. For major changes, please open an issue first
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+ to discuss what you would like to change.
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+ Please make sure to update tests as appropriate.
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+
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+ ## Citations
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+ ```
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+ @citation{ QA-BERT-small,
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+ author = {Shreyas Dixit},
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+ year = {2023},
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+ url = {https://huggingface.co/SRDdev/QA-BERT-small}
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+ }
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+ ```