This model is a fine-tuned version of BERT-base uncased on the SQuAD v2 dataset for extractive question answering.
It was trained for 3 epochs and can answer questions given a context passage, while also handling unanswerable questions (a key feature of SQuAD v2).

Developed by: Your Name
Model type: Extractive Question Answering
Language(s): English
License: Apache-2.0
Finetuned from: bert-base-uncased

Model Sources

Dataset: SQuAD v2
Base model: bert-base-uncased

Uses

Direct Use

Extractive Question Answering: Given a passage and a question, the model extracts the most likely span of text that answers the question.
Handles unanswerable questions by predicting "no answer" when appropriate.

Downstream Use

Can be integrated into chatbots, virtual assistants, or search systems that require question answering over text.

Out-of-Scope Use

Generative question answering (the model cannot generate new answers).
Non-English tasks (the model was trained only on English data).

Bias, Risks, and Limitations

The model inherits biases from the SQuAD v2 dataset.
Performance may degrade on domain-specific or noisy text not represented in SQuAD v2.
Not designed for open-domain QA across large corpora — works best when the context passage is provided.

How to Get Started with the Model

You can try the model with the following code:

python

from transformers import pipeline

qa_pipeline = pipeline("question-answering", model="peeyush01/bert-squad-v2")
result = qa_pipeline({
    "context": "Hugging Face is creating a tool that democratizes AI.",
    "question": "What is Hugging Face creating?"
})
print(result)

Author Details

Peeyush
Github : Github

peeyush01/bert-qa-finetuned

BERT-base uncased fine-tuned on SQuAD v2

Model Details

Model Description