Hugging Face model: aai520-group6/distilbert-finetuned-uncased-squad_v2
Author: aai520-group6
Created: 2023-10-23 03:22:59
Last modified: 2025-07-22 05:26:37
Pipeline: question-answering
Library: transformers
Likes: 0 | Downloads: 189
Tags: transformers, pytorch, safetensors, distilbert, question-answering, en, arxiv:1910.09700, license:mit, model-index, endpoints_compatible, region:us
Card metadata:
language: en
license: mit
model_details: 
        ## Abstract
        This model, 'distilbert-finetuned-uncased', is a question-answering chatbot trained on the SQuAD dataset, demonstrating competency in building conversational AI using recent advances in natural language processing. It utilizes a BERT model fine-tuned for extractive question answering.

        ## Data Collection and Preprocessing
        The model was trained on the Stanford Question Answering Dataset (SQuAD), which contains over 100,000 question-answer pairs based on Wikipedia articles. The data preprocessing involved tokenizing context paragraphs and questions, truncating sequences to fit BERT's max length, and adding special tokens to mark question and paragraph segments.

        ## Model Architecture and Training
        The architecture is based on the BERT transformer model, which was pretrained on large unlabeled text corpora. For this project, the BERT base model was fine-tuned on SQuAD for extractive question answering, with additional output layers for predicting the start and end indices of the answer span.

        ## SQuAD 2.0 Dataset
        SQuAD 2.0 combines the existing SQuAD data with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. This version of the dataset challenges models to not only produce answers when possible but also determine when no answer is supported by the paragraph and abstain from answering.
        
intended_use: 
        - Answering questions from the squad_v2 dataset.
        - Developing question-answering systems within the scope of the aai520-project.
        - Research and experimentation in the NLP question-answering domain.
        
limitations_and_bias: 
        The model inherits limitations and biases from the 'distilbert-base-uncased' model, as it was trained on the same foundational data.
        It may underperform on questions that are ambiguous or too far outside the scope of the topics covered in the squad_v2 dataset.
        Additionally, the model may reflect societal biases present in its training data.
        
ethical_considerations: 
        This model should not be used for making critical decisions without human oversight,
        as it can generate incorrect or biased answers, especially for topics not covered in the training data.
        Users should also consider the ethical implications of using AI in decision-making processes and the potential for perpetuating biases.
        
evaluation: 
        The model was evaluated on the squad_v2 dataset using various metrics. These metrics, along with their corresponding scores,
        are detailed in the 'eval_results' section. The evaluation process ensured a comprehensive assessment of the model's performance
        in question-answering scenarios.
        
training: 
        The model was trained over 10 epochs with a learning rate of 2e-05, using a batch size of 64.
        The training utilized a cross-entropy loss function and the AdamW optimizer, with gradient accumulation over 4 steps.
        
tips_and_tricks: 
        For optimal performance, questions should be clear, concise, and grammatically correct.
        The model performs best on questions related to topics covered in the squad_v2 dataset.
        It is advisable to pre-process text for consistency in encoding and punctuation, and to manage expectations for questions on topics outside the training data.