The Question Answering Model presented in this repository is built on BERT (Bidirectional Encoder Representations from Transformers), a state-of-the-art transformer-based architecture tailored for natural language processing tasks. Specifically, the model utilizes "bert-base-cased," a pre-trained version of BERT on cased English text. The primary objective of this model is to excel in question-answering tasks, where it interprets and responds to questions based on the provided information.
BERT Architecture: BERT's architecture is defined by its transformer-based design, featuring attention mechanisms and self-attention mechanisms across multiple layers. Its bidirectional contextual awareness allows it to understand the meaning of words in relation to their complete context, enabling a more nuanced comprehension of language semantics.
Key Features:
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Cased English Text: The chosen instantiation, "bert-base-cased," is configured to work with cased English text. This distinction is crucial as it enables the model to distinguish between uppercase and lowercase characters, retaining essential information in the language.
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Pre-training: BERT is pre-trained on extensive and diverse text material, exposing it to a wide range of linguistic contexts. This process allows the model to acquire contextual representations of words and phrases.
Training and Validation: The model is trained on a dataset supplied from a JSON file containing pairs of questions and related responses. A separate validation dataset is used to assess the model's performance during training, helping to detect overfitting and ensure robust generalization.
Hyperparameters and Settings: The model's behavior during training is governed by a set of hyperparameters and training settings, including maximum sequence length, number of training epochs, batch sizes, and more.
Metrics: Evaluation metrics such as accuracy, recall, and F1 score are employed to quantify the model's ability to generate relevant and accurate responses.
Usage: The model can be interactively tested through the provided link, where users can input questions based on a sample context, and the model generates corresponding answers.
Live Link to Model: Question Answering Model (BERT Base Cased)
Sample Result
