MRC question and answer approach using NLP and machine learning techniques
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Updated
Nov 27, 2018 - Jupyter Notebook
MRC question and answer approach using NLP and machine learning techniques
A context based question answering system trained on the SQUAD 2.0 dataset
Sentiment Classifier using: Softmax-Regression, Feed-Forward Neural Network, Bidirectional stacked LSTM/GRU Recursive Neural Network, fine-tuning on BERT pre-trained model. Question Answering using BERT pre-trained model and fine-tuning it on various datasets (SQuAD, TriviaQA, NewsQ, Natural Questions, QuAC)
Visual Question Answering System
Topic+QA pipeLine
Question Answering using BERT pre-trained model and fine-tuning it on various datasets (SQuAD, TriviaQA, NewsQ, Natural Questions, QuAC)
A project about fine-tuning bert-base-uncased model for reading comprehension tasks.
A personal implementation of "Adversarial Examples for Evaluating Reading Comprehension Systems".
Machine Comprehension on Squad Dataset using Match-LSTM + Ans-Ptr Network
🗨️ This repository contains a collection of notebooks and resources for various NLP tasks using different architectures and frameworks.
This project showcases how to fine-tune a HuggingFace model with the SQuAD dataset and create a Gradio interface for interactive question answering, enabling users to input context and questions and receive model-generated answers.
DistilBERT question-answering fine-tuned on SQuAD1.1
Assignment for DS525 - Natural Language Processing
Tutorial of Question Answering using SQuAD in English and Spanish with BERT and BiDAF.
Initially implement Document-Retrieval-System with SBERT embeddings and evaluate it in CORD-19 dataset. Afterwards, fine tune BERT model with SQuAD.v2 dataset so as to evaluate it in Question Answering task.
Sentence Bert for Question-Answering on COVID-19 Open Research Dataset (CORD-19)
Implementation of a Dynamic Coattention Network proposed by Xiong et al.(2017) for Question Answering, learning to find answers spans in a document, given a question, using the Stanford Question Answering Dataset (SQuAD2.0).
Question answering system developed using seq2seq modeling - The SQuAD dataset.
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