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
Visual Question Answering System
A context based question answering system trained on the SQUAD 2.0 dataset
Question answering system developed using seq2seq modeling - The SQuAD dataset.
Topic+QA pipeLine
A personal implementation of "Adversarial Examples for Evaluating Reading Comprehension Systems".
Tutorial of Question Answering using SQuAD in English and Spanish with BERT and BiDAF.
Important paper implementations for Question Answering using PyTorch
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)
BERT based pretrained model using SQuAD 2.0 Dataset for Question-Answering
A project about fine-tuning bert-base-uncased model for reading comprehension tasks.
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)
Question Answering using BERT pre-trained model and fine-tuning it on various datasets (SQuAD, TriviaQA, NewsQ, Natural Questions, QuAC)
DistilBERT question-answering fine-tuned on SQuAD1.1
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).
NLP-CHATBOT
Machine Comprehension on Squad Dataset using Match-LSTM + Ans-Ptr Network
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