🗨️ This repository contains a collection of notebooks and resources for various NLP tasks using different architectures and frameworks.
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Updated
May 26, 2024 - Jupyter Notebook
🗨️ 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.
Assignment for DS525 - Natural Language Processing
Machine Comprehension on Squad Dataset using Match-LSTM + Ans-Ptr Network
NLP-CHATBOT
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).
DistilBERT question-answering fine-tuned on SQuAD1.1
Question Answering using BERT pre-trained model and fine-tuning it on various datasets (SQuAD, TriviaQA, NewsQ, Natural Questions, QuAC)
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)
A project about fine-tuning bert-base-uncased model for reading comprehension tasks.
BERT based pretrained model using SQuAD 2.0 Dataset for Question-Answering
Sentence Bert for Question-Answering on COVID-19 Open Research Dataset (CORD-19)
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.
Important paper implementations for Question Answering using PyTorch
Tutorial of Question Answering using SQuAD in English and Spanish with BERT and BiDAF.
A personal implementation of "Adversarial Examples for Evaluating Reading Comprehension Systems".
Topic+QA pipeLine
Question answering system developed using seq2seq modeling - The SQuAD dataset.
A context based question answering system trained on the SQUAD 2.0 dataset
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