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NLP-CHATBOT

Python 100.00%
seq2seq natural-language-processing tensorflow cuda machine-learning nlp question-answering chatbot generative-network question question2answer squad nlp-question-answering python squad-dataset nlp-python

nlp-squad's Introduction

Question Answering On SQUAD

Short-Video

chatbot_record.mp4

Requirements

  • Tensorflow=2.4.0
  • CUDA=11.2
  • CUDNN=11.4 (necessary only for gpu computing (optional))
  • Python 3.6

Dataset

The Stanford Question Answering Dataset (SQuAD), which is derived from Wikipedia, can be used for question answering chatbot. The SQuAD includes:

  • 107,785 question-answer pairs depend on 536 articles.
  • Due to a lack of RAM, only 10.000 pairs have been used for training of the Seq2Seq model.
  • According to the results, given questions to the model can be predicted by the model accurately.
  • Given data to the model should be enhanced in order to increase the accuracy of the model.

Folders:

Data:

  • Downloaded data will be saved in this folder. Dowloading script is available in "Preporeccsing file.py". The program will download it in Data folder, if the folder exists.

Weight:

  • Model weight, encoder and decoder model will be saved in this folder.

Image:

  • The history of the model will be saved in this folder.

Model:

Seq2Seq architecture has been chosen to train the model.

Running:

  • The "main.py" file must be run in a virtual environment with the system requirements.

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