https://github.com/karma9874/seq2seq-chatbot
Chatbot based Seq2Seq model with bidirectional rnn and attention mechanism with tensorflow, trained on Cornell Movie-Dialogs Corpus and deployed on a Flask Server
https://github.com/karma9874/seq2seq-chatbot
attention-mechanism bidirectional-lstm chatbot deep-learning flask nlp question-answering seq2seq tensorflow
Last synced: 11 months ago
JSON representation
Chatbot based Seq2Seq model with bidirectional rnn and attention mechanism with tensorflow, trained on Cornell Movie-Dialogs Corpus and deployed on a Flask Server
- Host: GitHub
- URL: https://github.com/karma9874/seq2seq-chatbot
- Owner: karma9874
- Created: 2019-06-13T09:38:01.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2020-08-24T17:32:17.000Z (almost 6 years ago)
- Last Synced: 2025-04-10T02:44:37.786Z (about 1 year ago)
- Topics: attention-mechanism, bidirectional-lstm, chatbot, deep-learning, flask, nlp, question-answering, seq2seq, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 38.6 MB
- Stars: 24
- Watchers: 3
- Forks: 14
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Seq2Seq-Chatbot
## Introduction
This Chatbot is a TensorFlow implementation of Seq2Seq Mode. It make use of a seq2seq model RNN for sentence predictions. The chatbot is trained on Cornell Movie Dialogs Corpus on Conversation of lenghth between 5 to 2 words
## Installation
To run this chabtot you will need:
* Python 3.5
* Tensorflow (1.13.1)
* Numpy
* Flask
* Matplotlib
* [Dataset](http://www.cs.cornell.edu/~cristian/data/cornell_movie_dialogs_corpus.zip)
## Running
### To chat with the trained model from Web Chat:
1. First install the required depedencies from `Chat_UI_Web` and run
`pip install -r requirment.txt`
2. From the Chat_UI_Web directory run run.py
`python run.py`
3. Open a browser to the URL indicated by the server console, This is typically: http://localhost:5000/
### To chat with the trained model from Python Console:
1. First install the required depedencies from root directory and run
`pip install -r requirment.txt`
2. From the root directory run
`python chat.py`
## List of Hyperparameters
* Batch_size to 128
* Rnn_size to 128
* Embed_size to 128
* Learning_rate to 0.0.1
* Epochs to 500
* Keep_prob to 0.75
## For Training Model
To Train Model from Python Console:
1. Configure the `config.py` file for desired training hyperparameters
2. Run train.py
`python train.py`
3. Make sure the datasets should be present in the `datasets` folder
## Results
Model was able to train to `Accuracy of 62%` and `Loss of 19%` and after that it was overfitting
### Accuracy
### Loss
### Chat Results
```
Human: who are you
Bot: girlfriend
Human: who am i
Bot: doctor livingston
Human: where are you from
Bot: southern california
Human: how old are you
Bot: nineteen or
Human: are you a boy
Bot: i am trying
Human: are you a girl
Bot: something like that
Human: do you drink
Bot: no thanks
```
Some Funny Result
