https://github.com/jalajthanaki/chatbot_based_on_babi_dataset_using_keras
Built the chatbot based on bAbI dataset using Keras
https://github.com/jalajthanaki/chatbot_based_on_babi_dataset_using_keras
babi-dataset chatbot keras
Last synced: 5 months ago
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Built the chatbot based on bAbI dataset using Keras
- Host: GitHub
- URL: https://github.com/jalajthanaki/chatbot_based_on_babi_dataset_using_keras
- Owner: jalajthanaki
- Created: 2018-01-19T11:25:20.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-01-19T11:38:01.000Z (over 7 years ago)
- Last Synced: 2025-03-31T22:22:01.539Z (6 months ago)
- Topics: babi-dataset, chatbot, keras
- Language: Python
- Size: 2.15 MB
- Stars: 2
- Watchers: 1
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Chatbot using Keras
Here we are building a chatbot and this chatbot can have resoning capability.
## Dataset Information
Here we are using bAbI dataset which is buit by Facebook AI Research.
## Dependencies
* Python 2.7
* TensorFlow 1.4.1: Refer this [link](https://www.tensorflow.org/install/)
* keras: Refer this [link](https://keras.io/#installation)
* functools: It's python standard library.
* tarfile: It's python standard library.
* re : It's python standard library.
* h5py: `$ sudo pip install h5py`
## Usage
* The dataset used here is babi-tasks-v1-2. Link of the dataset is [here](https://research.fb.com/downloads/babi/), its a relatively small dataset but a great dataset nonetheless
* In `main.py` file there are following parameters which can be change in following manner to train and test the model
* We are using the concepts of memory network and it is LSTM based models performed better than GRU based models for this task.
### For Training
```bash
Step 1: Open main.py
Step 2: For training, set the parameters as given below.
train_model = 1 #(1 means training mode and 0 means no training mode)
train_epochs = 100
load_model = 0 #( 1 means load the trained model and 0 means doesn't load trained model)
batch_size = 32
lstm_size = 64
test_qualitative = 0 #(1 means test trained on randomly generated story and 0 means do not perform test on ramdomly generated story)
user_questions = 0 #(1 means test trained on randomly generated story and 0 means do not perform test on ramdomly generated story)
Step 3: Run main.py
```
### For Testing
Here we can perform two types of testing.
* Testing for randomly generated story
* Testing for used given story
#### Testing for randomly generated story
```bash
Step 1: Open main.py
Setp 2: For testing ramdomly generated story, set the parameters as given below.
train_model = 0
train_epochs = 100
load_model = 1
batch_size = 32
lstm_size = 64
test_qualitative = 1
user_questions = 0
Step 3: Run main.py
```
#### Testing for used given story
```bash
Step 1: Open main.py
Setp 2: For testing user given story, set the parameters as given below.
train_model = 0
train_epochs = 100
load_model = 1
batch_size = 32
lstm_size = 64
test_qualitative = 0
user_questions = 1
Step 3: Run main.py
```
## Credit
Credit for the majority of code here goes to [Batchu Vishal](https://github.com/erilyth). I've merely created a wrapper to get people started.