https://github.com/SwapneelM/Protobot
Prototyping a Chatbot using Jack the Reader
https://github.com/SwapneelM/Protobot
chatbot machine-comprehension natural-language-processing webapp
Last synced: 11 months ago
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Prototyping a Chatbot using Jack the Reader
- Host: GitHub
- URL: https://github.com/SwapneelM/Protobot
- Owner: SwapneelM
- License: mit
- Created: 2018-10-30T14:06:16.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2022-12-08T01:15:58.000Z (over 3 years ago)
- Last Synced: 2024-11-27T03:34:36.054Z (over 1 year ago)
- Topics: chatbot, machine-comprehension, natural-language-processing, webapp
- Language: Python
- Size: 81.9 MB
- Stars: 6
- Watchers: 1
- Forks: 0
- Open Issues: 14
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Prototyping a Chatbot
Conversational AI Systems have seen a meteoric rise in the past few years as Deep Learning Techniques offered increasingly accurate solutions for problems relating to language understanding. We present a model for prototyping chatbots developed using state-of-the-art technology in an attempt to offer a simple, clean interface for researchers developing solutions in this domain.
## Example: Context-based Question-Answering System
In this end-to-end pipeline we demonstrate how to build a context-based question answering system using [Jack the Reader](https://github.com/uclmr/jack/). Note that you can switch your model from FastQA (used here) to others trained on different datasets and plug it into the [Django View](qa/views.py) to change the use-case/model which will accordingly modify your chatbot.
## Installation
* Clone this repository
$ git clone https://github.com/SwapneelM/Protobot.git
* Create a Python3 virtual environment (we've named it 'venv' here)
$ virtualenv -p python3 venv
$ source venv/bin/activate # or activate.csh depending on your platform
* Install the requirements
$ pip3 install -r requirements.txt
* Download some data (here, we use the Stanford QA and GloVe datasets)
$ cd Protobot/
$ ./data/SQuAD/download.sh
$ ./data/GloVe/download.sh
* To play around, download the pretrained models
$ wget -O fastqa.zip https://www.dropbox.com/s/qb796uljoqj0lvo/fastqa.zip?dl=1
$ unzip fastqa.zip && mv fastqa fastqa_reader
* Run the migrations for creating the database for the django webapp
$ python manage.py makemigrations
$ python manage.py migrate
* Run the server and visit `localhost:8000` on your browser to see the result
$ python manage.py runserver
* Of course, you can always train your own model(s)
$ python3 bin/jack-train.py with train='data/SQuAD/train-v1.1.json' dev='data/SQuAD/dev-v1.1.json' reader='fastqa_reader' \
> repr_dim=300 dropout=0.5 batch_size=64 seed=1337 loader='squad' save_dir='./fastqa_reader' epochs=20 \
> with_char_embeddings=True embedding_format='memory_map_dir' embedding_file='data/GloVe/glove.840B.300d.memory_map_dir' vocab_from_embeddings=True
or using a pre-defined configuration
$ python3 bin/jack-train.py with config='./conf/qa/squad/fastqa.yaml'
* Refer to the documentation for [Jack the Reader](JACK-README.md) for more information on playing around with the models.
## Usage
#### Provide the Context Paragraph to the System
* You can also provide a document instead of plaintext as context, check out [custom_test.py](custom_test.py) for reference on how to read from a document.
#### Ask Away!
## References
This app draws mainly from two sources used as building blocks for the context-based question answering system:
1. [Jack the Reader: A Machine Reading Comprehension framework by Dirk Weissenborn et. al](https://github.com/uclmr/jack)
2. [Chat with Iron Man, by Kurt Lopez](https://codepen.io/kurtlopez/full/gevzRW)
### Developed By [CC Developers](http://www.ccdev.in)
* [Rudresh Panchal](https://github.com/rpanchal1996)
* [Avais Pagarkar](https://github.com/avaisp)
* [Swapneel Mehta](https://github.com/swapneelm)
**Shameless Plug:** Check out our student community and their projects, [DJ Unicode](https://github.com/djunicode)!