semvec/pypi-packages huggingface dataset:
conversation-bot 1.0.0
AI powered conversations.
License: MIT
Requires Python: >=3.7,<4.0
Keywords: conversation-bot,ai,gpt2,bot,conversation

# Conversation Bot
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Better Chatbots using AI.
**NOTE: The model is currently only trained on the TaskMaster dataset.**

## Training
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The model can be trained using the Model/train.py script.
The preprocessing/ directory contains scripts to convert unclean data to clean data.
Save the encoded data to a .pt file.

## Inference
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`conversation.py` provides a simple inference to check if the model works as expected.
Conversations can be saved to the Logs directory.

## Add files
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Create a directory inside the datasets and preprocessing for converting and analyzing data.

## Training on Colaboratory
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To train the model on google colab, first run the `bash colab-setup.sh` to set up the environment.

## Model Checkpoint
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The Checkpoints directory contains a basic model checkpoint trained using transformers' distilgpt2 on the TaskMaster dataset.
keras-mxnet 2.2.4.3
Deep Learning for humans. Keras with highly scalable,                   high performance Apache MXNet backend support.
License: MIT


Keras is a high-level neural networks API,
written in Python. Keras-MXNet is capable of running on top of
high performance, scalable Apache MXNet deep learning engine.

Use Keras-MXNet if you need a deep learning library that:

- Allows for easy and fast prototyping
  (through user friendliness, modularity, and extensibility).
- Supports both convolutional networks and recurrent networks,
  as well as combinations of the two.
- Runs seamlessly on CPU, one GPU and multi-GPU.

Read the Keras documentation at: https://keras.io/

Read the Keras-MXNet documentation at:
https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend

For a detailed overview of what makes Keras special, see:
https://keras.io/why-use-keras/
Keras is compatible with Python 2.7-3.6
and is distributed under the MIT liense.