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A library for running experiments of multiple chatbots having conversations with each other.

Project description

Chatting Chatbots

A library for running experiments of multiple genarative language models having conversations with each other.

python version Code style: black pytorch version

Installation

Install chatting chatbots from PiPy:

pip install chatting_chatbots

or directly from GitHub:

pip install git+https://github.com/centre-for-humanities-computing/chatbot-conversations

Usage

The package currently works only with PyTorch.

Open In Colab

an example of usage (seen in tutorials/examples.ipynb):

import chatting_chatbots as cc

cc.Experiment(
    population = {"John" : "gpt2", "Margaret" : "gpt2", "Alice" : "gpt2", "Bob" : "gpt2", "Eve" : "gpt2", "Charlie" : "gpt2", "Darwin" : "gpt2"}, 
    cycles = 2,
    initial_context="Hi. Why are you late?",
    conversation_length=10,
    verbose = True,
    use_gpu=0,
    use_files=False
    ).run()

Parameters for Experiment

parameter type default description
population dict required parameter A dictionary, where each element is an agent, defined as "Agent_Name" : "Agent_Model". The model can either be the name of a model available on HuggingFace, or a list [tokenizer, model] of a tokenizer and a model you have already created.
cycles int 1 The number of cycles of conversation, where one cycle is each agent in the population talking to each other agent twice (starting the conversation once, and being second once).
initial_context str "Hello." The initial context the first agent in a conversation uses i.e. each conversation starts with this sentence.
conversation_length int 10 The length of the conversation in turns (excluding the initial context).
random_length int 0 A randomisation variable for the length; the length is taken from a uniform distribution of [conversation_length - random_length, conversation_length + random_length].
training_args TrainingArguments None A TrainingArguments parameter passed to the HuggingFace Trainer() class. If None, some default parameters are used.
verbose bool False If set to True, class outputs some extra text about what is happening.
use_files bool True If set to True, during training, conversations are saved to files in the output_path directory, and all of the files in the output_path will be used for training. Otherwise, only the conversations generated during a single .run() are used.
use_gpu int -1 If not set, uses cpu, otherwise, uses the gpu specified.
generation_parameters list None A list of parameters used for the model.generate() function (not tested). If not used, some default parameters are used instead.
context_size int 600 How many tokens should be used on each generation. If set to 0, uses the entire conversation as context. Note that this is prone to crashing as some models cannot handle large contexts.
full_conversation bool True If set to True, uses the entire conversation for training. If set to False, uses only the other agent's text for training.
batch_size int 128 The batch size used for training. If the experiment crashes because of no examples for training, you should either decrease this value or increase the conversation length.
train_after_run bool True If set to True, every agent is trained after a .run().
output_path str "outputs" The path where the conversation files should be saved.
cache_path str ".cache/huggingface/transformers" The path where the models (if they are downloaded from HuggingFace) should be stored.

Functions available

function arguments return description
conversation_generation() str speaker_one, str speaker_two str conversation Generates a conversation between speaker_one and speaker_two. Returns the conversation. Note that the speakers must be in the population of the Experiment.
run() - - Runs the experiment by having each agent speak with each other agent for the set number of cycles, optionally training them afterwards.
train_participant() str participant - Trains the participant specified. Note that the participant must be part of the population of the Experiment.
return_model str participant model Returns the current model of the specified participant.

Credits

This package was created using the Name project template.

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