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.
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.
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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