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core

README.md

The core library contains the following files:

  • agents.py: this file contains a few basic agents which can be extended by your own model
    • Agent: base class for all other agents, implements the act() method which receives an observation table and returns a table in response
    • Teacher: child of Agent, also implements the report method for returning metrics. Tasks implement the Teacher class
    • MultiTaskTeacher: creates a set of teachers based on a "task string" passed to the Teacher, creating multiple teachers within it and alternating between them
    • create_task_teacher: instantiate a teacher from a given task string (e.g. 'babi:task:1' or 'squad')
  • build_data.py: basic utilities for setting up data for tasks. you can override if your filesystem needs different functionality.
  • dict.py: contains code for building general NLP-style dictionaries from observations
    • DictionaryAgent: agent which tracks the index and frequency of words in a dictionary, and can parse a sentence into indices into its dictionary or back
  • gpt2_helper.py: byte pair encoding utilities from GPT-2
  • image_featurizers.py: provides functionality for loading images
  • loader.py: functions for loading world, agents, tasks, and teacher modules
  • logs: utilities for logging metrics to tensorboard
  • message: contains message object
    • Message: class for objects and observations in ParlAI
  • metrics.py: computes evaluation metrics, e.g. ranking metrics, etc.
  • params.py: uses argparse to interpret command line arguments for ParlAI
  • pytorch_data_teacher: teacher that uses the PyTorch data loader
  • teachers.py: contains teachers that deal with dialogue-based tasks, as well as data classes for storing data
    • FixedDialogTeacher: base class for a teacher that utilizes fixed data
    • DialogTeacher: base class for a teacher doing dialogue with fixed chat logs
    • ParlAIDialogTeacher: a teacher that implements a simple standard text format for many tasks (non-visual tasks only)
  • torch_agent: utility code for building PyTorch-based agents in ParlAI
    • TorchAgent: class which serves as a useful parent class for other model agents
    • Batch: namedtuple which is the input type of the main abstract methods of the TorchAgent class
    • Output: namedtuple which is the expected output type of the main abstract methods of the TorchAgent class
    • History: class which handles tracking the dialogue state over the course of an episode.
  • torch_classifier_agent: abstract agent which extends TorchAgent and contains useful utilities for classification models
  • torch_generator_agent: abstract agent which extends TorchAgent and contains useful utilities for generation models
  • torch_ranker_agent: abstract agent which extends TorchAgent and contains useful utilities for ranking models
  • worlds.py: contains a set of basic worlds for tasks to take place inside
    • World: base class for all other worlds, implements parley, shutdown, __enter__, and __exit__
    • DialogPartnerWorld: default world for turn-based two-agent communication
    • MultiAgentDialogWorld: round-robin turn-based agent communication for two or more agents
    • HogwildWorld: default world for setting up a separate world for every thread when using multiple threads (processes)