---

name: training description: "Use ChatterBot trainers, corpus utilities, CSV/TSV/JSON field maps, Ubuntu corpus handling, progress controls, and training-data export workflows." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

ChatterBot Training

Use this sub-skill when a task asks how to teach a ChatterBot bot responses, load chatterbot-corpus, train from list/CSV/TSV/JSON files, export learned conversations, control training progress, or debug trainer data-format errors.

Quick route

1. Read references/training-api.md for trainer signatures, input shapes, and data formats.
2. Read references/workflows.md for minimal list, corpus, CSV/TSV, JSON, Ubuntu corpus, and export recipes.
3. Read references/troubleshooting.md for field-map, corpus, optional dependency, and large download failures.
4. Use scripts/list_training_demo.py for a tiny list-training smoke test.
5. Use scripts/file_training_demo.py to generate and train tiny CSV or JSON fixtures.
6. Use scripts/export_training_data.py to train a tiny bot and write export JSON.

Trainer selection

Task	Trainer
A single ordered conversation in Python	ListTrainer
Installed chatterbot-corpus data by dotted path	ChatterBotCorpusTrainer
CSV or TSV rows	CsvFileTrainer
JSON files with a root conversation array	JsonFileTrainer
Legacy Ubuntu dialog corpus tar/TSV layout	UbuntuCorpusTrainer
Custom source/format	subclass Trainer and implement train()

Required setup

Most trainer paths instantiate a normal ChatBot, so the default SQL + PosLemmaTagger path needs a spaCy model such as en_core_web_sm.

Corpus training needs pyyaml and usually the separate chatterbot-corpus package. Unit conversion is not a training feature; route that to logic-adapters.

Data model

Training creates Statement rows. Each response statement normally stores:

• its own text;
• in_response_to pointing at the previous utterance or explicit response field;
• search_text and search_in_response_to computed by the bot's tagger;
• optional conversation, persona, tags, and created_at metadata.

Configure preprocessors and tagger before training because trainers apply the bot's preprocessors and tagger while building statements.

Boundaries

• For core ChatBot lifecycle and read-only inference after training, use core-chatbot.
• For response selection after trained data exists, use logic-adapters.
• For storage backend persistence and query filters, use storage-adapters.
• For Django database migrations and web app training integration, use django-integration.