metadata

language:
  - en
dataset_info:
  features:
    - name: instruction
      dtype: string
    - name: context
      dtype: string
    - name: response
      dtype: string
    - name: category
      dtype: string
    - name: messages
      list:
        - name: content
          dtype: string
        - name: role
          dtype: string
  splits:
    - name: train
      num_bytes: 34692013
      num_examples: 15011
  download_size: 15166632
  dataset_size: 34692013
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
task_categories:
  - question-answering
  - text2text-generation

Dataset Card for "databricks-dolly-15k-chatml"

Dataset Summary

This dataset has been created by Re:cast AI to transform the existing dataset databricks/databricks-dolly-15k into a chatml friendly format for use in SFT tasks with pretrained models.

Dataset Structure

messages = [
  { "content": "You are an expert Q&A system that is trusted around the world. You always... etc.", "role": "system" }, 
  { "content": "(Optional) Context information is below.\n----------------\nVirgin Australia, the... etc.", "role": "user" }, 
  { "content": "Virgin Australia commenced services on 31 August 2000... etc.", "role": "assistant" } ]
]

Usage

from datasets import load_dataset
dataset = load_dataset("recastai/databricks-dolly-15k-chatml", split="train")

Processing applied to original dataset

INSTRUCTIONS = """You are an expert Q&A system that is trusted around the world. You always answer the user's query in a helpful and friendly way.
Some rules you always follow:
1. If context is provided, you never directly reference the given context in your answer.
2. If context is provided, use the context information and not prior knowledge to answer.
3. Avoid statements like 'Based on the context, ...' or 'The context information ...' or 'The answer to the user's query...' or anything along those lines.
4. If no context is provided use your internal knowledge to answer."""

# databricks-dolly-15k features:
# - instruction: The user query/question
# - context: (optional) context to use to help the assistant
# - response: The assistant's response to the query/question
#
key_mapping = dict(
    query = "instruction",
    context = "context",
    response = "response"
)

def process_chatml_fn(example, validation=False):
    """
    Processing specific to databricks-dolly-15k into a chat format.
    """
    user_content = (
        "(Optional) Context information is below.\n"
        "----------------\n"
        "{context}\n"
        "----------------\n"
        "Answer the following query.\n"
        "{query}\n"
    )
    assistant_content = "{response}"
    
    message = [
        {"role": "system", "content": INSTRUCTIONS},
        {"role": "user", "content": user_content.format(context=example[key_mapping['context']], query=example[key_mapping['query']])},
        {"role": "assistant", "content": assistant_content.format(response=example[key_mapping['response']])} 
    ]

    return message