Datasets

TRLoom loads data through Hugging Face datasets. Configure everything under the dataset section of your YAML.

Hub dataset

dataset:
  path: trl-lib/Capybara
  train_split: train
  eval_split: null

Optional Hub helpers:

dataset:
  path: some/dataset
  name: subset_name
  split: "train[:1000]"
  streaming: false
  kwargs:
    revision: main

split is applied at load time. train_split / eval_split select keys from the resulting dataset dict (or the single split name when you loaded one split).

Tip

The default eval_split is "test". Set eval_split: null when the dataset has no test split.

Local files

Supported extensions: .json, .jsonl, .csv, .tsv, .parquet, .txt, .arrow.

dataset:
  path: ./data/train.jsonl
  train_split: train
  eval_split: null

Local directories prefer load_from_disk, then fall back to load_dataset.

Paths are treated as local when they exist on disk, or look like a path (. / / / ~ / \, or a Windows drive like C:\).

Column rename and text column

dataset:
  path: ./data/train.jsonl
  train_split: train
  text_column: text
  columns:
    prompt: query
    completion: response

Mixtures

dataset:
  train_split: train
  datasets:
    - path: stanfordnlp/imdb
      split: train
      weight: 0.5
    - path: ./data/extra.jsonl
      weight: 0.5

• Equal weights → concatenate
• Unequal weights or streaming → interleave

Tiny smoke subset

Useful for proving a pipeline before a full run:

dataset:
  path: stanfordnlp/imdb
  split: "train[:64]"
  train_split: train
  eval_split: null
  text_column: text

Pair with training fields

For plain text SFT, set the TRL text field explicitly:

training:
  dataset_text_field: text
  max_length: 128
  remove_unused_columns: false

For chat / preference datasets, follow the column layout expected by that TRL trainer (for example messages for chat SFT, or chosen / rejected columns for DPO).