Masked language modeling

Predict masked-out words. This task is self-supervised: there is no label — the training side masks tokens on the fly. Each sample is one .txt file holding a space-separated token sequence, listed in a manifest CSV.

Folder layout

/data/shared/primekg-mlm/
├── labels.csv
└── sequences/
    ├── seq_0000001.txt
    ├── seq_0000002.txt
    └── ...

The folder is named sequences (not texts) — the ingestor reserves sequences/ for pre-tokenized data — and sits next to the manifest CSV.
One sequence per file, tokens separated by spaces, UTF-8 encoded. Every file must end in .txt. The ingestor also accepts .text, but training opens only <filename>.txt, so a .text dataset ingests and then fails to train. Example from the shipped sample: Lepirudin indication Huntington phenotype_present Chorea associated_with Dystonia.
The shipped sample also contains a tokenizer.json next to the CSV. The current ingestor neither reads nor copies it; it is part of the sample only.

Manifest CSV

filename
seq_0000001
seq_0000002
seq_0000003

Column	Required	Meaning
`filename`	yes, exactly this name	The sequence file, with or without extension.
`extension`	no	Present in the shipped sample (`'.txt'`); not read by the ingestor.

The task reads no label column. Setting label: in ingest.yaml is rejected. A label column in the manifest itself is not rejected: the ingest goes on without it and warns: “Column(s) [‘label’] in the manifest are ignored for masked_language_modeling and will not be stored”. The same goes for any other column besides filename and extension. Remove such columns to silence the warning.

Ingest with the CLI

Each split is one folder laid out as above. Ingest the training and the test split separately:

tracebloc data ingest ./primekg-mlm-train --task masked_language_modeling --name primekg_mlm_train --intent train --holds open_data
tracebloc data ingest ./primekg-mlm-test  --task masked_language_modeling --name primekg_mlm_test  --intent test --holds open_data

--holds open_data marks the dataset as open data. If your data is about patients, pass --holds patient_data; if it was generated, pass --holds synthetic_data. Leave --holds out on a terminal and the CLI asks; in a script, the dataset is marked as patient data. The mark decides which use case types you can create on the dataset. Do not pass --label-column: the CLI rejects it for this task because it trains on the text itself.

ingest.yaml

apiVersion: tracebloc.io/v1
kind: IngestConfig
category: masked_language_modeling
table: primekg_mlm_train
intent: train
csv: /data/shared/primekg-mlm/labels.csv
sequences: /data/shared/primekg-mlm/sequences/

Field	Required	Meaning
`csv`	yes	Path to the manifest CSV.
`sequences`	yes	The `sequences/` folder. Its parent becomes the dataset root.
`label`	must not be set	Self-supervised task.
`schema`	no	Extra typed columns only. Never a reserved column name such as `filename` or `extension`.
`spec.file_options.extension`	no	Leave it unset. The default `.txt` is the only extension training reads.
`language`, `normalization`	no	Dataset language and applied normalization.

What the ingestor checks

Check	Rejects
File type	Files under `sequences/` with a different extension than configured, or mixed extensions.
Text content	NUL bytes or invalid UTF-8; empty files produce a warning.
Data types	With a `schema`: values that do not match their declared type.

Plus the checks every ingest runs, except label diversity (no label). For text tasks the ingestor also records a data-derived text profile — Unicode-script mix and document-length distribution, never the text itself — so the platform can warn when a model’s tokenizer is a poor fit for the dataset.

Sample dataset

The template ships five knowledge-graph random-walk sequences and a five-row manifest. The manifest ships as labels_file_sample.csv: rename it to labels.csv before you ingest the folder, because the CLI reads only that name. For the Helm chart, stage the renamed folder so that it matches the paths in the ingest.yaml above.

Score formulas

When you define a use case on this data, the platform currently accepts Masked Token Accuracy and Perplexity.

Next steps

Ingest the data: Prepare Data
Shared rules for every template: Dataset templates

Overview

Create a Use Case

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Environment Setup

Tools & Help

Releases

Masked language modeling

Folder layout

Manifest CSV

Ingest with the CLI

ingest.yaml

What the ingestor checks

Sample dataset

Score formulas

Next steps

​Folder layout

​Manifest CSV

​Ingest with the CLI

​ingest.yaml

​What the ingestor checks

​Sample dataset

​Score formulas

​Next steps

Folder layout

Manifest CSV

Ingest with the CLI

ingest.yaml

What the ingestor checks

Sample dataset

Score formulas

Next steps