Montre

A modern, embeddable corpus query engine with first-class support for aligned corpora.

montre (/mɔ̃tʁ/): “shows,” “reveals,” “makes visible” — from French montrer, “to show.” The Latin root is monstrare, “to point out, indicate.”

No server, external services, or prerequisites.

A corpus is a self-contained directory with its own data, indexes, and (optionally) alignments. Build it in one line from your annotation files, or from a TOML manifest describing multiple components.

Designed to be used from the CLI or embedded directly in Julia or Python.

---

Install

curl -fsSL https://raw.githubusercontent.com/myersm0/montre/main/install.sh | sh

Quick start

# Build a corpus from a directory of CoNLL-U files:
montre build -i data/maupassant/ -o my-corpus/

# Query
montre query my-corpus/ '[pos="ADJ"] [pos="NOUN"]'

# Count
montre count my-corpus/ '[pos="ADJ"] [pos="NOUN"]'
montre count my-corpus/ '[pos="NOUN"]' --by-document
montre count my-corpus/ '[pos="NOUN"]' --by-component

# Filter
montre query my-corpus/ '[pos="ADJ"] [pos="NOUN"]' --document la-parure
montre query my-corpus/ '[pos="ADJ"] [pos="NOUN"]' --component fr

# Inspect
montre info my-corpus/
montre docs my-corpus/
montre layers my-corpus/
montre vocab my-corpus/ pos
montre vocab my-corpus/ lemma --top 50 --component fr

Query language

Montre uses a CQL-based language, extended with labels, constraints, and alignment-aware operations.

Core patterns

# Token queries
[pos="NOUN"]
[lemma="maison"]
[word="chat" & pos="NOUN"]
[lemma=/^un.*/]
[pos!="PUNCT"]

# Sequences
[pos="DET"] [pos="ADJ"]* [pos="NOUN"]

# Quantifiers
[pos="ADJ"]+
[pos="ADJ"]*
[pos="ADJ"]?
[pos="ADJ"]{2,4}

# Alternation
([pos="ADJ"] | [pos="ADV"])+ [pos="NOUN"]

Structural constraints

[pos="DET"] [pos="NOUN"] within s
[lemma="chat"] within doc

Morphological features

Requires using the flag --decompose-feats at build time.

[pos="NOUN" & feats.Number="Plur"]
[feats.Gender="Masc" & feats.Tense="Past"]

Component and document filtering

[pos="NOUN"] within component:fr
[pos="ADJ"] [pos="NOUN"] within doc:"la-parure","boule-de-suif"

Labeled captures and global constraints

a:[pos="NOUN"] []* b:[pos="NOUN"] :: a.lemma = b.lemma
a:[pos="ADJ"] b:[pos="NOUN"] :: a.lemma != b.lemma
a:[] []{0,20} b:[] :: distance(a,b) >= 5

Constraints are evaluated over full matches using labeled spans.

Parallel corpus support

Montre was designed from the ground up specifically for parallel corpora.

Montre treats a parallel corpus as a single object with multiple components and explicit alignment relations, rather than as separate corpora joined at query time.

Key features

• Multiple components (languages, editions, translations)
• Named alignments at any span level (sentence, paragraph, stanza)
• Multiple competing alignment sets (LaBSE, vecalign, manual)
• Alignment projection between components

Example

# Query French, project to English
[lemma="maison"] within component:fr =labse=>

This enables:

• tracing translations across languages
• detecting omissions or expansions
• comparing editions or variants

Build a multi-component corpus

[corpus]
name = "isosceles"
decompose_feats = true

[components.maupassant-fr]
path = "data/maupassant/fr/conllu/"
language = "fr"

[components.maupassant-en]
path = "data/maupassant/en/conllu/"
language = "en"

[alignments.labse]
source = "maupassant-fr"
target = "maupassant-en"
edges = "alignments/labse/"
source_layer = "sentence"
target_layer = "sentence"

montre build -m corpus.toml -o my-corpus/

Performance

Montre is competitive with established corpus engines while prioritizing structural flexibility and embeddability.

On a 1.5M token corpus (Maupassant French/English, Apple M4 Max):

Query	Matches	Time
[pos="NOUN"]	244,184	0.6ms
[pos="ADJ"] [pos="NOUN"]	30,672	12ms
[pos="ADJ"]? [pos="NOUN"]	272,019	71ms
([pos="ADJ"] | [pos="ADV"])+ [pos="NOUN"]	33,444	27ms
([pos="ADJ"] | [pos="DET"])+ [pos="NOUN"]	198,735	71ms

Key properties:

• Quantifiers use a run-based execution model (scales with matches, not corpus size)
• --count-only avoids hit allocation entirely (nanosecond-scale for simple queries)
• Memory-mapped indexes reduce load time and memory footprint by an order of magnitude

Bindings

Montre exposes a C FFI for embedding in other languages.

Julia (almost complete)

Montre.jl

using Montre

corpus = open_corpus("./my-corpus")
hits = query(corpus, "[pos=\"ADJ\"] [pos=\"NOUN\"]")

for line in concordance(corpus, hits)
    println(line)
end

Python (early)

Bindings via PyO3 are in progress.

import montre

corpus = montre.open("./my-corpus")
for hit in corpus.query('[pos="DET"] [pos="NOUN"]'):
    print(hit.start, hit.end)

Roadmap

Coming soon:

• Statistics: group, collocation
• Python bindings (feature-complete, pip install)
• REPL (persistent corpus session)
• TUI for interactive exploration
• Support for additional input formats (VRT, Stanza JSON, TEI)

Citing Montre

A paper describing Montre is in preparation. In the meantime, if you use Montre in published research, please cite:

@software{myers-montre,
  author       = {Myers, Michael J.},
  title        = {Montre: A Modern Corpus Query Engine for Aligned Corpora},
  year         = {2026},
  url          = {https://github.com/myersm0/montre},
  version      = {0.4.0}
}

License

Apache-2.0