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model · method

BarcodeBERT (4–4-4)

BarcodeBERT learns DNA-barcode representations for biodiversity analysis. This record is the paper’s four-layer, four-head, 4-mer configuration.

1 evaluations · 1 metric rows

At a glance

Explanatory profile: source reviewed · Automated source review, 2026-09-16. This does not change the review status of its results.

Inputs, outputs and configuration
PropertyDescription and evidence
Input preparationPad or truncate to 660 nucleotidesBarcodeBERT: transformers for biodiversity analyses · Sections 3.1–3.2, Figure 2, Section 4.1.3 and Table 1; PMC13008329 fullTextXML
Training sourceCanadian invertebrate DNA barcodesBarcodeBERT: transformers for biodiversity analyses · Sections 3.1–3.2, Figure 2, Section 4.1.3 and Table 1; PMC13008329 fullTextXML
Configuration in this record4–4–4BarcodeBERT: transformers for biodiversity analyses · Sections 3.1–3.2, Figure 2, Section 4.1.3 and Table 1; PMC13008329 fullTextXML
Model typeNot extracted or verified for this record.
Context limitsNot extracted or verified for this record.
AccessNot extracted or verified for this record.
Code licenceNot extracted or verified for this record.
Weights licenceNot extracted or verified for this record.

How it works

Conceptual procedure

Schematic of the documented input, computation and output; not an executable configuration.

Conceptual procedureCOI barcode. Then: Non-overlapping 4-mers. Then: Four transformer layers. Then: Average embedding. Then: Genus by 1-nearest neighbourCOI barcodeNon-overlapping 4-mersFour transformer layersAverage embeddingGenus by 1-nearest neighbour
Read the diagram as text
  1. COI barcode
  2. Non-overlapping 4-mers
  3. Four transformer layers
  4. Average embedding
  5. Genus by 1-nearest neighbour
BarcodeBERT: transformers for biodiversity analyses · Sections 3.1–3.2, Figure 2, Section 4.1.3 and Table 1; PMC13008329 fullTextXML

Non-overlapping DNA 4-mers enter four transformer layers. Masked-token pretraining uses barcode sequences, with random offsets to reduce tokenisation sensitivity. Average pooling produces a barcode embedding. The linked evaluation compares embeddings by cosine similarity to assign a genus from the nearest reference.

BarcodeBERT: transformers for biodiversity analyses · Sections 3.1–3.2, Figure 2, Section 4.1.3 and Table 1; PMC13008329 fullTextXML

Benchmarks and results

Release 2026-09-16-d74d282221a9 · 1 evaluation · 1 metric row. Different protocols are not a single leaderboard.

Results grouped by the exact reported evaluation
Metric and findingCoverage and uncertaintyEvidence
BarcodeBERT (4–4-4): unseen-species genus classification

genus-level nearest-neighbor probe on species unseen in training

Author-reported evaluation · Evaluation metadata: needs review

78.5% accuracy

Unit: percent · Direction: unknown

Uncertainty: not reported in legacy extract

Scored: Not reported · Eligible: Not reported

source checkedBarcodeBERT: transformers for biodiversity analyses · Table 1, BarcodeBERT (4–4-4) row, unseen-species genus-level 1-NN Acc (%) column

Source checking is not independent reproduction.

Strengths and limitations

Strengths supported by sources

Limitations and conditions

  • The unseen-species test retains known genera. It is not a demonstration of recognising entirely new genera; BLAST remains a meaningful comparator.BarcodeBERT: transformers for biodiversity analyses · Sections 3.1–3.2, Figure 2, Section 4.1.3 and Table 1; PMC13008329 fullTextXML
Profile review details

Primary project documentation or paper inspected for the explanatory claims and cited locations. Reviewed coverage concerns this narrative, not complete metadata, independent reproduction or a performance ranking.

Stable record: reported-model-05103f72325fe5

Sources and history

Release 2026-09-16-d74d282221a9 · Record review: needs review

Download this release
Technical metadata and extraction receipts

Stable ID: reported-model-05103f72325fe5

areas
dna-genomes
entity level
method
version
4–4–4
reported name
BarcodeBERT (4–4-4)
missing metadata
checkpoint revision: not_reported_in_legacy_extract; training data: not_reported_in_legacy_extract; licence: not_reported_in_legacy_extract
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