Configuration

BiRNA-BERT

BiRNA-BERT is an RNA transformer that combines nucleotide and byte-pair tokenisation for structural and longer-sequence tasks.

Sources

BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization · Abstract (paragraph 1); Discussion (paragraph 2)

1 evaluation · 1 result

How it worksEvaluated procedure (conceptual)

Conceptual input–method–output guide. Check the procedure text and linked evaluation for fitted components, additional inputs and exact settings.

Sources

BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization · Introduction (paragraph 4); Methods/Adaptive tokenization and dual pretraining (paragraph 1)

Overview

Model type

Transformer representation pipeline; this record is the paper-specific evaluated configuration.

Sources

BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization · Introduction (paragraph 4); Methods/Adaptive tokenization and dual pretraining (paragraph 1)

Biological inputs

RNA nucleotide sequences

Sources

BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization · Methods/Adaptive tokenization and dual pretraining (paragraph 3); Methods/Adaptive tokenization and dual pretraining (paragraph 5)

Outputs

RNA sequence or nucleotide representations for downstream prediction heads

Sources

BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization · Results (paragraph 4); Methods/Statistics and reproducibility (paragraph 1)

Access

Official study implementation and usage documentation: https://github.com/buetnlpbio/BiRNA-BERT/blob/14dc86b1b44c266f01025fc425103f2878646b39/README.md. This pinned documentation revision is not automatically the evaluated weight revision.

Sources

buetnlpbio/BiRNA-BERT README.md · README.md; installation, model download and usage instructions

limited source coverage · Automated source review, 2026-09-16. All specifications and missing details

Evaluations and results

1 evaluation · 1 result. Different protocols are not a single leaderboard.

Filter evaluations

Tested model or methodProtocolDatasetEvaluation setupMetricEvidence origin

Applied filters: All linked evaluations

Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: BiRNA-BERT	Task: extremely long RNA species classification Dataset: extremely long-sequence species classification	0.804 F1 fraction · unknown Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source BiRNA-BERT: extremely long RNA species classification adaptive tokenization on full-length long RNA sequences Aggregation: Not reported BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization · Table 2, BiRNA-BERT row, F1 Score column

Source checking is not independent reproduction. Release 2026-09-29-06401fd5b220.

Use this model

How it works, versions and access

How it works

How the evaluated method works

An ALiBi-equipped transformer encoder is pretrained with two tokenisation schemes. Nucleotide tokens retain position resolution; BPE compresses longer sequences. Tokenisation is selected according to input length.

Sources

BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization · Introduction (paragraph 4); Methods/Adaptive tokenization and dual pretraining (paragraph 1)

What was evaluated

The linked evaluation record identifies BiRNA-BERT: extremely long RNA species classification. Its dataset, split, adaptation and evidence origin remain attached to the reported results.

Sources

BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization · The named evaluation’s methods and comparison table; exact preserved evaluation IDs: evaluation-b2-birna-bert-2025

Strengths, limitations and unresolved questions

Strengths and limitations

Strengths and considerations

Supports nucleotide-level tasks and longer-context tasks within a dual-tokenisation framework.
Sources
BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization · Results/Nucleotide-level task (paragraph 1); Results/NUC tokenization is preferable over BPE if memory allows (paragraph 4)

Limitations and conditions

Relative positional encoding and compressed tokens do not prove unlimited useful biological context; practical memory and task-specific validation still constrain use.
Sources
BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization · Methods/Tokenization strategies for biological foundational models (paragraph 1); Methods/Positional encoding in the transformer architecture (paragraph 1)

Profile review details

Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction.

Stable record: reported-model-d3fd83835a2d44

Specifications

Inputs, training, access and other details

Explanatory profile: limited source coverage · Automated source review, 2026-09-16. Review applies to the cited claims; unresolved fields are listed below. Numerical results retain their own review status.

Inputs, outputs and configuration
Property	Description and evidence
Model type	Transformer representation pipeline; this record is the paper-specific evaluated configuration. Sources BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization · Introduction (paragraph 4); Methods/Adaptive tokenization and dual pretraining (paragraph 1)
Architecture / procedure	An ALiBi-equipped transformer encoder is pretrained with two tokenisation schemes. Nucleotide tokens retain position resolution; BPE compresses longer sequences. Tokenisation is selected according to input length. Sources BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization · Introduction (paragraph 4); Methods/Adaptive tokenization and dual pretraining (paragraph 1)
Biological inputs	RNA nucleotide sequences Sources BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization · Methods/Adaptive tokenization and dual pretraining (paragraph 3); Methods/Adaptive tokenization and dual pretraining (paragraph 5)
Outputs	RNA sequence or nucleotide representations for downstream prediction heads Sources BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization · Results (paragraph 4); Methods/Statistics and reproducibility (paragraph 1)
Parameters	The paper is internally inconsistent: model descriptions use 117M, while Results / Computational resources for pretraining / BiRNA-BERT calls it 116M. An exact reconciled count is unreported. · Not reported in inspected sources Sources BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization · Results (paragraph 2); Results/Comparison of computational efficiency/Computational resources for pretraining/BiRNA-BERT (paragraph 2)
Known versions / configuration	BiRNA-BERT is the comparison-table label; that label does not specify an immutable weight revision. · Not reported in inspected sources Sources BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization · Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label.
Training data / fitting	Approximately 36 million RNAcentral ncRNA sequences; the Methods describe two epochs and 26.42 billion training tokens. Hardware and elapsed-time statements differ across sections and are not treated as a reproducible compute budget. Sources BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization · Methods/Pretraining dataset (paragraph 1); Methods/Pretraining configuration (paragraph 1)
Context limits	For the long-sequence comparison, nucleotide-token inputs are truncated to 1,022 tokens for comparability; BPE inputs are not truncated and reach 807 tokens. ALiBi support does not establish an unlimited practical context. Sources BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization · Results/Impact of dual tokenization is more significant in long sequences (paragraph 2); Results/Empirical perplexity analysis of different RNA language models (paragraph 6)
Access	Official study implementation and usage documentation: https://github.com/buetnlpbio/BiRNA-BERT/blob/14dc86b1b44c266f01025fc425103f2878646b39/README.md. This pinned documentation revision is not automatically the evaluated weight revision. Sources buetnlpbio/BiRNA-BERT README.md · README.md; installation, model download and usage instructions
Code licence	No explicit code licence was established from the paper’s availability statement and inspected repository-root documentation. · Not reported in inspected sources Sources buetnlpbio/BiRNA-BERT README.md · README.md and repository-root licence-file search
Weights licence	The inspected model-access documentation does not explicitly identify terms for this exact evaluated checkpoint or fitted head; repository code terms are shown separately. · Not reported in inspected sources Sources buetnlpbio/BiRNA-BERT README.md · README.md; checkpoint/access documentation and licence scope

Evidence

Source checking verifies the cited claim or transcription. It does not establish independent reproduction.

Evidence table

Inspect claims, sources and review details

Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.

One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.

19 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-09-29-06401fd5b220
Property and statement	Original source and location	Review and provenance
Diagram caption Conceptual input–method–output guide. Check the procedure text and linked evaluation for fitted components, additional inputs and exact settings. Individual claims	BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization Original source ↗ Introduction (paragraph 4); Methods/Adaptive tokenization and dual pretraining (paragraph 1) Version: journal full text in PMC Retrieved: 2026-09-16T10:33:38.292Z	source checked automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.diagram.caption` Source artifact SHA-256: `bf7dbc52b6515301c77010c513f13e676c38395ddc82c20310171f518690c152` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Diagram steps RNA nucleotide sequences BiRNA-BERT RNA sequence or nucleotide representations for downstream prediction heads Individual claims	BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization Original source ↗ Introduction (paragraph 4); Methods/Adaptive tokenization and dual pretraining (paragraph 1) Version: journal full text in PMC Retrieved: 2026-09-16T10:33:38.292Z	source checked automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.diagram.steps` Source artifact SHA-256: `bf7dbc52b6515301c77010c513f13e676c38395ddc82c20310171f518690c152` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Diagram title Evaluated procedure (conceptual) Individual claims	BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization Original source ↗ Introduction (paragraph 4); Methods/Adaptive tokenization and dual pretraining (paragraph 1) Version: journal full text in PMC Retrieved: 2026-09-16T10:33:38.292Z	source checked automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.diagram.title` Source artifact SHA-256: `bf7dbc52b6515301c77010c513f13e676c38395ddc82c20310171f518690c152` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Model type Transformer representation pipeline; this record is the paper-specific evaluated configuration. Individual claims	BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization Original source ↗ Introduction (paragraph 4); Methods/Adaptive tokenization and dual pretraining (paragraph 1) Version: journal full text in PMC Retrieved: 2026-09-16T10:33:38.292Z	source checked automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.0.value` Source artifact SHA-256: `bf7dbc52b6515301c77010c513f13e676c38395ddc82c20310171f518690c152` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Architecture / procedure An ALiBi-equipped transformer encoder is pretrained with two tokenisation schemes. Nucleotide tokens retain position resolution; BPE compresses longer sequences. Tokenisation is selected according to input length. Individual claims	BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization Original source ↗ Introduction (paragraph 4); Methods/Adaptive tokenization and dual pretraining (paragraph 1) Version: journal full text in PMC Retrieved: 2026-09-16T10:33:38.292Z	source checked automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.1.value` Source artifact SHA-256: `bf7dbc52b6515301c77010c513f13e676c38395ddc82c20310171f518690c152` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Weights licence The inspected model-access documentation does not explicitly identify terms for this exact evaluated checkpoint or fitted head; repository code terms are shown separately. Individual claims	buetnlpbio/BiRNA-BERT README.md Original source ↗ README.md; checkpoint/access documentation and licence scope Version: 14dc86b1b44c266f01025fc425103f2878646b39 Retrieved: 2026-09-16T19:54:12.014900+00:00	unreported automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.10.value` Source artifact SHA-256: `00f3835cae38a2307d8095fc5240496e01a9a981c8450230f9ae5722153ac2cc` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Biological inputs RNA nucleotide sequences Individual claims	BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization Original source ↗ Methods/Adaptive tokenization and dual pretraining (paragraph 3); Methods/Adaptive tokenization and dual pretraining (paragraph 5) Version: journal full text in PMC Retrieved: 2026-09-16T10:33:38.292Z	source checked automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.2.value` Source artifact SHA-256: `bf7dbc52b6515301c77010c513f13e676c38395ddc82c20310171f518690c152` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Outputs RNA sequence or nucleotide representations for downstream prediction heads Individual claims	BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization Original source ↗ Results (paragraph 4); Methods/Statistics and reproducibility (paragraph 1) Version: journal full text in PMC Retrieved: 2026-09-16T10:33:38.292Z	source checked automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.3.value` Source artifact SHA-256: `bf7dbc52b6515301c77010c513f13e676c38395ddc82c20310171f518690c152` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Parameters The paper is internally inconsistent: model descriptions use 117M, while Results / Computational resources for pretraining / BiRNA-BERT calls it 116M. An exact reconciled count is unreported. Individual claims	BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization Original source ↗ Results (paragraph 2); Results/Comparison of computational efficiency/Computational resources for pretraining/BiRNA-BERT (paragraph 2) Version: journal full text in PMC Retrieved: 2026-09-16T10:33:38.292Z	unreported automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.4.value` Source artifact SHA-256: `bf7dbc52b6515301c77010c513f13e676c38395ddc82c20310171f518690c152` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Known versions / configuration BiRNA-BERT is the comparison-table label; that label does not specify an immutable weight revision. Individual claims	BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization Original source ↗ Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label. Version: journal full text in PMC Retrieved: 2026-09-16T10:33:38.292Z	unreported automated source review · 2026-09-16 Audit details Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: `attributes.profile.facts.5.value` Source artifact SHA-256: `bf7dbc52b6515301c77010c513f13e676c38395ddc82c20310171f518690c152` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact

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Release 2026-09-29-06401fd5b220 · Record review: needs review

2 source records and release history

BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization · Original source · journal full text in PMC
buetnlpbio/BiRNA-BERT README.md · Original source · 14dc86b1b44c266f01025fc425103f2878646b39

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Technical metadata and extraction receipts

Stable ID: reported-model-d3fd83835a2d44

areas: rna-transcriptomes
entity level: method
version: not stated in table
reported name: BiRNA-BERT
historical missing metadata: checkpoint revision: not_reported_in_legacy_extract; training data: not_reported_in_legacy_extract; licence: not_reported_in_legacy_extract
metadata review scope: historical_missing_metadata preserves the original discovery state. Current descriptive evidence and missingness are recorded in profile.facts; numerical-result review is separate.
legacy kinds: model
entity classification: review date: 2026-09-17; rationale: This source-scoped entry preserves the method/configuration actually named in an evaluation. It is neither a global family identity nor proof of an immutable checkpoint; the linked evaluation retains adaptation, fitting and scoring details.; source ids: birna-bert-2025; source locator: Introduction (paragraph 4); Methods/Adaptive tokenization and dual pretraining (paragraph 1) | Abstract (paragraph 1); Discussion (paragraph 2); ambiguities: Configuration means the source-labelled evaluated identity. It does not establish missing checkpoint hashes, default settings or equivalence to same-named records in other papers.

Related records

model: BiRNA-BERT: extremely long RNA species classification

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