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AgroNT: terminator strength, maize protoplasts

AgroNT learns DNA representations from plant reference genomes for plant molecular prediction tasks.

Sources (7)instadeepai/nucleotide-transformer: README.md; instadeepai/nucleotide-transformer: docs/nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/agro_nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/segment_nt.md; InstaDeepAI/agro-nucleotide-transformer-1b: README.md; InstaDeepAI/agro-nucleotide-transformer-1b: config.json; agront: Journal full-text XML · AgroNT paper Methods: Architecture, Pre-training dataset and Pre-training strategy; official model-card licence metadata

3 evaluations · 3 metric rows

How it worksAgro Nucleotide Transformer workflow
Agro Nucleotide Transformer workflow1. Plant DNA. Then: 2. 6-mer tokenizer. Then: 3. Masked-language transformer. Then: 4. Plant DNA embeddingsAgro Nucleotide Transformer workflow1. Plant DNA. Then: 2. 6-mer tokenizer. Then: 3. Masked-language transformer. Then: 4. Plant DNA embeddingsAgro Nucleotide Transformer workflow1. Plant DNA. Then: 2. 6-mer tokenizer. Then: 3. Masked-language transformer. Then: 4. Plant DNA embeddings

Conceptual summary of the documented data flow; optional inputs and configured downstream stages must be reported for a reproducible evaluation.

Sources (7)instadeepai/nucleotide-transformer: README.md; instadeepai/nucleotide-transformer: docs/nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/agro_nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/segment_nt.md; InstaDeepAI/agro-nucleotide-transformer-1b: README.md; InstaDeepAI/agro-nucleotide-transformer-1b: config.json; agront: Journal full-text XML · AgroNT paper Methods: Architecture, Pre-training dataset and Pre-training strategy; official model-card licence metadata

Overview

Model type

Plant DNA transformer encoder

Sources (7)instadeepai/nucleotide-transformer: README.md; instadeepai/nucleotide-transformer: docs/nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/agro_nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/segment_nt.md; InstaDeepAI/agro-nucleotide-transformer-1b: README.md; InstaDeepAI/agro-nucleotide-transformer-1b: config.json; agront: Journal full-text XML · AgroNT paper Methods: Architecture, Pre-training dataset and Pre-training strategy; official model-card licence metadata

Inputs

Plant DNA sequence, with standalone tokens for ambiguous or remainder bases.

Sources (7)instadeepai/nucleotide-transformer: README.md; instadeepai/nucleotide-transformer: docs/nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/agro_nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/segment_nt.md; InstaDeepAI/agro-nucleotide-transformer-1b: README.md; InstaDeepAI/agro-nucleotide-transformer-1b: config.json; agront: Journal full-text XML · AgroNT paper Methods: Architecture, Pre-training dataset and Pre-training strategy; official model-card licence metadata

Outputs

DNA embeddings for downstream regulatory, RNA-processing or expression tasks.

Sources (7)instadeepai/nucleotide-transformer: README.md; instadeepai/nucleotide-transformer: docs/nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/agro_nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/segment_nt.md; InstaDeepAI/agro-nucleotide-transformer-1b: README.md; InstaDeepAI/agro-nucleotide-transformer-1b: config.json; agront: Journal full-text XML · AgroNT paper Methods: Architecture, Pre-training dataset and Pre-training strategy; official model-card licence metadata

Access

Official project documentation and implementation: https://github.com/instadeepai/nucleotide-transformer

Sources (7)instadeepai/nucleotide-transformer: README.md; instadeepai/nucleotide-transformer: docs/nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/agro_nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/segment_nt.md; InstaDeepAI/agro-nucleotide-transformer-1b: README.md; InstaDeepAI/agro-nucleotide-transformer-1b: config.json; agront: Journal full-text XML · AgroNT paper Methods: Architecture, Pre-training dataset and Pre-training strategy; official model-card licence metadata

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

Evaluations and results

3 evaluations · 3 metric rows. Different protocols are not a single leaderboard.

Filter evaluations

Applied filters: All linked evaluations

Exact evaluated configurations and original reported results
Tested configurationProtocol and datasetFindingEvidence and details
Configuration: AgroNT: terminator strength, maize protoplastsProtocol: PGB terminator strength maize protoplasts: A. thaliana: terminator strength prediction
Dataset subset: terminator strength test sequences: maize protoplasts: A. thaliana (PGB terminator strength split)
0.69 r2
coefficient of determination · higher

Uncertainty: Not reported

Coverage: Not reported scored / Not reported eligible

Author-reported evaluation · source checked
Methods, coverage and source

AgroNT: terminator strength, maize protoplasts on PGB terminator strength maize protoplasts: A. thaliana: terminator strength prediction

170 bp assay sequences; original study train/test datasets, as described in Methods Sec21. R² is scored separately by assay system and sequence class. Fitted model checkpoint, seeds and scored counts are not established here.

Aggregation: Not reported

AgroNT Figure 3f source table · Figures/Fig3_panelf.txt, line 2 (data row 1), column R2; Species=A. thaliana; Model=Maize model; Type=AgroNT
Configuration: AgroNT: terminator strength, maize protoplastsProtocol: PGB terminator strength maize protoplasts: randomized GC sequences: terminator strength prediction
Dataset subset: terminator strength test sequences: maize protoplasts: randomized GC sequences (PGB terminator strength split)
0.68 r2
coefficient of determination · higher

Uncertainty: Not reported

Coverage: Not reported scored / Not reported eligible

Author-reported evaluation · source checked
Methods, coverage and source

AgroNT: terminator strength, maize protoplasts on PGB terminator strength maize protoplasts: randomized GC sequences: terminator strength prediction

170 bp assay sequences; original study train/test datasets, as described in Methods Sec21. R² is scored separately by assay system and sequence class. Fitted model checkpoint, seeds and scored counts are not established here.

Aggregation: Not reported

AgroNT Figure 3f source table · Figures/Fig3_panelf.txt, line 3 (data row 2), column R2; Species=GC; Model=Maize model; Type=AgroNT
Configuration: AgroNT: terminator strength, maize protoplastsProtocol: PGB terminator strength maize protoplasts: Z. mays: terminator strength prediction
Dataset subset: terminator strength test sequences: maize protoplasts: Z. mays (PGB terminator strength split)
0.65 r2
coefficient of determination · higher

Uncertainty: Not reported

Coverage: Not reported scored / Not reported eligible

Author-reported evaluation · source checked
Methods, coverage and source

AgroNT: terminator strength, maize protoplasts on PGB terminator strength maize protoplasts: Z. mays: terminator strength prediction

170 bp assay sequences; original study train/test datasets, as described in Methods Sec21. R² is scored separately by assay system and sequence class. Fitted model checkpoint, seeds and scored counts are not established here.

Aggregation: Not reported

AgroNT Figure 3f source table · Figures/Fig3_panelf.txt, line 4 (data row 3), column R2; Species=Z. mays; Model=Maize model; Type=AgroNT

Source checking is not independent reproduction. Release 2026-09-23-2b89723c6dd9.

Use this model

How it works, versions and access

Related profile: Agro Nucleotide Transformer. This page retains the exact record and its evaluation context.

This configuration

AgroNT with task-specific regression and IA3 fine-tuning, as described in the paper. Exact fitted checkpoint is unreported.

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AgroNT: terminator strength, maize protoplasts
configuration
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entity type
Configuration

How it works

How it works

AgroNT learns DNA representations from plant reference genomes for plant molecular prediction tasks. One-billion-parameter encoder-only transformer with 40 attention blocks, hidden width 1,500, learned positional embeddings and a six-mer masked-language-model head. The documented inputs are plant DNA sequence, with standalone tokens for ambiguous or remainder bases. The output consists of DNA embeddings for downstream regulatory, RNA-processing or expression tasks.

Sources (7)instadeepai/nucleotide-transformer: README.md; instadeepai/nucleotide-transformer: docs/nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/agro_nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/segment_nt.md; InstaDeepAI/agro-nucleotide-transformer-1b: README.md; InstaDeepAI/agro-nucleotide-transformer-1b: config.json; agront: Journal full-text XML · AgroNT paper Methods: Architecture, Pre-training dataset and Pre-training strategy; official model-card licence metadata
Versions and reproducibility

1B_agro_nt pretrained model. 1,024 tokens, approximately 6kb of unambiguous sequence rather than an unconditional 6,144-base guarantee.

Sources (7)instadeepai/nucleotide-transformer: README.md; instadeepai/nucleotide-transformer: docs/nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/agro_nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/segment_nt.md; InstaDeepAI/agro-nucleotide-transformer-1b: README.md; InstaDeepAI/agro-nucleotide-transformer-1b: config.json; agront: Journal full-text XML · AgroNT paper Methods: Architecture, Pre-training dataset and Pre-training strategy; official model-card licence metadata
Strengths, limitations and unresolved questions

Strengths and limitations

Strengths and considerations

Limitations and conditions

Profile review details

Inspected pinned official documentation, relevant implementation files and named primary-paper sections. Claims are limited to those artifacts. Remaining field extraction and identity conflicts are explicit; no new performance claims, model runs or human review are implied.

Stable record: discovery-model-agro-nucleotide-transformer

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
PropertyDescription and evidence
Model typePlant DNA transformer encoder
Sources (7)instadeepai/nucleotide-transformer: README.md; instadeepai/nucleotide-transformer: docs/nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/agro_nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/segment_nt.md; InstaDeepAI/agro-nucleotide-transformer-1b: README.md; InstaDeepAI/agro-nucleotide-transformer-1b: config.json; agront: Journal full-text XML · AgroNT paper Methods: Architecture, Pre-training dataset and Pre-training strategy; official model-card licence metadata
ArchitectureOne-billion-parameter encoder-only transformer with 40 attention blocks, hidden width 1,500, learned positional embeddings and a six-mer masked-language-model head.
Sources (7)instadeepai/nucleotide-transformer: README.md; instadeepai/nucleotide-transformer: docs/nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/agro_nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/segment_nt.md; InstaDeepAI/agro-nucleotide-transformer-1b: README.md; InstaDeepAI/agro-nucleotide-transformer-1b: config.json; agront: Journal full-text XML · AgroNT paper Methods: Architecture, Pre-training dataset and Pre-training strategy; official model-card licence metadata
InputsPlant DNA sequence, with standalone tokens for ambiguous or remainder bases.
Sources (7)instadeepai/nucleotide-transformer: README.md; instadeepai/nucleotide-transformer: docs/nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/agro_nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/segment_nt.md; InstaDeepAI/agro-nucleotide-transformer-1b: README.md; InstaDeepAI/agro-nucleotide-transformer-1b: config.json; agront: Journal full-text XML · AgroNT paper Methods: Architecture, Pre-training dataset and Pre-training strategy; official model-card licence metadata
OutputsDNA embeddings for downstream regulatory, RNA-processing or expression tasks.
Sources (7)instadeepai/nucleotide-transformer: README.md; instadeepai/nucleotide-transformer: docs/nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/agro_nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/segment_nt.md; InstaDeepAI/agro-nucleotide-transformer-1b: README.md; InstaDeepAI/agro-nucleotide-transformer-1b: config.json; agront: Journal full-text XML · AgroNT paper Methods: Architecture, Pre-training dataset and Pre-training strategy; official model-card licence metadata
Parameters1 billion.
Sources (7)instadeepai/nucleotide-transformer: README.md; instadeepai/nucleotide-transformer: docs/nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/agro_nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/segment_nt.md; InstaDeepAI/agro-nucleotide-transformer-1b: README.md; InstaDeepAI/agro-nucleotide-transformer-1b: config.json; agront: Journal full-text XML · AgroNT paper Methods: Architecture, Pre-training dataset and Pre-training strategy; official model-card licence metadata
Known versions1B_agro_nt pretrained model.
Sources (7)instadeepai/nucleotide-transformer: README.md; instadeepai/nucleotide-transformer: docs/nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/agro_nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/segment_nt.md; InstaDeepAI/agro-nucleotide-transformer-1b: README.md; InstaDeepAI/agro-nucleotide-transformer-1b: config.json; agront: Journal full-text XML · AgroNT paper Methods: Architecture, Pre-training dataset and Pre-training strategy; official model-card licence metadata
Training dataApproximately 10.5M sequences from reference genomes of 48 plant species in Ensembl Plants.
Sources (7)instadeepai/nucleotide-transformer: README.md; instadeepai/nucleotide-transformer: docs/nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/agro_nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/segment_nt.md; InstaDeepAI/agro-nucleotide-transformer-1b: README.md; InstaDeepAI/agro-nucleotide-transformer-1b: config.json; agront: Journal full-text XML · AgroNT paper Methods: Architecture, Pre-training dataset and Pre-training strategy; official model-card licence metadata
Training cutoffThe inspected Methods identifies 48 Ensembl Plants reference species. It does not state one latest-deposition date for their combined genomic sequences. · Not reported in inspected sources
Sources (7)instadeepai/nucleotide-transformer: README.md; instadeepai/nucleotide-transformer: docs/nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/agro_nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/segment_nt.md; InstaDeepAI/agro-nucleotide-transformer-1b: README.md; InstaDeepAI/agro-nucleotide-transformer-1b: config.json; agront: Journal full-text XML · AgroNT paper Methods: Architecture, Pre-training dataset and Pre-training strategy; official model-card licence metadata
Context limits1,024 tokens, approximately 6kb of unambiguous sequence rather than an unconditional 6,144-base guarantee.
Sources (7)instadeepai/nucleotide-transformer: README.md; instadeepai/nucleotide-transformer: docs/nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/agro_nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/segment_nt.md; InstaDeepAI/agro-nucleotide-transformer-1b: README.md; InstaDeepAI/agro-nucleotide-transformer-1b: config.json; agront: Journal full-text XML · AgroNT paper Methods: Architecture, Pre-training dataset and Pre-training strategy; official model-card licence metadata
Weights licenceCC-BY-NC-SA-4.0 declared by the official agro-nucleotide-transformer-1b model card.
Sources (7)instadeepai/nucleotide-transformer: README.md; instadeepai/nucleotide-transformer: docs/nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/agro_nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/segment_nt.md; InstaDeepAI/agro-nucleotide-transformer-1b: README.md; InstaDeepAI/agro-nucleotide-transformer-1b: config.json; agront: Journal full-text XML · AgroNT paper Methods: Architecture, Pre-training dataset and Pre-training strategy; official model-card licence metadata
AccessOfficial project documentation and implementation: https://github.com/instadeepai/nucleotide-transformer
Sources (7)instadeepai/nucleotide-transformer: README.md; instadeepai/nucleotide-transformer: docs/nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/agro_nucleotide_transformer.md; instadeepai/nucleotide-transformer: docs/segment_nt.md; InstaDeepAI/agro-nucleotide-transformer-1b: README.md; InstaDeepAI/agro-nucleotide-transformer-1b: config.json; agront: Journal full-text XML · AgroNT paper Methods: Architecture, Pre-training dataset and Pre-training strategy; official model-card licence metadata
Code licenceCC-BY-NC-SA-4.0
Sourcesinstadeepai/nucleotide-transformer: LICENSE.md · LICENSE.md: licence text

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Claims, original sources and review scope · Release 2026-09-23-2b89723c6dd9
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Relationship: family
discovery-model-agro-nucleotide-transformer
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AgroNT: published methods and Figure 3

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Methods: Fine-tuning strategy (Sec16), Promoter and terminator strength prediction (Sec21); Figure 3 caption

Version: Version of record, 2024-07-09; retrieved XML snapshot
Retrieved: 2026-09-23

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Field: links:family:discovery-model-agro-nucleotide-transformer

Claim: agront-2024-fig3f-method-agront-terminator-strength-maize-protoplasts-family-claim

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Fig3_panelf.txt, Type=AgroNT, Model=Maize model; Methods: Fine-tuning strategy (Sec16), Promoter and terminator strength prediction (Sec21); Figure 3 caption
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checkpoint revision: unreported; parameters: unextracted
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