Configuration

Nucleotide Transformer (fine-tuned)

Nucleotide Transformer is a family of DNA encoders pretrained on human or multispecies sequence corpora.

Sources (5)

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; nt: Journal full-text XML · docs/nucleotide_transformer.md: Model Variants and Sizes, Tokenization and How to use; Nucleotide Transformer paper Methods: Architecture, Pre-training datasets and Training

22 evaluations · 22 results

How it worksNucleotide Transformer workflow

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

Sources (5)

Overview

Model type

DNA transformer encoder family

Inputs

DNA sequences tokenized into 6-mers, with single-base handling of N and remainder bases.

Outputs

Contextual representations used in specified downstream prediction workflows.

Access

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

Sources (5)

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

Evaluations and results

22 evaluations · 22 results. 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: Nucleotide Transformer (fine-tuned)	Task: DART-Eval CA-AUROC-GM12878: Chromatin activity prediction, GM12878, positives against negatives Dataset subset: ENCODE chromatin accessibility peaks in five cell lines (DART-Eval split)	0.938 auroc fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Nucleotide Transformer (fine-tuned) on DART-Eval CA-AUROC-GM12878: Chromatin activity prediction, GM12878, positives against negatives Separating positive GM12878 peaks from matched negatives. Aggregation: Not reported DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA · Table 5, row(Fine-Tuned NT), column(CA-AUROC-GM12878)
Configuration: Nucleotide Transformer (fine-tuned)	Task: DART-Eval CA-AUROC-H1ESC: Chromatin activity prediction, H1ESC, positives against negatives Dataset subset: ENCODE chromatin accessibility peaks in five cell lines (DART-Eval split)	0.958 auroc fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Nucleotide Transformer (fine-tuned) on DART-Eval CA-AUROC-H1ESC: Chromatin activity prediction, H1ESC, positives against negatives Separating positive H1ESC peaks from matched negatives. Aggregation: Not reported DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA · Table 5, row(Fine-Tuned NT), column(CA-AUROC-H1ESC)
Configuration: Nucleotide Transformer (fine-tuned)	Task: DART-Eval CA-AUROC-HEPG2: Chromatin activity prediction, HEPG2, positives against negatives Dataset subset: ENCODE chromatin accessibility peaks in five cell lines (DART-Eval split)	0.922 auroc fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Nucleotide Transformer (fine-tuned) on DART-Eval CA-AUROC-HEPG2: Chromatin activity prediction, HEPG2, positives against negatives Separating positive HEPG2 peaks from matched negatives. Aggregation: Not reported DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA · Table 5, row(Fine-Tuned NT), column(CA-AUROC-HEPG2)
Configuration: Nucleotide Transformer (fine-tuned)	Task: DART-Eval CA-AUROC-IMR90: Chromatin activity prediction, IMR90, positives against negatives Dataset subset: ENCODE chromatin accessibility peaks in five cell lines (DART-Eval split)	0.975 auroc fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Nucleotide Transformer (fine-tuned) on DART-Eval CA-AUROC-IMR90: Chromatin activity prediction, IMR90, positives against negatives Separating positive IMR90 peaks from matched negatives. Aggregation: Not reported DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA · Table 5, row(Fine-Tuned NT), column(CA-AUROC-IMR90)
Configuration: Nucleotide Transformer (fine-tuned)	Task: DART-Eval CA-AUROC-K562: Chromatin activity prediction, K562, positives against negatives Dataset subset: ENCODE chromatin accessibility peaks in five cell lines (DART-Eval split)	0.941 auroc fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Nucleotide Transformer (fine-tuned) on DART-Eval CA-AUROC-K562: Chromatin activity prediction, K562, positives against negatives Separating positive K562 peaks from matched negatives. Aggregation: Not reported DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA · Table 5, row(Fine-Tuned NT), column(CA-AUROC-K562)
Configuration: Nucleotide Transformer (fine-tuned)	Task: DART-Eval CA-SPEARMAN-GM12878: Chromatin activity prediction, GM12878, positives only Dataset subset: ENCODE chromatin accessibility peaks in five cell lines (DART-Eval split)	0.515 spearman_r correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Nucleotide Transformer (fine-tuned) on DART-Eval CA-SPEARMAN-GM12878: Chromatin activity prediction, GM12878, positives only Rank correlation with measured accessibility among positive GM12878 peaks. Aggregation: Not reported DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA · Table 5, row(Fine-Tuned NT), column(CA-SPEARMAN-GM12878)
Configuration: Nucleotide Transformer (fine-tuned)	Task: DART-Eval CA-SPEARMAN-H1ESC: Chromatin activity prediction, H1ESC, positives only Dataset subset: ENCODE chromatin accessibility peaks in five cell lines (DART-Eval split)	0.737 spearman_r correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Nucleotide Transformer (fine-tuned) on DART-Eval CA-SPEARMAN-H1ESC: Chromatin activity prediction, H1ESC, positives only Rank correlation with measured accessibility among positive H1ESC peaks. Aggregation: Not reported DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA · Table 5, row(Fine-Tuned NT), column(CA-SPEARMAN-H1ESC)
Configuration: Nucleotide Transformer (fine-tuned)	Task: DART-Eval CA-SPEARMAN-HEPG2: Chromatin activity prediction, HEPG2, positives only Dataset subset: ENCODE chromatin accessibility peaks in five cell lines (DART-Eval split)	0.513 spearman_r correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Nucleotide Transformer (fine-tuned) on DART-Eval CA-SPEARMAN-HEPG2: Chromatin activity prediction, HEPG2, positives only Rank correlation with measured accessibility among positive HEPG2 peaks. Aggregation: Not reported DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA · Table 5, row(Fine-Tuned NT), column(CA-SPEARMAN-HEPG2)
Configuration: Nucleotide Transformer (fine-tuned)	Task: DART-Eval CA-SPEARMAN-IMR90: Chromatin activity prediction, IMR90, positives only Dataset subset: ENCODE chromatin accessibility peaks in five cell lines (DART-Eval split)	0.489 spearman_r correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Nucleotide Transformer (fine-tuned) on DART-Eval CA-SPEARMAN-IMR90: Chromatin activity prediction, IMR90, positives only Rank correlation with measured accessibility among positive IMR90 peaks. Aggregation: Not reported DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA · Table 5, row(Fine-Tuned NT), column(CA-SPEARMAN-IMR90)
Configuration: Nucleotide Transformer (fine-tuned)	Task: DART-Eval CA-SPEARMAN-K562: Chromatin activity prediction, K562, positives only Dataset subset: ENCODE chromatin accessibility peaks in five cell lines (DART-Eval split)	0.583 spearman_r correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Nucleotide Transformer (fine-tuned) on DART-Eval CA-SPEARMAN-K562: Chromatin activity prediction, K562, positives only Rank correlation with measured accessibility among positive K562 peaks. Aggregation: Not reported DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA · Table 5, row(Fine-Tuned NT), column(CA-SPEARMAN-K562)
Configuration: Nucleotide Transformer (fine-tuned)	Task: DART-Eval CTS-ACC: Cell-type-specific element classification, overall accuracy Dataset subset: ENCODE chromatin accessibility peaks in five cell lines (DART-Eval split)	0.632 accuracy fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Nucleotide Transformer (fine-tuned) on DART-Eval CTS-ACC: Cell-type-specific element classification, overall accuracy Classify which of five cell lines a accessible element belongs to. Aggregation: Not reported DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA · Table 4, row(Fine-Tuned Nucleotide Transformer), column(CTS-ACC)
Configuration: Nucleotide Transformer (fine-tuned)	Task: DART-Eval CTS-GM12878: Cell-type-specific element classification, GM12878 Dataset subset: ENCODE chromatin accessibility peaks in five cell lines (DART-Eval split)	0.88 auroc fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Nucleotide Transformer (fine-tuned) on DART-Eval CTS-GM12878: Cell-type-specific element classification, GM12878 One-against-rest AUROC for GM12878 accessible elements. Aggregation: Not reported DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA · Table 4, row(Fine-Tuned Nucleotide Transformer), column(CTS-GM12878)
Configuration: Nucleotide Transformer (fine-tuned)	Task: DART-Eval CTS-H1ESC: Cell-type-specific element classification, H1ESC Dataset subset: ENCODE chromatin accessibility peaks in five cell lines (DART-Eval split)	0.925 auroc fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Nucleotide Transformer (fine-tuned) on DART-Eval CTS-H1ESC: Cell-type-specific element classification, H1ESC One-against-rest AUROC for H1ESC accessible elements. Aggregation: Not reported DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA · Table 4, row(Fine-Tuned Nucleotide Transformer), column(CTS-H1ESC)
Configuration: Nucleotide Transformer (fine-tuned)	Task: DART-Eval CTS-HEPG2: Cell-type-specific element classification, HEPG2 Dataset subset: ENCODE chromatin accessibility peaks in five cell lines (DART-Eval split)	0.881 auroc fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Nucleotide Transformer (fine-tuned) on DART-Eval CTS-HEPG2: Cell-type-specific element classification, HEPG2 One-against-rest AUROC for HEPG2 accessible elements. Aggregation: Not reported DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA · Table 4, row(Fine-Tuned Nucleotide Transformer), column(CTS-HEPG2)
Configuration: Nucleotide Transformer (fine-tuned)	Task: DART-Eval CTS-IMR90: Cell-type-specific element classification, IMR90 Dataset subset: ENCODE chromatin accessibility peaks in five cell lines (DART-Eval split)	0.92 auroc fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Nucleotide Transformer (fine-tuned) on DART-Eval CTS-IMR90: Cell-type-specific element classification, IMR90 One-against-rest AUROC for IMR90 accessible elements. Aggregation: Not reported DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA · Table 4, row(Fine-Tuned Nucleotide Transformer), column(CTS-IMR90)
Configuration: Nucleotide Transformer (fine-tuned)	Task: DART-Eval CTS-K562: Cell-type-specific element classification, K562 Dataset subset: ENCODE chromatin accessibility peaks in five cell lines (DART-Eval split)	0.867 auroc fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Nucleotide Transformer (fine-tuned) on DART-Eval CTS-K562: Cell-type-specific element classification, K562 One-against-rest AUROC for K562 accessible elements. Aggregation: Not reported DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA · Table 4, row(Fine-Tuned Nucleotide Transformer), column(CTS-K562)
Configuration: Nucleotide Transformer (fine-tuned)	Task: DART-Eval REI-ABS: Regulatory element identification, absolute accuracy Dataset subset: ENCODE cCREs against dinucleotide-shuffled backgrounds (DART-Eval split)	0.92 absolute_accuracy fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Nucleotide Transformer (fine-tuned) on DART-Eval REI-ABS: Regulatory element identification, absolute accuracy Distinguish ENCODE cCREs from dinucleotide-shuffled background sequences. The setting the model was run in is part of the method name. Aggregation: Not reported DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA · Table 3, row(Nucleotide Transformer), column(fine-tuned absolute_accuracy)
Configuration: Nucleotide Transformer (fine-tuned)	Task: DART-Eval REI-PAIR: Regulatory element identification, paired accuracy Dataset subset: ENCODE cCREs against dinucleotide-shuffled backgrounds (DART-Eval split)	0.976 paired_accuracy fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Nucleotide Transformer (fine-tuned) on DART-Eval REI-PAIR: Regulatory element identification, paired accuracy Distinguish ENCODE cCREs from dinucleotide-shuffled background sequences. The setting the model was run in is part of the method name. Aggregation: Not reported DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA · Table 3, row(Nucleotide Transformer), column(fine-tuned paired_accuracy)
Configuration: Nucleotide Transformer (fine-tuned)	Task: DART-Eval VS-AFRICAN-AUROC: Variant scoring on Chromatin QTLs in African LCLs, AUROC Dataset subset: Chromatin QTLs in African LCLs (DART-Eval split)	0.623 auroc fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Nucleotide Transformer (fine-tuned) on DART-Eval VS-AFRICAN-AUROC: Variant scoring on Chromatin QTLs in African LCLs, AUROC Score the effect of a variant on chromatin accessibility, against the measured QTL call. Aggregation: Not reported DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA · Table 6, row(African NT), column(fine-tuned auroc)
Configuration: Nucleotide Transformer (fine-tuned)	Task: DART-Eval VS-AFRICAN-PEARSON_R: Variant scoring on Chromatin QTLs in African LCLs, Pearson r Dataset subset: Chromatin QTLs in African LCLs (DART-Eval split)	0.23 pearson_r correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Nucleotide Transformer (fine-tuned) on DART-Eval VS-AFRICAN-PEARSON_R: Variant scoring on Chromatin QTLs in African LCLs, Pearson r Score the effect of a variant on chromatin accessibility, against the measured QTL call. Aggregation: Not reported DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA · Table 6, row(African NT), column(fine-tuned pearson_r)
Configuration: Nucleotide Transformer (fine-tuned)	Task: DART-Eval VS-YORUBAN-AUROC: Variant scoring on DNase QTLs in Yoruban LCLs, AUROC Dataset subset: DNase QTLs in Yoruban LCLs (DART-Eval split)	0.67 auroc fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Nucleotide Transformer (fine-tuned) on DART-Eval VS-YORUBAN-AUROC: Variant scoring on DNase QTLs in Yoruban LCLs, AUROC Score the effect of a variant on chromatin accessibility, against the measured QTL call. Aggregation: Not reported DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA · Table 6, row(Yoruban NT), column(fine-tuned auroc)
Configuration: Nucleotide Transformer (fine-tuned)	Task: DART-Eval VS-YORUBAN-PEARSON_R: Variant scoring on DNase QTLs in Yoruban LCLs, Pearson r Dataset subset: DNase QTLs in Yoruban LCLs (DART-Eval split)	0.507 pearson_r correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Nucleotide Transformer (fine-tuned) on DART-Eval VS-YORUBAN-PEARSON_R: Variant scoring on DNase QTLs in Yoruban LCLs, Pearson r Score the effect of a variant on chromatin accessibility, against the measured QTL call. Aggregation: Not reported DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA · Table 6, row(Yoruban NT), column(fine-tuned pearson_r)

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

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How it works, versions and access

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

This configuration

DNA language model evaluated by the DART-Eval authors in the fine-tuned setting.

record: Nucleotide Transformer (fine-tuned)
configuration: Not reported
entity type: Configuration

How it works

Nucleotide Transformer is a family of DNA encoders pretrained on human or multispecies sequence corpora. Encoder-only transformers with 6-mer tokens; v1 uses learned positional encodings and v2 uses rotary positions and SwiGLU. The documented inputs are DNA sequences tokenized into 6-mers, with single-base handling of N and remainder bases. The output consists of contextual representations used in specified downstream prediction workflows.

Sources (5)

Versions and reproducibility

NT-v1 human-reference/1000G/multispecies variants and NT-v2 50M/100M/250M/500M. NT-v3 is a separate architecture described elsewhere in the repository. v1: approximately 6kb; v2: 2,048 tokens, approximately 12kb. Exact base count depends on special and ambiguous tokens.

Sources (5)

Strengths, limitations and unresolved questions

Strengths and limitations

Strengths and considerations

The family provides multiple sizes and training corpora, allowing those factors to be compared explicitly.
Sources (5)
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; nt: Journal full-text XML · docs/nucleotide_transformer.md: Model Variants and Sizes, Tokenization and How to use; Nucleotide Transformer paper Methods: Architecture, Pre-training datasets and Training

Limitations and conditions

Training corpora and architectures differ across v1 and v2. A family-level name is insufficient to reproduce a score.
Sources (5)
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; nt: Journal full-text XML · docs/nucleotide_transformer.md: Model Variants and Sizes, Tokenization and How to use; Nucleotide Transformer paper Methods: Architecture, Pre-training datasets and Training

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-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
Property	Description and evidence
Model type	DNA transformer encoder family Sources (5) 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; nt: Journal full-text XML · docs/nucleotide_transformer.md: Model Variants and Sizes, Tokenization and How to use; Nucleotide Transformer paper Methods: Architecture, Pre-training datasets and Training
Architecture	Encoder-only transformers with 6-mer tokens; v1 uses learned positional encodings and v2 uses rotary positions and SwiGLU. Sources (5) 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; nt: Journal full-text XML · docs/nucleotide_transformer.md: Model Variants and Sizes, Tokenization and How to use; Nucleotide Transformer paper Methods: Architecture, Pre-training datasets and Training
Inputs	DNA sequences tokenized into 6-mers, with single-base handling of N and remainder bases. Sources (5) 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; nt: Journal full-text XML · docs/nucleotide_transformer.md: Model Variants and Sizes, Tokenization and How to use; Nucleotide Transformer paper Methods: Architecture, Pre-training datasets and Training
Outputs	Contextual representations used in specified downstream prediction workflows. Sources (5) 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; nt: Journal full-text XML · docs/nucleotide_transformer.md: Model Variants and Sizes, Tokenization and How to use; Nucleotide Transformer paper Methods: Architecture, Pre-training datasets and Training
Parameters	50M to 2.5B across the documented v1/v2 family. Sources (5) 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; nt: Journal full-text XML · docs/nucleotide_transformer.md: Model Variants and Sizes, Tokenization and How to use; Nucleotide Transformer paper Methods: Architecture, Pre-training datasets and Training
Known versions	NT-v1 human-reference/1000G/multispecies variants and NT-v2 50M/100M/250M/500M. NT-v3 is a separate architecture described elsewhere in the repository. Sources (5) 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; nt: Journal full-text XML · docs/nucleotide_transformer.md: Model Variants and Sizes, Tokenization and How to use; Nucleotide Transformer paper Methods: Architecture, Pre-training datasets and Training
Training data	v1 variants use GRCh38, 3,202 human genomes or 850 multispecies genomes; v2 uses the multispecies corpus. Sources (5) 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; nt: Journal full-text XML · docs/nucleotide_transformer.md: Model Variants and Sizes, Tokenization and How to use; Nucleotide Transformer paper Methods: Architecture, Pre-training datasets and Training
Training cutoff	The paper specifies human-reference, 1000 Genomes and multispecies training collections by variant. A single latest-deposition date for all sequences is not supplied in the inspected pretraining-data section. · Not reported in inspected sources Sources (5) 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; nt: Journal full-text XML · docs/nucleotide_transformer.md: Model Variants and Sizes, Tokenization and How to use; Nucleotide Transformer paper Methods: Architecture, Pre-training datasets and Training
Context limits	v1: approximately 6kb; v2: 2,048 tokens, approximately 12kb. Exact base count depends on special and ambiguous tokens. Sources (5) 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; nt: Journal full-text XML · docs/nucleotide_transformer.md: Model Variants and Sizes, Tokenization and How to use; Nucleotide Transformer paper Methods: Architecture, Pre-training datasets and Training
Weights licence	Separate checkpoint-distribution terms are not stated in the inspected release documentation and licence material. The source-code licence alone is not recorded as an explicit weight grant. · Not reported in inspected sources Sources (6) 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; nt: Journal full-text XML; instadeepai/nucleotide-transformer: LICENSE.md · docs/nucleotide_transformer.md: Model Variants and Sizes, Tokenization and How to use; Nucleotide Transformer paper Methods: Architecture, Pre-training datasets and Training; LICENSE.md: licence text
Access	Official project documentation and implementation: https://github.com/instadeepai/nucleotide-transformer Sources (5) 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; nt: Journal full-text XML · docs/nucleotide_transformer.md: Model Variants and Sizes, Tokenization and How to use; Nucleotide Transformer paper Methods: Architecture, Pre-training datasets and Training
Code licence	CC-BY-NC-SA-4.0 Sources instadeepai/nucleotide-transformer: LICENSE.md · LICENSE.md: licence text

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.

2 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
Relationship: family discovery-model-nucleotide-transformer Individual claims	DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA Original source ↗ DART-Eval Table 1: Nucleotide Transformer, v2-500m-multi-species; adaptation-specific result tables; source-labelled configuration Nucleotide Transformer (fine-tuned) \| Existing reviewed locator: Table 1, row(Nucleotide Transformer) Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 2412.05430v1 Retrieved: 2026-09-17T07:56:09.182117+00:00	source checked automated source review · 2026-09-23 Audit details Source review establishes this relationship only. Exact evaluated configurations and original numerical review status remain unchanged. Explicit 500M-v2 backbone. Link broad NT family; do not attribute these scores to catalog-model-nt-v2, which documents 50M. Field: `links:family:discovery-model-nucleotide-transformer` Claim: model-evaluation-identity-8c96cba8ea56bc16f2e0 Source artifact SHA-256: `4194b137ba55c9a2c269d119a9afec6ae1bb0feaf17d91433ae483c41221a56b` Hash scope: Exact retrieved primary paper artifact bytes. Inspected artifact
Relationship: family discovery-model-nucleotide-transformer Individual claims	instadeepai/nucleotide-transformer: docs/nucleotide_transformer.md Original source ↗ DART-Eval Table 1: Nucleotide Transformer, v2-500m-multi-species; adaptation-specific result tables; source-labelled configuration Nucleotide Transformer (fine-tuned) \| Existing reviewed locator: Table 1, row(Nucleotide Transformer) Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 2dc37b86e16a6970fbc731751f7719d9f676f7f9 Retrieved: 2026-09-16T19:46:19.364532+00:00	source checked automated source review · 2026-09-23 Audit details Source review establishes this relationship only. Exact evaluated configurations and original numerical review status remain unchanged. Explicit 500M-v2 backbone. Link broad NT family; do not attribute these scores to catalog-model-nt-v2, which documents 50M. Field: `links:family:discovery-model-nucleotide-transformer` Claim: model-evaluation-identity-8c96cba8ea56bc16f2e0 Source artifact SHA-256: `ab16d582de98652526b5cebb120eec969328f9db29dc741826bcd81c397e0672` Hash scope: SHA-256 of retrieved original artifact bytes Format: original_artifact Inspected artifact

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Release 2026-09-29-06401fd5b220 · Record review: source checked

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DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA · Original source · 2412.05430v1

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Stable ID: dart-eval-method-nucleotide-transformer-fine-tuned

areas: dna-genomes
source locator: Table 1, row(Nucleotide Transformer)
missing metadata: checkpoint revision: unreported; parameters: unextracted

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