Model type
Single-cell transformer; this record is the paper-specific evaluated configuration.
Configuration
Geneformer
This single-cell foundation-model configuration is a comparator in the scELMo study.
Sources
scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis · Results/scELMo for clustering and batch effect correction. (paragraph 1); Methods/Data pre-processing. (paragraph 1)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
scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis · Methods/scELMo under the fine-tuning framework. (paragraph 1); Methods/Data pre-processing./Metrics. (paragraph 12)At a glance
Biological inputs
Single-cell gene-expression measurements
Sources
scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis · Methods/Data pre-processing./Metrics. (paragraph 15); Methods/Data pre-processing./Metrics. (paragraph 12)Outputs
Cell embeddings or adapted task predictions
Sources
scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis · Methods/Problem definition. (paragraph 4); Results/scELMo for clustering and batch effect correction. (paragraph 1)limited source coverage · Automated source review, 2026-09-16. All specifications and missing details
Evaluations and results
Release 2026-09-17-d277315f7d76 · 1 evaluation · 1 metric row. Different protocols are not a single leaderboard.
| Metric and finding | Coverage and uncertainty | Evidence |
|---|---|---|
| Geneformer: Cell-type annotation Zero-shot setting; source caption says some comparator rows come from GenePT. Result quoted from another source · Evaluation metadata: needs review | ||
| 0.270 F1 Unit: unitless · Direction: unknown | Uncertainty: not reported in legacy extract Scored: Not reported · Eligible: Not reported | source checkedscELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis · Table 1, hPancreas zero-shot / Geneformer (z) row, F1 column Source checking is not independent reproduction. |
How it works
How the evaluated method works
Pretrained cell/gene representations are evaluated on the paper’s clustering, integration, annotation or perturbation tasks, with the associated evaluation retaining the task-specific procedure.
Sources
scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis · Methods/scELMo under the fine-tuning framework. (paragraph 1); Methods/Data pre-processing./Metrics. (paragraph 12)Underlying method and version boundaries
Geneformer ranks genes by expression scaled against its pretraining corpus, then uses a transformer encoder with a masked-gene objective. V1 and V2 have different corpora, vocabularies, sizes and context limits, so a historical paper name is not replaced with today’s default checkpoint.
Sources
huggingface.co/ctheodoris/Geneformer README.md · README.md; introduction, model description, pretrained-model and usage sections at pinned revisionWhat was evaluated
The linked evaluation record identifies Geneformer: Cell-type annotation. Its dataset, split, adaptation and evidence origin remain attached to the reported results.
Sources
scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis · The named evaluation’s methods and comparison table; exact preserved evaluation IDs: evaluation-lit-030Strengths and limitations
Strengths and considerations
- Provides a learned-expression reference for comparison with text-derived biological embeddings.
Sources
scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis · Methods/scELMo under the fine-tuning framework. (paragraph 4); Discussion (paragraph 2)
Limitations and conditions
- Different downstream tasks and adaptation settings cannot be combined into a single model-quality claim.
Sources
scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis · Introduction (paragraph 3); Results/Overview of scELMo. (paragraph 2)
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-d60f505aabb19cSpecifications
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.
| Property | Description and evidence |
|---|---|
| Model type | Single-cell transformer; this record is the paper-specific evaluated configuration.Sourceshuggingface.co/ctheodoris/Geneformer README.md · README.md model description |
| Architecture / procedure | Pretrained cell/gene representations are evaluated on the paper’s clustering, integration, annotation or perturbation tasks, with the associated evaluation retaining the task-specific procedure.SourcesscELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis · Methods/scELMo under the fine-tuning framework. (paragraph 1); Methods/Data pre-processing./Metrics. (paragraph 12) |
| Biological inputs | Single-cell gene-expression measurementsSourcesscELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis · Methods/Data pre-processing./Metrics. (paragraph 15); Methods/Data pre-processing./Metrics. (paragraph 12) |
| Outputs | Cell embeddings or adapted task predictionsSourcesscELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis · Methods/Problem definition. (paragraph 4); Results/scELMo for clustering and batch effect correction. (paragraph 1) |
| Parameters | An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sourcesSources (2)scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis; huggingface.co/ctheodoris/Geneformer README.md · Methods/Problem definition.; Methods/Method explanation.; Methods/scELMo under the zero-shot learning framework.; Methods/scELMo under the fine-tuning framework.; Methods/Data pre-processing.; Methods/Data pre-processing./Metrics.; Methods/Explanations of baseline models.; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision |
| Known versions / configuration | Geneformer is the comparison-table label; that label does not specify an immutable weight revision. · Not reported in inspected sourcesSourcesscELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis · Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label. |
| Training data / fitting | The comparison includes frozen and fine-tuned embedding settings followed by k-nearest-neighbour classification. Table 1 notes that some results were taken from GenePT; the exact origin and adaptation must therefore remain attached to each row.SourcesscELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis · Methods / Explanations of baseline models; Table 1 caption |
| Context limits | A maximum input/context length for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sourcesSources (2)scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis; huggingface.co/ctheodoris/Geneformer README.md · Methods/Problem definition.; Methods/Method explanation.; Methods/scELMo under the zero-shot learning framework.; Methods/scELMo under the fine-tuning framework.; Methods/Data pre-processing.; Methods/Data pre-processing./Metrics.; Methods/Explanations of baseline models.; inspected for explicit maximum input length (dataset lengths and family-wide limits are not substituted); README.md at pinned repository revision |
| Access | Official upstream implementation and usage documentation: https://huggingface.co/ctheodoris/Geneformer/blob/1f7fbae4e469a5f4f1af8c111a529cfe1b3829f5/README.md. This pinned documentation revision is not automatically the evaluated weight revision.Sourceshuggingface.co/ctheodoris/Geneformer 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 sourcesSourceshuggingface.co/ctheodoris/Geneformer README.md · README.md and repository-root licence-file search |
| Weights licence | Apache 2.0 is declared in the official Geneformer model-card metadata; the exact historical configuration still needs its checkpoint identity.Sourceshuggingface.co/ctheodoris/Geneformer README.md · README.md front matter, license field; model-version list |
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.
22 evidence rows matching the loaded filters
| 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 | scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis Methods/scELMo under the fine-tuning framework. (paragraph 1); Methods/Data pre-processing./Metrics. (paragraph 12) Version: preprint archived 2025-08-23 | source checked automated source review · 2026-09-16 Audit detailsPrimary 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: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram steps ["Single-cell gene-expression measurements","Geneformer","Cell embeddings or adapted task predictions"] Individual claims | scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis Methods/scELMo under the fine-tuning framework. (paragraph 1); Methods/Data pre-processing./Metrics. (paragraph 12) Version: preprint archived 2025-08-23 | source checked automated source review · 2026-09-16 Audit detailsPrimary 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: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram title Evaluated procedure (conceptual) Individual claims | scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis Methods/scELMo under the fine-tuning framework. (paragraph 1); Methods/Data pre-processing./Metrics. (paragraph 12) Version: preprint archived 2025-08-23 | source checked automated source review · 2026-09-16 Audit detailsPrimary 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: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Model type Single-cell transformer; this record is the paper-specific evaluated configuration. Individual claims | huggingface.co/ctheodoris/Geneformer README.md README.md model description Version: 1f7fbae4e469a5f4f1af8c111a529cfe1b3829f5 | source checked automated source review · 2026-09-16 Audit detailsPrimary 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: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Architecture / procedure Pretrained cell/gene representations are evaluated on the paper’s clustering, integration, annotation or perturbation tasks, with the associated evaluation retaining the task-specific procedure. Individual claims | scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis Methods/scELMo under the fine-tuning framework. (paragraph 1); Methods/Data pre-processing./Metrics. (paragraph 12) Version: preprint archived 2025-08-23 | source checked automated source review · 2026-09-16 Audit detailsPrimary 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: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Weights licence Apache 2.0 is declared in the official Geneformer model-card metadata; the exact historical configuration still needs its checkpoint identity. Individual claims | huggingface.co/ctheodoris/Geneformer README.md README.md front matter, license field; model-version list Version: 1f7fbae4e469a5f4f1af8c111a529cfe1b3829f5 | source checked automated source review · 2026-09-16 Audit detailsPrimary 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: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Biological inputs Single-cell gene-expression measurements Individual claims | scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis Methods/Data pre-processing./Metrics. (paragraph 15); Methods/Data pre-processing./Metrics. (paragraph 12) Version: preprint archived 2025-08-23 | source checked automated source review · 2026-09-16 Audit detailsPrimary 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: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Outputs Cell embeddings or adapted task predictions Individual claims | scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis Methods/Problem definition. (paragraph 4); Results/scELMo for clustering and batch effect correction. (paragraph 1) Version: preprint archived 2025-08-23 | source checked automated source review · 2026-09-16 Audit detailsPrimary 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: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Parameters An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. Individual claims | huggingface.co/ctheodoris/Geneformer README.md Methods/Problem definition.; Methods/Method explanation.; Methods/scELMo under the zero-shot learning framework.; Methods/scELMo under the fine-tuning framework.; Methods/Data pre-processing.; Methods/Data pre-processing./Metrics.; Methods/Explanations of baseline models.; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 1f7fbae4e469a5f4f1af8c111a529cfe1b3829f5 | unreported automated source review · 2026-09-16 Audit detailsPrimary 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: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Parameters An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. Individual claims | scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis Methods/Problem definition.; Methods/Method explanation.; Methods/scELMo under the zero-shot learning framework.; Methods/scELMo under the fine-tuning framework.; Methods/Data pre-processing.; Methods/Data pre-processing./Metrics.; Methods/Explanations of baseline models.; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: preprint archived 2025-08-23 | unreported automated source review · 2026-09-16 Audit detailsPrimary 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: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Sources and history
Release 2026-09-17-d277315f7d76 · Record review: needs review
2 source records and release history
- scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis · Original source · preprint archived 2025-08-23
- huggingface.co/ctheodoris/Geneformer README.md · Original source · 1f7fbae4e469a5f4f1af8c111a529cfe1b3829f5
Technical metadata and extraction receipts
Stable ID: reported-model-d60f505aabb19c
- areas
- cells-tissues
- entity level
- method
- version
- Not reported
- reported name
- Geneformer
- historical missing metadata
- version: not_reported_in_legacy_extract; 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: scelmo-2025; evidence-reported-base-geneformer-readme-md; source locator: Methods/scELMo under the fine-tuning framework. (paragraph 1); Methods/Data pre-processing./Metrics. (paragraph 12) | README.md model description | Results/scELMo for clustering and batch effect correction. (paragraph 1); Methods/Data pre-processing. (paragraph 1); 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.