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model-family passport · Review date not recorded

ProtT5

A widely used protein Transformer for transferable sequence embeddings.

1/7Evidence fields documented
60-SECOND EVALUATION VIEW

What should a scientist know before using ProtT5?

UnresolvedEvidence direction is incomplete or not yet resolved
Best suited forRepresentation · Prediction
Evidence supportsPrimary links may be present, but BioAtlas does not claim a review date without a record-level timestamp.
Evidence does not establishUniversal superiority, therapeutic success, clinical utility or regulatory acceptance.
Major limitationPerformance depends on the evaluation dataset and operating conditions.
Current registry recordVersion history not yet curated1 recorded release · Review date not recorded. A newer version is not assumed to be universally better.

What it is

ProtT5 adapts the T5 family to protein sequences and produces embeddings used across protein prediction tasks.

Evidence trail

BioAtlas keeps the path from source to decision visible. A connection records provenance; it does not imply that evidence is sufficient for every context.

Sources1 connectedPrimary resources and normalized claims
Claims0 normalizedNo normalized claim yet
EntityProtT5model-family · Version history not yet curated
ReviewReview date not recordedReview date not claimed
ConclusionContext requiredAdd to an evaluation before operational use

Model passport

Entity typemodel-family
OrganizationRostlab
Model family introducedNot normalized
AccessOpen source
Commercial useAllowed / verify checkpoint terms
DeploymentSelf-hosted
ComputeGPU recommended
Domainsprotein
Biology → representation → computation → evidence

How ProtT5 represents biology

model-familyprotein

Category is navigation. These fields describe the model-specific computational transformation and deliberately override broad category defaults.

1 · Biological inputs
Amino-acid sequence
2 · Input representation
Amino-acid tokens
3 · Internal representation
Contextual protein embeddings
4 · Architecture
Protein Transformer
5 · Learning objective
Self-supervised protein language modelling
6 · Output representation
Dense vectorsScores

Biological scale

proteinresidue

Modalities & tasks

ProteinRepresentationPrediction

Registry, claims and frontier intelligence

Versioned registry

Version history not yet curated

1 version record · release year not yet normalized. Model-family identity remains separate from capability and access changes.

Explore version lineage →
Benchmark claim ledger

0 normalized claims

No task, dataset, split and metric claim has been normalized for this record yet.

Open claim intelligence →

Inputs and outputs

Inputs

Amino-acid sequence

Outputs

Protein embeddingsTask predictions

Scientific and technical profile

Scientific principles

Protein language modellingTransfer learning

Technology

T5 Transformer
Ideas before algorithms

Scientific lineage

Explore all foundations

These are transparent concept matches—not claims that one scientist alone caused this model. Each connection is based on the model’s recorded domain, scientific principles, technical terms or an explicit lineage link.

Computational intelligence

Transformer self-attention

Ashish Vaswani and colleagues

Protein, genome, molecule and single-cell foundation models use attention to learn dependencies across biological sequences and multimodal inputs.

Matched concepts: transformer, language model, sequence

Evaluation evidence

Dataset or evaluationNot yet curated
Task or metricNot yet extracted
Evidence statusNo task-specific benchmark record curated
Open source ↗

BioAtlas has not yet extracted a structured benchmark claim for this record.

Known limitations

  • Performance depends on the evaluation dataset and operating conditions.
  • A structured benchmark claim has not yet been extracted for this record.
  • Outputs require task-specific scientific and experimental validation.

Milestones

Not normalized

A common protein-embedding benchmark baseline.