Datasets:

BIIE-AI
/

protein-model-registry

alias large_stringlengths 4 22	registry_id large_stringlengths 7 30	notes large_stringclasses 9 values
esm2 650m	esm2_t33_650m	null
esm2-650m	esm2_t33_650m	null
facebook esm2 650m	esm2_t33_650m	null
esm2 default	esm2_t33_650m	Canonical default ESM-2
esm2 3b	esm2_t36_3b	null
esm2-3b	esm2_t36_3b	null
facebook esm2 3b	esm2_t36_3b	null
esm2 15b	esm2_t48_15b	null
esm2 8m	esm2_t6_8m	null
esm2 35m	esm2_t12_35m	null
esm2 150m	esm2_t30_150m	null
esm1v	esm1v_650m	Defaults to model 1 of 5-ensemble
esm-1v	esm1v_650m	null
esm1v 650m	esm1v_650m	null
esm cambrian	esmc_300m	null
esmc	esmc_300m	Defaults to 300M variant
esmc 300m	esmc_300m	null
esm3	esm3_sm_open	Defaults to open 1.4B
esm3 small open	esm3_sm_open	null
esm3 1.4b	esm3_sm_open	null
evolutionaryscale esm3	esm3_sm_open	null
esmfold	esmfold_v1	null
esm fold	esmfold_v1	null
esm inverse folding	esm_if1	null
esm-if1	esm_if1	null
esm if1	esm_if1	null
boltzgen	boltzgen_1	null
boltz 2	boltz_2	null
boltz2	boltz_2	null
la proteina	nv_la_proteina	null
nvidia la proteina	nv_la_proteina	null
nv la proteina	nv_la_proteina	null
dplm 150m	dplm_150m	null
dplm 650m	dplm_650m	null
dplm 3b	dplm_3b	null
dplm2	dplm2_150m	Defaults to 150M
dplm2 150m	dplm2_150m	null
prot t5	prot_t5_xl_enc	Defaults to encoder-only for embedding use
prot t5 xl	prot_t5_xl	null
prot bert	prot_bert	null
protbert	prot_bert	null
ankh3	ankh3_large	Defaults to large
ankh3 large	ankh3_large	null
ankh3 xl	ankh3_xl	null
saprot	saprot_650m	null
saprot 650m	saprot_650m	null
tranception	tranception	null
protgpt2	protgpt2	null
prot gpt2	protgpt2	null
nucleotide transformer	nucleotide_transformer_v2_500m	Defaults to v2 500M multi-species
nt v2 500m	nucleotide_transformer_v2_500m	null
biomedbert	biomedbert_base	null
pubmedbert	biomedbert_base	Same model, different common name
biobert	biobert_base	null

BIIE-AI Protein Model Registry

A curated, structured registry of protein language models (PLMs), diffusion generators, structure predictors, inverse folding models, nucleotide language models, and biomedical NLP models.

Canonical local source: registry_src/models.jsonl (one record per line, edit here) Published format: Parquet (generated by scripts/build_parquet.py) Schema: schema/model_registry.schema.json Selection policy: registry_src/selection_policy.yaml

Dataset configs

Config	Description
`models`	One row per model — all registry fields
`aliases`	Natural-language aliases → `registry_id` mappings
`benchmarks`	Task/metric/value evaluation results (populated over time)

Key fields

Field	Description
`registry_id`	Unique snake_case identifier
`hf_repo_id`	HuggingFace Hub `org/model` path
`category`	Functional type (see taxonomy.md)
`primary_tasks`	Main use cases
`compute_tier`	GPU requirements
`commercial_use`	Licensing assessment
`selection_tags`	Tags used by the model-selection skill for routing

Workflow

# Edit registry
vim registry_src/models.jsonl

# Validate
python scripts/validate_registry.py

# Build Parquet
python scripts/build_parquet.py

# Sync live HF metadata (downloads, likes, gating)
python scripts/refresh_hf_metadata.py

# Publish to this repo
HF_TOKEN=hf_... python scripts/publish_to_hub.py

Taxonomy reference

See docs/taxonomy.md for all enum definitions (category, architecture_type, compute_tier, commercial_use, etc.).

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