polymathic-ai/LORE-examples
LORE Examples A small set of matched multimodal examples from LORE, for the MIMIC model — enough to try inference, embedding, and generation across DNA, RNA, and protein modalities without wiring up your own data. Each example is a single biological entity (a transcript and/or its protein) with several co-observed modalities. Rows are drawn from the held-out (validation) split of MIMIC's training data, so they are in-distribution and length-bounded to the model's context… See the full description on the dataset page: https://huggingface.co/datasets/polymathic-ai/LORE-examples.
<p align="center"> <img src="MIMIC_logo.png" alt="MIMIC" width="320"> </p>
LORE Examples
A small set of matched multimodal examples from LORE, for the MIMIC model — enough to try inference, embedding, and generation across DNA, RNA, and protein modalities without wiring up your own data.
Each example is a single biological entity (a transcript and/or its protein) with several co-observed modalities. Rows are drawn from the held-out (validation) split of MIMIC's training data, so they are in-distribution and length-bounded to the model's context window.
Contents
Two parallel views of the same rows, exposed as two dataset configs:
raw is the default config, so load_dataset("polymathic-ai/LORE-examples") (no config name) loads it. Every row carries kind (rna / protein / both) plus uniprot_id / genome_feature_id anchors. Both views cover the same rows and the same modalities; tokenized just skips running the tokenizers (and the one-time ESM3 weight download for prot_struct).
Usage
from datasets import load_dataset
from mimic import load_pretrained
model = load_pretrained(version="1.0")
ANCHORS = ("kind", "uniprot_id", "genome_feature_id")
def to_sample(row):
# drop anchor columns and modalities absent from this row (stored as null)
return {k: v for k, v in row.items() if k not in ANCHORS and v is not None}
# raw view — modality name -> raw value; tokenizers run inside input()
raw = load_dataset("polymathic-ai/LORE-examples", "raw", split="train")
model.input([to_sample(raw[0])])
reps = model.embed() # {"full": [B, N, D], "mod_ids": [B, N]}
# tokenized view — tok_ keys with pre-tokenized ids
# (skips running the BioBERT/ESM3 tokenizers; same rows as the raw view)
tok = load_dataset("polymathic-ai/LORE-examples", "tokenized", split="train")
model.input([to_sample(tok[0])])Modalities
MIMIC represents each molecule as a set of co-observed modalities grouped into three tracks: nucleic (RNA/DNA and its per-position annotations), protein (amino-acid sequence, structure, and derived features), and text (free-text / categorical context). Each row here populates a subset of these under its short name (raw view) or its tok_ key (tokenized view). The authoritative per-checkpoint list is model.modality_info. The Dtype column is the raw/decoded Python type; the tokenized config stores every modality as list[int]. A few assay tracks (atac, cage, rasp2, prot_abund) are context-conditional: pass a free-text context alongside them to condition on cell-state / assay metadata — the Conditioning context column shows a real example for each.
See also
- Model: `polymathic-ai/MIMIC` — the model these examples are for.
- Code: `PolymathicAI/MIMIC` (
pip install git+https://github.com/PolymathicAI/MIMIC.git).
Provenance & license
Derived from the validation split of MIMIC's training corpus (LORE). Released under the MIT license, matching the model code. See the MIMIC repository for details and citation.