AIDO.RNA 1.6B
AIDO.RNA is a 1.6B parameter RNA foundation model trained on 42 million non-coding RNA sequences at single-nucleotide resolution. It achieves state-of-the-art performance on a comprehensive set of tasks, including RNA secondary structure prediction, mRNA-related tasks, RNA function prediction tasks, and RNA inverse folding.
Model architectural details
AIDO.RNA is an encoder-only transformer and is pre-trained using masked language modeling (MLM) objective. The model architecture parameters are as follows:
| hyperparameter | value |
|---|---|
| num-layers | 32 |
| hidden-size | 2,048 |
| ffn-hidden-size | 5,440 |
| num-attn-heads | 32 |
Pre-training data
The pre-training data contains 42 million unique ncRNA sequences from RNAcentral version 24.0.
Downstream evaluation
How to Use
Build any downstream models from this backbone
Get RNA sequence embedding
from genbio_finetune.tasks import Embed
model = Embed.from_config({"model.backbone": "rnafm"}).eval()
collated_batch = model.collate({"sequences": ["ACGT", "ACGT"]})
embedding = model(collated_batch)
print(embedding.shape)
print(embedding)
Sequence-level classification
import torch
from genbio_finetune.tasks import SequenceClassification
model = SequenceClassification.from_config({"model.backbone": "rnafm", "model.n_classes": 2}).eval()
collated_batch = model.collate({"sequences": ["ACGT", "AGCT"]})
logits = model(collated_batch)
print(logits)
print(torch.argmax(logits, dim=-1))
Token-level classification
import torch
from genbio_finetune.tasks import TokenClassification
model = TokenClassification.from_config({"model.backbone": "rnafm", "model.n_classes": 3}).eval()
collated_batch = model.collate({"sequences": ["ACGT", "AGCT"]})
logits = model(collated_batch)
print(logits)
print(torch.argmax(logits, dim=-1))
Pairwise token-level classification
@Sazan TODO
Sequence-level regression
from genbio_finetune.tasks import SequenceRegression
model = SequenceRegression.from_config({"model.backbone": "rnafm"}).eval()
collated_batch = model.collate({"sequences": ["ACGT", "AGCT"]})
logits = model(collated_batch)
print(logits)
Or use our one-liner CLI to finetune or evaluate any of the above!
gbft fit --model SequenceClassification --model.backbone rnafm --data SequenceClassification --data.path <hf_or_local_path_to_your_dataset>
gbft test --model SequenceClassification --model.backbone rnafm --data SequenceClassification --data.path <hf_or_local_path_to_your_dataset>
For more information, visit: Model Generator
Citation
Please cite AIDO.RNA using the following BibTeX code:
License
@Hongyi TODO