AIDO.DNA
For a more detailed description, refer to the SOTA model in this collection https://huggingface.co/genbio-ai/dnafm-7b
How to Use
Build any downstream models from this backbone
Embedding
from genbio_finetune.tasks import Embed
model = Embed.from_config({"model.backbone": "dna300m"}).eval()
collated_batch = model.collate({"sequences": ["ACGT", "AGCT"]})
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": "dna300m", "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": "dna300m", "model.n_classes": 3}).eval()
collated_batch = model.collate({"sequences": ["ACGT", "AGCT"]})
logits = model(collated_batch)
print(logits)
print(torch.argmax(logits, dim=-1))
Regression
from genbio_finetune.tasks import SequenceRegression
model = SequenceRegression.from_config({"model.backbone": "dna300m"}).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 dna300m --data SequenceClassification --data.path <hf_or_local_path_to_your_dataset>
gbft test --model SequenceClassification --model.backbone dna300m --data SequenceClassification --data.path <hf_or_local_path_to_your_dataset>
For more information, visit: Model Generator