File size: 2,503 Bytes
2457dc7 214b3f6 a4e8915 214b3f6 9105a16 66685b0 214b3f6 66685b0 214b3f6 2b47b8c 66685b0 214b3f6 66685b0 214b3f6 2b47b8c 66685b0 214b3f6 66685b0 214b3f6 2b47b8c 66685b0 214b3f6 66685b0 214b3f6 2b47b8c 66685b0 214b3f6 2b47b8c 214b3f6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 |
---
license: other
---
# AIDO.RNA-300M
AIDO.RNA-300M-MARS is a 300 million parameter RNA foundation model pre-trained on 886 million RNA sequences from the MARS database.
For a more detailed description, refer to the SOTA model in this collection https://huggingface.co/genbio-ai/AIDO.RNA-1.6B
## How to Use
### Build any downstream models from this backbone with ModelGenerator
For more information, visit: [Model Generator](https://github.com/genbio-ai/modelgenerator)
```bash
mgen fit --model SequenceClassification --model.backbone aido_rna_300m_mars --data SequenceClassificationDataModule --data.path <hf_or_local_path_to_your_dataset>
mgen test --model SequenceClassification --model.backbone aido_rna_300m_mars --data SequenceClassificationDataModule --data.path <hf_or_local_path_to_your_dataset>
```
### Or use directly in Python
#### Embedding
```python
from modelgenerator.tasks import Embed
model = Embed.from_config({"model.backbone": "aido_rna_300m_mars"}).eval()
transformed_batch = model.transform({"sequences": ["ACGT", "AGCT"]})
embedding = model(transformed_batch)
print(embedding.shape)
print(embedding)
```
#### Sequence-level Classification
```python
import torch
from modelgenerator.tasks import SequenceClassification
model = SequenceClassification.from_config({"model.backbone": "aido_rna_300m_mars", "model.n_classes": 2}).eval()
transformed_batch = model.transform({"sequences": ["ACGT", "AGCT"]})
logits = model(transformed_batch)
print(logits)
print(torch.argmax(logits, dim=-1))
```
#### Token-level Classification
```python
import torch
from modelgenerator.tasks import TokenClassification
model = TokenClassification.from_config({"model.backbone": "aido_rna_300m_mars", "model.n_classes": 3}).eval()
transformed_batch = model.transform({"sequences": ["ACGT", "AGCT"]})
logits = model(transformed_batch)
print(logits)
print(torch.argmax(logits, dim=-1))
```
#### Sequence-level Regression
```python
from modelgenerator.tasks import SequenceRegression
model = SequenceRegression.from_config({"model.backbone": "aido_rna_300m_mars"}).eval()
transformed_batch = model.transform({"sequences": ["ACGT", "AGCT"]})
logits = model(transformed_batch)
print(logits)
```
### Get RNA sequence embedding
```python
from genbio_finetune.tasks import Embed
model = Embed.from_config({"model.backbone": "aido_rna_300m_mars"}).eval()
transformed_batch = model.transform({"sequences": ["ACGT", "ACGT"]})
embedding = model(transformed_batch)
print(embedding.shape)
print(embedding)
``` |