Update README.md

README.md

@@ -7,68 +7,73 @@ base_model:
 AIDO.RNA-1.6B-CDS is a domain adaptation model on the coding sequences. It was pre-trained on 9 million coding sequences released by Carlos et al. (2024) [1] based on our [AIDO.RNA-1.6B](https://huggingface.co/genbio-ai/AIDO.RNA-1.6B) model.
 
 ## How to Use
-### Build any downstream models from this backbone
-#### Get RNA sequence embedding
+### 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_1b600m_cds --data SequenceClassificationDataModule --data.path <hf_or_local_path_to_your_dataset>
+mgen test --model SequenceClassification --model.backbone aido_rna_1b600m_cds --data SequenceClassificationDataModule --data.path <hf_or_local_path_to_your_dataset>
+```
+
+### Or use directly in Python
+#### Embedding
 ```python
-from genbio_finetune.tasks import Embed
+from modelgenerator.tasks import Embed
 model = Embed.from_config({"model.backbone": "aido_rna_1b600m_cds"}).eval()
 collated_batch = model.collate({"sequences": ["ACGT", "AGCT"]})
 embedding = model(collated_batch)
 print(embedding.shape)
 print(embedding)
 ```
-
-#### Sequence-level regression
+#### Sequence-level Classification
 ```python
-from genbio_finetune.tasks import SequenceRegression
-model = SequenceRegression.from_config({"model.backbone": "aido_rna_1b600m_cds"}).eval()
+import torch
+from modelgenerator.tasks import SequenceClassification
+model = SequenceClassification.from_config({"model.backbone": "aido_rna_1b600m_cds", "model.n_classes": 2}).eval()
 collated_batch = model.collate({"sequences": ["ACGT", "AGCT"]})
 logits = model(collated_batch)
 print(logits)
+print(torch.argmax(logits, dim=-1))
 ```
-
-#### Sequence-level classification
+#### Token-level Classification
 ```python
 import torch
-from genbio_finetune.tasks import SequenceClassification
-model = SequenceClassification.from_config({"model.backbone": "aido_rna_1b600m_cds", "model.n_classes": 2}).eval()
+from modelgenerator.tasks import TokenClassification
+model = TokenClassification.from_config({"model.backbone": "aido_rna_1b600m_cds", "model.n_classes": 3}).eval()
 collated_batch = model.collate({"sequences": ["ACGT", "AGCT"]})
 logits = model(collated_batch)
 print(logits)
 print(torch.argmax(logits, dim=-1))
 ```
-
-#### Token-level classification
+#### Sequence-level Regression
 ```python
-import torch
-from genbio_finetune.tasks import TokenClassification
-model = TokenClassification.from_config({"model.backbone": "aido_rna_1b600m_cds", "model.n_classes": 3}).eval()
+from modelgenerator.tasks import SequenceRegression
+model = SequenceRegression.from_config({"model.backbone": "aido_rna_1b600m_cds"}).eval()
 collated_batch = model.collate({"sequences": ["ACGT", "AGCT"]})
 logits = model(collated_batch)
 print(logits)
-print(torch.argmax(logits, dim=-1))
 ```
 
-#### Or use our one-liner CLI to finetune or evaluate any of the above!
-```
-mgen fit --model SequenceClassification --model.backbone aido_rna_1b600m_cds --data SequenceClassification --data.path <hf_or_local_path_to_your_dataset>
-mgen test --model SequenceClassification --model.backbone aido_rna_1b600m_cds --data SequenceClassification --data.path <hf_or_local_path_to_your_dataset>
+### Get RNA sequence embedding
+```python
+from genbio_finetune.tasks import Embed
+model = Embed.from_config({"model.backbone": "aido_rna_1b600m_cds"}).eval()
+collated_batch = model.collate({"sequences": ["ACGT", "ACGT"]})
+embedding = model(collated_batch)
+print(embedding.shape)
+print(embedding)
 ```
-For more information, visit: [ModelGenerator](https://github.com/genbio-ai/modelgenerator)
-
 
 ## Citation
 Please cite AIDO.RNA using the following BibTeX code:
 ```
-@inproceedings{
-zou2024a,
-title={A Large-Scale Foundation Model for {RNA} Function and Structure Prediction},
-author={Shuxian Zou and Tianhua Tao and Sazan Mahbub and Caleb Ellington and Robin Jonathan Algayres and Dian Li and Yonghao Zhuang and Hongyi Wang and Le Song and Eric P. Xing},
-booktitle={NeurIPS 2024 Workshop on AI for New Drug Modalities},
-year={2024},
-url={https://openreview.net/forum?id=Gzo3JMPY8w}
+@misc{zou_large-scale_2024,
+	title = {A Large-Scale Foundation Model for RNA Function and Structure Prediction},
+	url = {https://www.biorxiv.org/content/10.1101/2024.11.28.625345v1},
+	doi = {10.1101/2024.11.28.625345},
+	publisher = {bioRxiv},
+	author = {Zou, Shuxian and Tao, Tianhua and Mahbub, Sazan and Ellington, Caleb N. and Algayres, Robin and Li, Dian and Zhuang, Yonghao and Wang, Hongyi and Song, Le and Xing, Eric P.},
+	year = {2024},
 }
-```
 
 ## Reference
 1. Carlos Outeiral and Charlotte M Deane. Codon language embeddings provide strong signals for use in protein engineering. Nature Machine Intelligence, 6(2):170–179, 2024.