Update README.md

README.md

@@ -1,6 +1,6 @@
-# AIDO.RNA 1.6B
+# AIDO.RNA-1.6B
 
-AIDO.RNA is a general-purpose RNA foundation model with 1.6 billion parameters, 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, and RNA inverse folding. After domain adaptation, AIDO.RNA excels in modeling protein-level tasks, highlighting its potential to leverage the central dogma for enhancing biomolecular representations.
+AIDO.RNA-1.6B is a general-purpose RNA foundation model with 1.6 billion parameters, 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, and RNA inverse folding. After domain adaptation, AIDO.RNA excels in modeling protein-level tasks, highlighting its potential to leverage the central dogma for enhancing biomolecular representations.
 
 <p align="center">
 <img src="https://cdn-uploads.huggingface.co/production/uploads/63008d4bc1e149ceaff724a3/mNqn5SKQFHxSby3E2dosE.png" alt="description" style="width:80%; height:auto;">
@@ -35,18 +35,27 @@ Build any downstream models from this backbone
 ### Get RNA sequence embedding
 ```python
 from genbio_finetune.tasks import Embed
-model = Embed.from_config({"model.backbone": "rnafm"}).eval()
+model = Embed.from_config({"model.backbone": "aido_rna_1b600m"}).eval()
 collated_batch = model.collate({"sequences": ["ACGT", "ACGT"]})
 embedding = model(collated_batch)
 print(embedding.shape)
 print(embedding)
 ```
 
+### Sequence-level regression
+```python
+from genbio_finetune.tasks import SequenceRegression
+model = SequenceRegression.from_config({"model.backbone": "aido_rna_1b600m"}).eval()
+collated_batch = model.collate({"sequences": ["ACGT", "AGCT"]})
+logits = model(collated_batch)
+print(logits)
+```
+
 ### Sequence-level classification
 ```python
 import torch
 from genbio_finetune.tasks import SequenceClassification
-model = SequenceClassification.from_config({"model.backbone": "rnafm", "model.n_classes": 2}).eval()
+model = SequenceClassification.from_config({"model.backbone": "aido_rna_1b600m", "model.n_classes": 2}).eval()
 collated_batch = model.collate({"sequences": ["ACGT", "AGCT"]})
 logits = model(collated_batch)
 print(logits)
@@ -57,37 +66,28 @@ print(torch.argmax(logits, dim=-1))
 ```python
 import torch
 from genbio_finetune.tasks import TokenClassification
-model = TokenClassification.from_config({"model.backbone": "rnafm", "model.n_classes": 3}).eval()
+model = TokenClassification.from_config({"model.backbone": "aido_rna_1b600m", "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
-```python
-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)
-```
-
 ## RNA inverse folding
-@Sazan TODO
+@Sazan 
+
 
 Or use our one-liner CLI to finetune or evaluate any of the above!
 ```bash
-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>
+mgen fit --model SequenceClassification --model.backbone aido_rna_1b600m --data SequenceClassification --data.path <hf_or_local_path_to_your_dataset>
+mgen test --model SequenceClassification --model.backbone aido_rna_1b600m --data SequenceClassification --data.path <hf_or_local_path_to_your_dataset>
 ```
 
-For more information, visit: [Model Generator](https://github.com/genbio-ai/modelgenerator)
+For more information, visit: [ModelGenerator](https://github.com/genbio-ai/modelgenerator)
 
 ## Citation
 Please cite AIDO.RNA using the following BibTeX code: