Update README.md

README.md

@@ -1,49 +1,62 @@
 ## AIDO.RNA-650M
 
-AIDO.RNA-650M is an RNA foundation model trained on 42 million non-coding RNA sequences at single-nucleotide resolution.
+AIDO.RNA-650M is an RNA foundation model trained on 42 million non-coding RNA sequences at single-nucleotide resolution. 
+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
+### 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_650m --data SequenceClassificationDataModule --data.path <hf_or_local_path_to_your_dataset>
+mgen test --model SequenceClassification --model.backbone aido_rna_650m --data SequenceClassificationDataModule --data.path <hf_or_local_path_to_your_dataset>
+```
+
+### Or use directly in Python
 #### Embedding
 ```python
-from genbio_finetune.tasks import Embed
-model = Embed.from_config({"model.backbone": "rnafm_650m"}).eval()
+from modelgenerator.tasks import Embed
+model = Embed.from_config({"model.backbone": "aido_rna_650m"}).eval()
 collated_batch = model.collate({"sequences": ["ACGT", "AGCT"]})
 embedding = model(collated_batch)
 print(embedding.shape)
 print(embedding)
 ```
-#### Sequence Level Classification
+#### Sequence-level Classification
 ```python
 import torch
-from genbio_finetune.tasks import SequenceClassification
-model = SequenceClassification.from_config({"model.backbone": "rnafm_650m", "model.n_classes": 2}).eval()
+from modelgenerator.tasks import SequenceClassification
+model = SequenceClassification.from_config({"model.backbone": "aido_rna_650m", "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
+#### Token-level Classification
 ```python
 import torch
-from genbio_finetune.tasks import TokenClassification
-model = TokenClassification.from_config({"model.backbone": "rnafm_650m", "model.n_classes": 3}).eval()
+from modelgenerator.tasks import TokenClassification
+model = TokenClassification.from_config({"model.backbone": "aido_rna_650m", "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
+#### Sequence-level Regression
 ```python
-from genbio_finetune.tasks import SequenceRegression
-model = SequenceRegression.from_config({"model.backbone": "rnafm_650m"}).eval()
+from modelgenerator.tasks import SequenceRegression
+model = SequenceRegression.from_config({"model.backbone": "aido_rna_650m"}).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_650m --data SequenceClassification --data.path <hf_or_local_path_to_your_dataset>
-gbft test --model SequenceClassification --model.backbone rnafm_650m --data SequenceClassification --data.path <hf_or_local_path_to_your_dataset>
-```
-For more information, visit: [Model Generator](https://github.com/genbio-ai/modelgenerator)
+
+### Get RNA sequence embedding
+```python
+from genbio_finetune.tasks import Embed
+model = Embed.from_config({"model.backbone": "aido_rna_650m"}).eval()
+collated_batch = model.collate({"sequences": ["ACGT", "ACGT"]})
+embedding = model(collated_batch)
+print(embedding.shape)
+print(embedding)
+```