Update README.md

README.md

@@ -91,8 +91,8 @@ We encode protein sequence with single amino acid resolution with 44 vocabularie
 For more information, visit: [Model Generator](https://github.com/genbio-ai/modelgenerator)
 
 ```bash
-mgen fit --model SequenceClassification --model.backbone aido_ragprotein_16b --data SequenceClassificationDataModule --data.path <hf_or_local_path_to_your_dataset>
-mgen test --model SequenceClassification --model.backbone aido_ragprotein_16b --data SequenceClassificationDataModule --data.path <hf_or_local_path_to_your_dataset>
+mgen fit --model SequenceClassification --model.backbone aido_protein_rag_16b --data SequenceClassificationDataModule --data.path <hf_or_local_path_to_your_dataset>
+mgen test --model SequenceClassification --model.backbone aido_protein_rag_16b --data SequenceClassificationDataModule --data.path <hf_or_local_path_to_your_dataset>
 ```
 
 ### Or use directly in Python
@@ -102,7 +102,7 @@ mgen test --model SequenceClassification --model.backbone aido_ragprotein_16b --
 ```python
 import torch
 from modelgenerator.tasks import Embed
-model = Embed.from_config({"model.backbone": "aido_ragprotein_16b"}).eval()
+model = Embed.from_config({"model.backbone": "aido_protein_rag_16b"}).eval()
 model.backbone.max_length = 12800
 data = torch.load("examples.pt", 'cpu')[0]
 transformed_batch = model.transform(data)
@@ -117,7 +117,7 @@ print(embedding.shape)
 ```python
 import torch
 from modelgenerator.tasks import SequenceClassification
-model = SequenceClassification.from_config({"model.backbone": "aido_ragprotein_16b", "model.n_classes": 2}).eval()
+model = SequenceClassification.from_config({"model.backbone": "aido_protein_rag_16b", "model.n_classes": 2}).eval()
 model.backbone.max_length = 12800
 data = torch.load("examples.pt", 'cpu')[0]
 transformed_batch = model.transform(data)
@@ -133,7 +133,7 @@ print(torch.argmax(logits, dim=-1))
 ```python
 import torch
 from modelgenerator.tasks import TokenClassification
-model = TokenClassification.from_config({"model.backbone": "aido_ragprotein_16b", "model.n_classes": 3}).eval()
+model = TokenClassification.from_config({"model.backbone": "aido_protein_rag_16b", "model.n_classes": 3}).eval()
 model.backbone.max_length = 12800
 data = torch.load("examples.pt", 'cpu')[0]
 transformed_batch = model.transform(data)
@@ -148,7 +148,7 @@ print(torch.argmax(logits, dim=-1))
 
 ```python
 from modelgenerator.tasks import SequenceRegression
-model = SequenceRegression.from_config({"model.backbone": "aido_ragprotein_16b"}).eval()
+model = SequenceRegression.from_config({"model.backbone": "aido_protein_rag_16b"}).eval()
 model.backbone.max_length = 12800
 data = torch.load("examples.pt", 'cpu')[0]
 transformed_batch = model.transform(data)