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genbio-ai
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AIDO.Protein-16B

PyTorch
fm4bio
biology
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@@ -73,8 +73,8 @@ mgen test --model SequenceClassification --model.backbone aido_protein_16b --dat
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  ```python
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  from modelgenerator.tasks import Embed
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  model = Embed.from_config({"model.backbone": "aido_protein_16b"}).eval()
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- collated_batch = model.collate({"sequences": ["HELLQ", "WRLD"]})
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- embedding = model(collated_batch)
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  print(embedding.shape)
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  print(embedding)
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  ```
@@ -83,8 +83,8 @@ print(embedding)
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  import torch
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  from modelgenerator.tasks import SequenceClassification
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  model = SequenceClassification.from_config({"model.backbone": "aido_protein_16b", "model.n_classes": 2}).eval()
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- collated_batch = model.collate({"sequences": ["HELLQ", "WRLD"]})
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- logits = model(collated_batch)
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  print(logits)
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  print(torch.argmax(logits, dim=-1))
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  ```
@@ -93,8 +93,8 @@ print(torch.argmax(logits, dim=-1))
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  import torch
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  from modelgenerator.tasks import TokenClassification
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  model = TokenClassification.from_config({"model.backbone": "aido_protein_16b", "model.n_classes": 3}).eval()
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- collated_batch = model.collate({"sequences": ["HELLQ", "WRLD"]})
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- logits = model(collated_batch)
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  print(logits)
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  print(torch.argmax(logits, dim=-1))
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  ```
@@ -102,8 +102,8 @@ print(torch.argmax(logits, dim=-1))
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  ```python
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  from modelgenerator.tasks import SequenceRegression
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  model = SequenceRegression.from_config({"model.backbone": "aido_protein_16b"}).eval()
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- collated_batch = model.collate({"sequences": ["HELLQ", "WRLD"]})
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- logits = model(collated_batch)
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  print(logits)
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  ```
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  ```python
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  from modelgenerator.tasks import Embed
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  model = Embed.from_config({"model.backbone": "aido_protein_16b"}).eval()
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+ transformed_batch = model.transform({"sequences": ["HELLQ", "WRLD"]})
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+ embedding = model(transformed_batch)
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  print(embedding.shape)
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  print(embedding)
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  ```
 
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  import torch
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  from modelgenerator.tasks import SequenceClassification
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  model = SequenceClassification.from_config({"model.backbone": "aido_protein_16b", "model.n_classes": 2}).eval()
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+ transformed_batch = model.transform({"sequences": ["HELLQ", "WRLD"]})
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+ logits = model(transformed_batch)
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  print(logits)
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  print(torch.argmax(logits, dim=-1))
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  ```
 
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  import torch
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  from modelgenerator.tasks import TokenClassification
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  model = TokenClassification.from_config({"model.backbone": "aido_protein_16b", "model.n_classes": 3}).eval()
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+ transformed_batch = model.transform({"sequences": ["HELLQ", "WRLD"]})
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+ logits = model(transformed_batch)
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  print(logits)
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  print(torch.argmax(logits, dim=-1))
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  ```
 
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  ```python
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  from modelgenerator.tasks import SequenceRegression
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  model = SequenceRegression.from_config({"model.backbone": "aido_protein_16b"}).eval()
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+ transformed_batch = model.transform({"sequences": ["HELLQ", "WRLD"]})
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+ logits = model(transformed_batch)
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  print(logits)
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  ```
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