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AIDO.RNA-1.6B

PyTorch
rnabert
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@@ -45,8 +45,8 @@ mgen test --model SequenceClassification --model.backbone aido_rna_1b600m --data
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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_rna_1b600m"}).eval()
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- collated_batch = model.collate({"sequences": ["ACGT", "AGCT"]})
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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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  ```
@@ -55,8 +55,8 @@ 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_rna_1b600m", "model.n_classes": 2}).eval()
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- collated_batch = model.collate({"sequences": ["ACGT", "AGCT"]})
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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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  ```
@@ -65,8 +65,8 @@ 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_rna_1b600m", "model.n_classes": 3}).eval()
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- collated_batch = model.collate({"sequences": ["ACGT", "AGCT"]})
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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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  ```
@@ -74,8 +74,8 @@ 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_rna_1b600m"}).eval()
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- collated_batch = model.collate({"sequences": ["ACGT", "AGCT"]})
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- logits = model(collated_batch)
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  print(logits)
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  ```
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@@ -83,8 +83,8 @@ print(logits)
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  ```python
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  from genbio_finetune.tasks import Embed
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  model = Embed.from_config({"model.backbone": "aido_rna_1b600m"}).eval()
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- collated_batch = model.collate({"sequences": ["ACGT", "ACGT"]})
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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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  ```
 
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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_rna_1b600m"}).eval()
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+ transformed_batch = model.transform({"sequences": ["ACGT", "AGCT"]})
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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_rna_1b600m", "model.n_classes": 2}).eval()
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+ transformed_batch = model.transform({"sequences": ["ACGT", "AGCT"]})
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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_rna_1b600m", "model.n_classes": 3}).eval()
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+ transformed_batch = model.transform({"sequences": ["ACGT", "AGCT"]})
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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_rna_1b600m"}).eval()
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+ transformed_batch = model.transform({"sequences": ["ACGT", "AGCT"]})
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+ logits = model(transformed_batch)
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  print(logits)
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  ```
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  ```python
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  from genbio_finetune.tasks import Embed
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  model = Embed.from_config({"model.backbone": "aido_rna_1b600m"}).eval()
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+ transformed_batch = model.transform({"sequences": ["ACGT", "ACGT"]})
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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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  ```