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Update README.md
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README.md
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@@ -19,8 +19,8 @@ mgen test --model SequenceClassification --model.backbone aido_protein_16b_v1 --
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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_v1"}).eval()
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embedding = model(
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print(embedding.shape)
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print(embedding)
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```
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@@ -29,8 +29,8 @@ 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_v1", "model.n_classes": 2}).eval()
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logits = model(
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print(logits)
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print(torch.argmax(logits, dim=-1))
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```
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@@ -39,8 +39,8 @@ 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_v1", "model.n_classes": 3}).eval()
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logits = model(
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print(logits)
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print(torch.argmax(logits, dim=-1))
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```
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@@ -48,8 +48,8 @@ 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_v1"}).eval()
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logits = model(
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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_v1"}).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_v1", "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_v1", "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_v1"}).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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