Create README.md

README.md

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+# Build any downstream models from this backbone
+## Embedding
+```python
+from genbio_finetune.tasks import Embed
+
+model = Embed.from_config({"model.backbone": "dna300m"})
+
+collated_batch = model.collate({"sequences": ["ACGT", "ACGT"]})
+embedding = model(collated_batch)
+print(embedding.shape)
+print(embedding)
+```
+## Sequence Level Classification
+```python
+import torch
+from genbio_finetune.tasks import SequenceClassification
+
+model = SequenceClassification.from_config({"model.backbone": "dna300m", "model.n_classes": 2})
+
+collated_batch = model.collate({"sequences": ["ACGT", "ACGT"]})
+logits = model(collated_batch)
+print(logits)
+print(torch.argmax(logits, dim=-1))
+```
+## Token Level Classification
+```python
+import torch
+from genbio_finetune.tasks import TokenClassification
+
+model = TokenClassification.from_config({"model.backbone": "dna300m", "model.n_classes": 3})
+
+collated_batch = model.collate({"sequences": ["ACGT", "ACGT"]})
+logits = model(collated_batch)
+print(logits)
+print(torch.argmax(logits, dim=-1))
+```
+## Regression
+```python
+from genbio_finetune.tasks import SequenceRegression
+
+model = SequenceRegression.from_config({"model.backbone": "dna300m"})
+
+collated_batch = model.collate({"sequences": ["ACGT", "ACGT"]})
+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 dna300m --data SequenceClassification --data.path <hf_or_local_path_to_your_dataset>
+gbft test --model SequenceClassification --model.backbone dna300m --data SequenceClassification --data.path <hf_or_local_path_to_your_dataset>
+```
+For more information, visit: [Model Generator](https://github.com/genbio-ai/test)