Image Feature Extraction
Transformers
PyTorch
Joblib
esm
DNA
biology
genomics
segmentation
custom_code
Instructions to use InstaDeepAI/segment_nt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use InstaDeepAI/segment_nt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="InstaDeepAI/segment_nt", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("InstaDeepAI/segment_nt", trust_remote_code=True) model = AutoModel.from_pretrained("InstaDeepAI/segment_nt", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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@@ -96,7 +96,7 @@ probabilities = torch.nn.functional.softmax(logits, dim=-1)
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print(f"Probabilities shape: {probabilities.shape}")
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# Get probabilities associated with intron
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idx_intron = features.index("intron")
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probabilities_intron = probabilities[:,:,idx_intron]
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print(f"Intron probabilities shape: {probabilities_intron.shape}")
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print(f"Probabilities shape: {probabilities.shape}")
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# Get probabilities associated with intron
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idx_intron = model.config.features.index("intron")
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probabilities_intron = probabilities[:,:,idx_intron]
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print(f"Intron probabilities shape: {probabilities_intron.shape}")
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