Instructions to use HuggingFaceBio/Carbon-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuggingFaceBio/Carbon-3B with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("HuggingFaceBio/Carbon-3B", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -161,11 +161,11 @@ bp_probs_list, actual_probs_list = model.score_sequence(sequences)
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# Compute metrics for each sequence
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for i, (seq, actual_probs) in enumerate(zip(sequences, actual_probs_list)):
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log_likelihood = actual_probs.log().
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perplexity = torch.exp(-actual_probs.log().mean()).item() # Perplexity
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print(f"Sequence {i+1} (length {len(seq)}):")
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print(f"
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print(f" Perplexity: {perplexity:.4f}")
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print(f" Mean probability: {actual_probs.mean().item():.4f}")
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```
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# Compute metrics for each sequence
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for i, (seq, actual_probs) in enumerate(zip(sequences, actual_probs_list)):
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log_likelihood = actual_probs.log().mean().item() # Total log-likelihood
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perplexity = torch.exp(-actual_probs.log().mean()).item() # Perplexity
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print(f"Sequence {i+1} (length {len(seq)}):")
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print(f" Mean log-likelihood: {log_likelihood:.2f}")
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print(f" Perplexity: {perplexity:.4f}")
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print(f" Mean probability: {actual_probs.mean().item():.4f}")
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```
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