Instructions to use ElnaggarLab/ankh3-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ElnaggarLab/ankh3-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ElnaggarLab/ankh3-large")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ElnaggarLab/ankh3-large") model = AutoModelForSeq2SeqLM.from_pretrained("ElnaggarLab/ankh3-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -11,7 +11,7 @@ Ankh3 is a protein language model that is jointly optimized on two objectives:
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This is the model of the paper [Ankh3: Multi-Task Pretraining with Sequence Denoising and Completion Enhances Protein Representations](https://huggingface.co/papers/2505.20052).
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Code: https://github.com/
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1. Masked Language Modeling:
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- The idea of this task is to intentionally 'corrupt' an input protein sequence by
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This is the model of the paper [Ankh3: Multi-Task Pretraining with Sequence Denoising and Completion Enhances Protein Representations](https://huggingface.co/papers/2505.20052).
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Code: https://github.com/agemagician/Ankh
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1. Masked Language Modeling:
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- The idea of this task is to intentionally 'corrupt' an input protein sequence by
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