Safetensors
PyTorch
Transformers
custom
peptedgev2
biology
bioinformatics
peptides
protein
antimicrobial-peptide
amp
protein-sequence
sequence-classification
Eval Results (legacy)
Instructions to use devansh0703/PeptEdgeV2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devansh0703/PeptEdgeV2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import PeptEdgeV2 model = PeptEdgeV2.from_pretrained("devansh0703/PeptEdgeV2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Remove misleading text-classification pipeline/task tags; use protein-sequence task
Browse files
README.md
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---
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language:
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- en
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license: mit
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tags:
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- biology
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- amp
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- pytorch
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- transformers
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-
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- sequence-classification
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pipeline_tag: text-classification
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library_name: custom
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base_model: none
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metrics:
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- name: PeptEdgeV2
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results:
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- task:
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type:
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name: Antimicrobial Peptide (AMP) Binary Classification
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dataset:
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type: devansh0703/GenPept-Curated-2025
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---
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license: mit
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tags:
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- biology
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- amp
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- pytorch
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- transformers
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- protein-sequence
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- sequence-classification
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library_name: custom
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base_model: none
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metrics:
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- name: PeptEdgeV2
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results:
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- task:
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type: protein-sequence
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name: Antimicrobial Peptide (AMP) Binary Classification
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dataset:
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type: devansh0703/GenPept-Curated-2025
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