Feature Extraction
Transformers
PyTorch
Safetensors
English
modernbert
genomics
nucleotide
dna
sequence-modeling
biology
bioinformatics
electra
Instructions to use FreakingPotato/NucEL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FreakingPotato/NucEL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="FreakingPotato/NucEL")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("FreakingPotato/NucEL") model = AutoModel.from_pretrained("FreakingPotato/NucEL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload config.json with huggingface_hub
Browse files- config.json +1 -7
config.json
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"max_position_embeddings": 8192,
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"mlp_bias": false,
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"mlp_dropout": 0.0,
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"model_type": "
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"norm_bias": false,
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"norm_eps": 1e-12,
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"num_attention_heads": 16,
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"max_sequence_length": 512,
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"embedding_dimension": 512
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}
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},
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"custom_tokenizer": true,
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"tokenizer_class": "NucEL_Tokenizer",
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"auto_map": {
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"AutoModel": "modeling_nucel.NucELModel",
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"AutoTokenizer": "model.tokenizer.NucEL_Tokenizer"
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}
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}
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"max_position_embeddings": 8192,
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"mlp_bias": false,
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"mlp_dropout": 0.0,
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"model_type": "modernbert",
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"norm_bias": false,
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"norm_eps": 1e-12,
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"num_attention_heads": 16,
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"max_sequence_length": 512,
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"embedding_dimension": 512
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}
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}
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}
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