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
| { | |
| "added_tokens_decoder": { | |
| "0": {"content": "[PAD]", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true}, | |
| "1": {"content": "[UNK]", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true}, | |
| "2": {"content": "[CLS]", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true}, | |
| "3": {"content": "[SEP]", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true}, | |
| "4": {"content": "[MASK]", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true}, | |
| "5": {"content": "[BOS]", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true}, | |
| "6": {"content": "[EOS]", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true} | |
| }, | |
| "auto_map": { | |
| "AutoTokenizer": ["tokenizer.NucEL_Tokenizer", null] | |
| }, | |
| "k": 1, | |
| "bos_token": "[BOS]", | |
| "clean_up_tokenization_spaces": false, | |
| "cls_token": "[CLS]", | |
| "eos_token": "[EOS]", | |
| "extra_special_tokens": {}, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 2048, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "tokenizer_class": "NucEL_Tokenizer", | |
| "unk_token": "[UNK]" | |
| } | |