Fill-Mask
Transformers
Safetensors
prokbert
bioinformatics
genomics
sequence embedding
genomic language models
nucleotide
dna-sequence
promoter-prediction
phage
custom_code
Instructions to use neuralbioinfo/prokbert-mini-long with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use neuralbioinfo/prokbert-mini-long with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="neuralbioinfo/prokbert-mini-long", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("neuralbioinfo/prokbert-mini-long", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Fixing path to tokenizer
Browse files- tokenizer_config.json +1 -1
tokenizer_config.json
CHANGED
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@@ -50,7 +50,7 @@
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"sep_token": "[SEP]",
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"tokenizer_class": "LCATokenizer",
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"auto_map": {
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-
"AutoTokenizer": ["neuralbioinfo/nbrg-transformers--
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},
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"unk_token": "[UNK]",
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"kmer": 6,
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| 50 |
"sep_token": "[SEP]",
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"tokenizer_class": "LCATokenizer",
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"auto_map": {
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+
"AutoTokenizer": ["neuralbioinfo/nbrg-transformers--tokenizer.LCATokenizer", null]
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},
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"unk_token": "[UNK]",
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| 56 |
"kmer": 6,
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