Feature Extraction
Transformers
PyTorch
Safetensors
English
bert
token-classification
text-embeddings-inference
Instructions to use bioscan-ml/BarcodeBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bioscan-ml/BarcodeBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="bioscan-ml/BarcodeBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("bioscan-ml/BarcodeBERT") model = AutoModelForTokenClassification.from_pretrained("bioscan-ml/BarcodeBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "added_tokens_decoder": { | |
| "0": { | |
| "content": "[MASK]", | |
| "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 | |
| } | |
| }, | |
| "auto_map": { | |
| "AutoTokenizer": [ | |
| "tokenizer.KmerTokenizer", | |
| null | |
| ] | |
| }, | |
| "clean_up_tokenization_spaces": true, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 1000000000000.0, | |
| "tokenizer_class": "KmerTokenizer", | |
| "unk_token": "[UNK]" | |
| } |