Token Classification
GLiNER
PyTorch
ner
named-entity-recognition
zero-shot
pii
privacy
biomedical
multilingual
lfm2.5
bidirectional
sauerkrautlm
vago-solutions
Instructions to use VAGOsolutions/SauerkrautLM-LFM2.5-GLiNER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use VAGOsolutions/SauerkrautLM-LFM2.5-GLiNER with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("VAGOsolutions/SauerkrautLM-LFM2.5-GLiNER") - Notebooks
- Google Colab
- Kaggle
| { | |
| "backend": "tokenizers", | |
| "bos_token": "<|startoftext|>", | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|im_end|>", | |
| "extra_special_tokens": [], | |
| "is_local": true, | |
| "legacy": false, | |
| "local_files_only": false, | |
| "mask_token": "<mask>", | |
| "model_input_names": [ | |
| "input_ids", | |
| "attention_mask" | |
| ], | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "<|pad|>", | |
| "sp_model_kwargs": {}, | |
| "spaces_between_special_tokens": false, | |
| "tokenizer_class": "TokenizersBackend", | |
| "use_default_system_prompt": false, | |
| "use_fast": true | |
| } | |