--- language: fr datasets: - nlpso/m1_fine_tuning_ocr_ptrn_cmbert_io tag: token-classification widget: - text: 'Duflot, loueur de carrosses, r. de Paradis-
 505
 Poissonnière, 22.' example_title: 'Noisy entry #1' - text: 'Duſour el Besnard, march, de bois à bruler,
 quai de la Tournelle, 17. etr. des Fossés-
 SBernard. 11.
 Dí' example_title: 'Noisy entry #2' - text: 'Dufour (Charles), épicier, r. St-Denis
 ☞
 332' example_title: 'Ground-truth entry #1' --- # m1_ind_layers_ocr_ptrn_cmbert_io_level_2 ## Introduction This model is a model that was fine-tuned from [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) for **nested NER task** on a nested NER Paris trade directories dataset. ## Dataset Abbreviation|Entity group (level)|Description -|-|- O |1 & 2|Outside of a named entity PER |1|Person or company name ACT |1 & 2|Person or company professional activity TITREH |2|Military or civil distinction DESC |1|Entry full description TITREP |2|Professionnal reward SPAT |1|Address LOC |2|Street name CARDINAL |2|Street number FT |2|Geographical feature ## Experiment parameter * Pretrained-model : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) * Dataset : noisy (Pero OCR) * Tagging format : IO * Recognised entities : level 2 ## Load model from the Hugging Face **Warning 1 ** : this model only recognises level-2 entities of dataset. It has to be used with [m1_ind_layers_ocr_ptrn_cmbert_io_level_1](https://huggingface.co/nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_1) to recognise nested entities level-1. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_2") model = AutoModelForTokenClassification.from_pretrained("nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_2")