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--- |
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language: fr |
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datasets: |
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- nlpso/m2m3_fine_tuning_ref_ptrn_cmbert_iob2 |
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tag: token-classification |
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widget: |
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- text: 'Duflot, loueur de carrosses, r. de Paradis-
505
Poissonnière, 22.' |
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example_title: 'Noisy entry #1' |
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- text: 'Duſour el Besnard, march, de bois à bruler,
quai de la Tournelle, 17. etr. des Fossés-
SBernard. 11.
Dí' |
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example_title: 'Noisy entry #2' |
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- text: 'Dufour (Charles), épicier, r. St-Denis
☞
332' |
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example_title: 'Ground-truth entry #1' |
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--- |
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# m3_hierarchical_ner_ref_ptrn_cmbert_iob2 |
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## Introduction |
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This model is a fine-tuned verion from [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) for **nested NER task** on a nested NER Paris trade directories dataset. |
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## Dataset |
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Abbreviation|Entity group (level)|Description |
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-|-|- |
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O |1 & 2|Outside of a named entity |
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PER |1|Person or company name |
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ACT |1 & 2|Person or company professional activity |
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TITREH |2|Military or civil distinction |
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DESC |1|Entry full description |
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TITREP |2|Professionnal reward |
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SPAT |1|Address |
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LOC |2|Street name |
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CARDINAL |2|Street number |
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FT |2|Geographical feature |
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## Experiment parameter |
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* Pretrained-model : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) |
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* Dataset : ground-truth |
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* Tagging format : IOB2 |
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* Recognised entities : 'All' |
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## Load model from the Hugging Face |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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tokenizer = AutoTokenizer.from_pretrained("nlpso/m3_hierarchical_ner_ref_ptrn_cmbert_iob2") |
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model = AutoModelForTokenClassification.from_pretrained("nlpso/m3_hierarchical_ner_ref_ptrn_cmbert_iob2") |
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