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README.md

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+---
+language: fr
+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'
+---
+
+# m3_hierarchical_ner_ref_ptrn_cmbert_iob2
+
+## Introduction
+
+This model is a fine-tuned verion from [Jean-Baptiste/camembert-ner](https://huggingface.co/nlpso/Jean-Baptiste/camembert-ner) 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 : [Jean-Baptiste/camembert-ner](https://huggingface.co/nlpso/Jean-Baptiste/camembert-ner)
+* Dataset : ground-truth
+* Tagging format : IOB2
+* Recognised entities : 'All'
+
+## Load model from the Hugging Face
+
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+
+tokenizer = AutoTokenizer.from_pretrained("m3_hierarchical_ner_ref_ptrn_cmbert_iob2")
+model = AutoModelForTokenClassification.from_pretrained("m3_hierarchical_ner_ref_ptrn_cmbert_iob2")
+
+