| language: | |
| - fr | |
| multilinguality: | |
| - monolingual | |
| task_categories: | |
| - token-classification | |
| # m2m3_fine_tuning_ref_cmbert_io | |
| ## Introduction | |
| This dataset was used to fine-tuned [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) for **nested NER task** using Independant NER layers approach [M1]. | |
| It contains Paris trade directories entries from the 19th century. | |
| ## Dataset parameters | |
| * Approachrd : M2 and M3 | |
| * Dataset type : ground-truth | |
| * Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) | |
| * Tagging format : IO | |
| * Counts : | |
| * Train : 6084 | |
| * Dev : 676 | |
| * Test : 1685 | |
| * Associated fine-tuned models : | |
| * M2 : [nlpso/m2_joint_label_ref_cmbert_io](https://huggingface.co/nlpso/m2_joint_label_ref_cmbert_io) | |
| * M3 : [nlpso/m3_hierarchical_ner_ref_cmbert_io](https://huggingface.co/nlpso/m3_hierarchical_ner_ref_cmbert_io) | |
| ## Entity types | |
| 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 | |
| ## How to use this dataset | |
| ```python | |
| from datasets import load_dataset | |
| train_dev_test = load_dataset("nlpso/m2m3_fine_tuning_ref_cmbert_io") | |