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--- |
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language: |
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- fr |
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multilinguality: |
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- monolingual |
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task_categories: |
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- token-classification |
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--- |
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# m1_qualitative_analysis_ocr_cmbert_iob2 |
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## Introduction |
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This dataset was used to perform **qualitative analysis** of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on **nested NER task** using Independant NER layers approach [M1]. |
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It contains Paris trade directories entries from the 19th century. |
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## Dataset parameters |
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* Approach : M1 |
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* Dataset type : noisy (Pero OCR) |
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* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) |
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* Tagging format : IOB2 |
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* Counts : |
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* Train : 6084 |
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* Dev : 676 |
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* Test : 1685 |
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* Associated fine-tuned models : |
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* Level-1 : [nlpso/m1_ind_layers_ocr_cmbert_iob2_level_1](https://huggingface.co/nlpso/m1_ind_layers_ocr_cmbert_iob2_level_1) |
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* Level 2 : [nlpso/m1_ind_layers_ocr_cmbert_iob2_level_2](https://huggingface.co/nlpso/m1_ind_layers_ocr_cmbert_iob2_level_2) |
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## Entity types |
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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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## How to use this dataset |
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```python |
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from datasets import load_dataset |
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train_dev_test = load_dataset("nlpso/m1_qualitative_analysis_ocr_cmbert_iob2") |
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