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  ---
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- dataset_info:
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- features:
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- - name: tokens
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- sequence: string
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- - name: ner_tags
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- sequence: string
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- - name: input_ids
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- sequence: int32
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- - name: attention_mask
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- sequence: int8
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- - name: labels
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- sequence: int64
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- splits:
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- - name: train
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- num_bytes: 3511306
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- num_examples: 6084
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- - name: dev
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- num_bytes: 384264
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- num_examples: 676
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- - name: test
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- num_bytes: 941302
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- num_examples: 1685
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- download_size: 898208
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- dataset_size: 4836872
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  ---
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- # Dataset Card for "m2m3_fine_tuning_ocr_cmbert_io"
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- [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+ # m2m3_fine_tuning_ocr_cmbert_io
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+
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+ ## Introduction
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+
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+ 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].
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+ It contains Paris trade directories entries from the 19th century.
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+
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+ ## Dataset parameters
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+
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+ * Approachrd : M2 and M3
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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 : IO
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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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+ * M2 : [nlpso/m2_joint_label_ocr_cmbert_io](https://huggingface.co/nlpso/m2_joint_label_ocr_cmbert_io)
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+ * M3 : [nlpso/m3_hierarchical_ner_ocr_cmbert_io](https://huggingface.co/nlpso/m3_hierarchical_ner_ocr_cmbert_io)
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+
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+ ## Entity types
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+
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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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+
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+ ## How to use this dataset
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+
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+ ```python
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+ from datasets import load_dataset
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+
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+ train_dev_test = load_dataset("nlpso/m2m3_fine_tuning_ocr_cmbert_io")