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README.md
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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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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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# m2m3_fine_tuning_ocr_cmbert_io
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## Introduction
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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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## Dataset parameters
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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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## 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/m2m3_fine_tuning_ocr_cmbert_io")
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