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README.md

@@ -1,29 +1,51 @@
 ---
-dataset_info:
-  features:
-  - name: tokens
-    sequence: string
-  - name: ner_tags
-    sequence: string
-  - name: input_ids
-    sequence: int32
-  - name: attention_mask
-    sequence: int8
-  - name: labels
-    sequence: int64
-  splits:
-  - name: train
-    num_bytes: 4582729
-    num_examples: 6084
-  - name: dev
-    num_bytes: 502257
-    num_examples: 676
-  - name: test
-    num_bytes: 1224626
-    num_examples: 1685
-  download_size: 924671
-  dataset_size: 6309612
+language:
+- fr
+multilinguality:
+- monolingual
+task_categories:
+- token-classification
 ---
-# Dataset Card for "m2m3_qualitative_analysis_ref_ptrn_cmbert_io"
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+# m2m3_qualitative_analysis_ref_ptrn_cmbert_io
+
+## Introduction
+
+This dataset was used to perform **qualitative analysis** of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on **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 : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
+* Tagging format : IO
+* Counts : 
+    * Train : 6084
+    * Dev : 676
+    * Test : 1685
+* Associated fine-tuned models :
+    * M2 : [nlpso/m2_joint_label_ref_ptrn_cmbert_io](https://huggingface.co/nlpso/m2_joint_label_ref_ptrn_cmbert_io)
+    * M3 : [nlpso/m3_hierarchical_ner_ref_ptrn_cmbert_io](https://huggingface.co/nlpso/m3_hierarchical_ner_ref_ptrn_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_qualitative_analysis_ref_ptrn_cmbert_io")