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README.md

@@ -1,5 +1,37 @@
 ---
+license:
+- cc-by-4.0
+- etalab-2.0
+language:
+- fr
+tags:
+- medical
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: finetuning/train-*
+- config_name: finetuning
+  data_files:
+  - split: train
+    path: finetuning/*.parquet
+- config_name: instruction-tuning
+  data_files:
+  - split: train
+    path: instruction-tuning/*.parquet
 dataset_info:
+- config_name: finetuning
+  features:
+  - name: input
+    dtype: string
+  - name: source
+    dtype: string
+  - name: document_type
+    dtype: string
+  splits:
+  - name: train
+    num_examples: 891196
+- config_name: instruction-tuning
   features:
   - name: input
     dtype: string
@@ -13,13 +45,179 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 23768340
     num_examples: 22390
-  download_size: 7446529
-  dataset_size: 23768340
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: instruction-tuning/train-*
 ---
+# PARCOMED - PARTAGES Corpus of Open MEdical Documents
+
+This document describes the first version of the **commercial** corpus. 
+
+## Overview
+
+The availability of French biomedical data remains a major challenge for improving the multilingual capabilities of large language models (LLMs) in the medical domain.
+We introduce and release the PARCOMED corpus, a collection of French biomedical texts compiled from a wide range of sources for commercial use.
+
+While similar datasets have been released in the past couple of years (NACHOS from DrBERT, JARGON), ours is the result of a greater scrutiny of the licensing terms of each source. Therefore, the PARTAGES corpus is fully compatible with research usage and is also distributed with a version compatible with commercial usage.
+Here, we present the commercial corpus released.
+
+
+## Document types and data sources
+
+The selected datasets for our corpus come from a variety of sources which can be categorized as follows: 
+
+### Clinical
+**FRASIMED**: Annotated corpus of synthetic clinical cases written in French. Available at https://zenodo.org/records/8355629. License CC-BY-4.0.
+### Dialogue
+**PXCORPUS**: French corpus of medical dialogues on prescriptions, transcripted and annotated. Available at https://doi.org/10.5281/zenodo.6482586. License CC-BY-4.0.
+### Education
+**CERIMES**: Indexing of digital pedagogical resources proposed by higher education institutions and research organizations in France. NACHOS versioning. Available at https://data.enseignementsup-recherche.gouv.fr/explore/dataset/fr_esr_ressources-pedagogiques/export/?flg=en-gb&refine.lom_lifecycle_contribute_entity_fn=CERIMES. License Etalab.
+### Encyclopedic
+**WIKIPEDIA**: Corpus extracted from Wikipedia in French, collected via the python wikipediaapi on medical, pharmaceutical and biological categories. License CC-BY-SA 3.0, GNU Free Documentation License.
+### Medical
+**ECDC_TM**: Corpus of medical texts from the European Centre for Disease Prevention and Control (ECDC) for machine translation tasks. NACHOS versioning. Available at https://joint-research-centre.ec.europa.eu/language-technology-resources/ecdc-translation-memory_en#Introduction. Free License.
+### Medicinal
+**EMEA_V3**: Corpus of multilingual medical documents from the European Medicines Agency (EMEA), 3rd version. NACHOS versioning. Available at https://huggingface.co/datasets/qanastek/EMEA-V3. License CC-BY-4.0.
+
+**BDPM**: Public database of medicines. NACHOS versioning. Available at https://www.data.gouv.fr/fr/datasets/base-de-donnees-publique-des-medicaments-base-officielle/. License Etalab.
+### Question Answering
+**DEFT2021**: Corpus from the DEFT challenge for three tasks: extraction of clinical profiles, evaluation of student responses and existing ratings. Available at https://huggingface.co/datasets/DrBenchmark/DEFT2021. License CC-BY-4.0.
+
+**FRENCHMEDMCQA** (INSTRUCT): Francophone corpus of questions in the medical domain with 5 response options (single or multiple choice) and their manual corrections. Available at https://huggingface.co/datasets/qanastek/frenchmedmcqa. License Apache 2.0.
+
+**MEDIQAL** (INSTRUCT): MediQAl is a French medical question answering dataset designed to evaluate the capabilities of language models in factual medical recall and clinical reasoning. Disponible à https://huggingface.co/datasets/ANR-MALADES/MediQAl. Licence CC-BY-4.0
+### Regulation
+**QUALISCOPE**: Data on the quality of healthcare establishments in France, extracted from Scope Santé. NACHOS versioning. Available at https://www.data.gouv.fr/fr/datasets/base-sur-la-qualite-et-la-securite-des-soins-anciennement-scope-sante/. License Etalab.
+
+**CNEDIMTS**: Dataset from a specialized commission of the HAS that evaluates individual medical devices as well as diagnostic, therapeutic or assistive products (excluding medications), as well as associated services. NACHOS versioning. Available at https://www.data.gouv.fr/datasets/evaluation-des-dispositifs-medicaux/. License Etalab.
+### Scientific
+**WMT16**: Biomedical variant of the WMT16 corpus built from PubMed scientific publications, containing multilingual data used for machine translation. Available at https://huggingface.co/datasets/qanastek/WMT-16-PubMed. License CC-BY-4.0.
+
+**HAL**: Corpus extracted from the HAL platform, grouping French scientific publications in the biomedical domain. NACHOS versioning. Available via harvesting following the api protocol https://api.documentation-administrative.gouv.fr/oai. License Etalab.
+
+**HAS**: Data from the High Authority of Health. NACHOS versioning. Available at https://www.data.gouv.fr/fr/datasets/textes-des-publications-de-la-has-7/. License Etalab.
+
+**QUAERO**: Corpus of multilingual medical documents from MEDLINE titles and documents from the European Medicines Agency (EMEA-V3), used for training and evaluating models of automatic medical language processing. NACHOS versioning. Available at https://huggingface.co/datasets/DrBenchmark/QUAERO. License GNU Free Documentation License.
+
+**ISTEX**: Corpus of scientific publications from the ISTEX platform, gathering French scientific literature. NACHOS versioning. Available at https://data.istex.fr/. License Etalab.
+
+**MANTRA_GSC**: Dataset extracted from biomedical corpora (Medline abstract titles, pharmaceutical notices, biomedical patents), with independent concept annotation according to a subset of the UMLS. NACHOS versioning. Available at https://huggingface.co/datasets/bigbio/mantra_gsc. License CC-BY-4.0.
+
+
+## Preprocessing steps 
+
+### Text cleaning
+
+All the documents were preprocessed using a pipeline inspired by FlauBERT (Le et al., 2020), including Unicode conversion and normalization, removal of characters outside standard French encoding, removal of multiple spaces, and removal of URLs.
+
+To this initial cleaning script, additional steps were added due to the lack of relevant content in some documents included in the corpus. These were based on criteria such as a minimum word count (=5; a higher number would have been too restrictive for dialogues) in the texts that were retained.
+
+### De-duplication
+
+To avoid overfitting on redundant samples in our dataset, we added an additional deduplication step during preprocessing. We used a very “classic” method based on MinHash similarity, with a similarity threshold of 0.85 and a number of permitted permutations set to 128.
+
+This deduplication was applied during the transfer of the sourced datasets to the ready-to-use, unsourced corpus. Indeed, since some corpora intersect, the granularity of the source becomes less relevant because the documents are compared in an inter-corpus manner.
+
+
+## Features Scheme
+
+| Column Name   | Data Type   | Description                                                                                                                                                |
+|:--------------|:------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------|
+| instruction   | string      | instruction-tuning only feature, corresponding to the system prompt for instruction-tuning samples.                                                        |
+| input         | string      | input text, regardless of the adaptation method (e.g., finetuning or instruction-tuning). For instruction-tuning, this is the "user prompt" or "question". |
+| output        | string      | **instruction-tuning only feature** gold standard output for supervised instruction-tuning.                                                                |
+| source        | string      | dataset name of the data sample.                                                                                                                           |
+| document_type | string      | typology of document (e.g., Scientific, Encyclopedic, Clinical, Medication, Question-Answering, Dialogue, Regulation).                                     |
+
+## Statistics 
+
+### Document-type granularity
+
+**FINETUNING** data 
+
+|                    |   nb_docs |         nb_words |   mean_words |   std_words |         nb_chars |   mean_chars |   std_chars |
+|:-------------------|----------:|-----------------:|-------------:|------------:|-----------------:|-------------:|------------:|
+| Total              |    891196 |      8.83648e+08 |     991.53   |   6768.64   |      5.50441e+09 |    6176.42   |  41398.6    |
+| Scientific         |    640257 |      8.49351e+08 |    1326.58   |   7931.16   |      5.27612e+09 |    8240.63   |  48468.1    |
+| Medicinal          |    233960 |      2.44849e+07 |     104.654  |    647.2    |      1.63167e+08 |     697.415  |   4332.35   |
+| Wiki               |      9957 |      6.53102e+06 |     655.923  |   1252.04   |      4.32721e+07 |    4345.89   |   8209.94   |
+| Education          |        22 |      1.71519e+06 |   77963.1    |  47413.5    |      1.16235e+07 |  528341      | 321525      |
+| Clinical           |      2048 |      1.3229e+06  |     645.946  |    333.903  |      8.73342e+06 |    4264.37   |   2207.73   |
+| Question Answering |       275 | 111792           |     406.516  |    264.436  | 626549           |    2278.36   |   1402.57   |
+| Regulation         |      1111 |  70081           |      63.0792 |     54.7356 | 478447           |     430.645  |    365.089  |
+| Medical            |      2152 |  42460           |      19.7305 |     13.3516 | 280626           |     130.402  |     92.0109 |
+| Dialogue           |      1414 |  18372           |      12.9929 |      6.0802 | 103531           |      73.2185 |     33.7791 |
+
+**INSTRUCTION-TUNING** data 
+
+|                    |   nb_docs |    nb_words |   mean_words |   std_words |    nb_chars |   mean_chars |   std_chars |
+|:-------------------|----------:|------------:|-------------:|------------:|------------:|-------------:|------------:|
+| Question Answering |     22390 | 1.78385e+06 |      79.6716 |     59.3966 | 1.17989e+07 |      526.971 |     372.088 |
+| Total              |     22390 | 1.78385e+06 |      79.6716 |     59.3966 | 1.17989e+07 |      526.971 |     372.088 |
+
+### Source-wise granularity
+
+**FINETUNING** data 
+
+|            |   nb_docs |         nb_words |   mean_words |   std_words |         nb_chars |   mean_chars |   std_chars |
+|:-----------|----------:|-----------------:|-------------:|------------:|-----------------:|-------------:|------------:|
+| Total      |    891196 |      8.83648e+08 |     991.53   |  6768.64    |      5.50441e+09 |    6176.42   |  41398.6    |
+| HAL        |     26987 |      7.03474e+08 |   26067.1    | 26603.8     |      4.32567e+09 |  160287      | 160053      |
+| HAS        |     11334 |      9.61734e+07 |    8485.39   | 16098.9     |      6.20009e+08 |   54703.4    | 102858      |
+| ISTEX      |     12179 |      4.31384e+07 |    3542.03   |  2156.57    |      2.82624e+08 |   23205.9    |  14238.5    |
+| BDPM       |     11023 |      2.00358e+07 |    1817.63   |  2409.58    |      1.35081e+08 |   12254.5    |  16062.4    |
+| WIKIPEDIA  |      9957 |      6.53102e+06 |     655.923  |  1252.04    |      4.32721e+07 |    4345.89   |   8209.94   |
+| WMT16      |    587562 |      6.49552e+06 |      11.055  |     5.40784 |      4.73973e+07 |      80.6677 |     37.5055 |
+| EMEA_V3    |    222937 |      4.44909e+06 |      19.9567 |    15.5252  |      2.80864e+07 |     125.984  |     99.953  |
+| CERIMES    |        22 |      1.71519e+06 |   77963.1    | 47413.5     |      1.16235e+07 |  528341      | 321525      |
+| FRASIMED   |      2048 |      1.3229e+06  |     645.946  |   333.903   |      8.73342e+06 |    4264.37   |   2207.73   |
+| DEFT2021   |       275 | 111792           |     406.516  |   264.436   | 626549           |    2278.36   |   1402.57   |
+| QUAERO     |      2083 |  66877           |      32.1061 |   161.208   | 394933           |     189.598  |    905.512  |
+| CNEDIMTS   |       813 |  58345           |      71.7651 |    60.599   | 398478           |     490.133  |    403.23   |
+| ECDC_TM    |      2152 |  42460           |      19.7305 |    13.3516  | 280626           |     130.402  |     92.0109 |
+| PXCORPUS   |      1414 |  18372           |      12.9929 |     6.0802  | 103531           |      73.2185 |     33.7791 |
+| QUALISCOPE |       298 |  11736           |      39.3826 |    19.5879  |  79969           |     268.352  |    131.707  |
+| MANTRA_GSC |       112 |   3085           |      27.5446 |    39.6518  |  22356           |     199.607  |    306.097  |
+
+**INSTRUCTION-TUNING** data 
+
+|               |   nb_docs |         nb_words |   mean_words |   std_words |         nb_chars |   mean_chars |   std_chars |
+|:--------------|----------:|-----------------:|-------------:|------------:|-----------------:|-------------:|------------:|
+| Total         |     22390 |      1.78385e+06 |      79.6716 |     59.3966 |      1.17989e+07 |      526.971 |     372.088 |
+| MEDIQAL       |     19907 |      1.6593e+06  |      83.3526 |     61.6255 |      1.09334e+07 |      549.225 |     386.325 |
+| FRENCHMEDMCQA |      2483 | 124547           |      50.1599 |     19.6412 | 865475           |      348.56  |     126.799 |
+
+## File Organization
+
+PARTAGES/
+
+├── fine-tuning/
+
+│   ├── dataset1_part1.parquet
+
+│   ├── dataset1_part2.parquet
+
+│   └── ...
+
+├── instruction-tuning/
+
+│   ├── dataset2_part1.parquet
+
+│   ├── dataset2_part2.parquet
+
+│   └── ...
+
+└── README.md
+
+
+## Usage
+
+```python
+from dataset import load_dataset
+
+data = load_dataset(
+	"LIMICS/PARTAGES", 
+	split="train", 
+	data_dir="finetuning"  # or "instruction-tuning"
+	download_mode="force_redownload", 
+	verification_mode="no_checks", 
+)
+```