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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
- name: instruction
dtype: string
- name: output
dtype: string
- name: source
dtype: string
- name: document_type
dtype: string
splits:
- name: train
num_examples: 22390
---
# 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",
)
```
|