Super-squash branch 'main' using huggingface_hub

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+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
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+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
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+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+pretty_name: FineMed-fr
+language:
+  - fr
+license:
+  - odc-by
+  - cc-by-sa-4.0
+task_categories:
+  - fill-mask
+  - text-generation
+size_categories:
+  - 10M<n<100M
+tags:
+  - medical
+  - healthcare
+  - biomedical
+  - clinical
+  - french
+  - pretraining
+  - data-filtering
+  - fineweb-2
+  - finepdfs
+  - finewiki
+configs:
+  - config_name: fineweb-2
+    default: true
+    data_files:
+      - split: train
+        path: data/fineweb-2/*.parquet
+  - config_name: finepdfs
+    data_files:
+      - split: train
+        path: data/finepdfs/*.parquet
+  - config_name: finewiki
+    data_files:
+      - split: train
+        path: data/finewiki/*.parquet
+---
+
+# FineMed-fr
+
+<center>
+  <img src="assets/logo_finemed.png" width="100%" alt="FineMed-fr: A large-scale French medical corpus annotated along multiple quality axes">
+</center>
+
+<!-- arXiv ID pending: banner with Technical Report (and the citation below) hidden until the ID is assigned; uncomment these and replace the arXiv ID placeholder. -->
+<!--
+<p align="center">
+<a href="https://huggingface.co/blog/bofenghuang/doctobert-fr-release">🤗 Blog</a> |
+<a href="https://arxiv.org/abs/2606.XXXXX">📄 Technical Report</a> |
+<a href="https://github.com/doctolib-lab/doctobert">💻 Code</a> |
+<a href="https://huggingface.co/collections/doctolib-lab/finemed-fr">🌐 FineMed</a> |
+<a href="https://huggingface.co/collections/doctolib-lab/doctobert-fr">🩺 DoctoBERT</a>
+</p>
+-->
+
+<p align="center">
+<a href="https://huggingface.co/blog/bofenghuang/doctobert-fr-release">🤗 Blog</a> |
+<a href="https://github.com/doctolib-lab/doctobert">💻 Code</a> |
+<a href="https://huggingface.co/collections/doctolib-lab/finemed-fr">🌐 FineMed</a> |
+<a href="https://huggingface.co/collections/doctolib-lab/doctobert-fr">🩺 DoctoBERT</a>
+</p>
+
+## 📚 Introduction
+
+**FineMed-fr** is a large, openly available corpus of French medical text for language-model pretraining: **21.1M documents** and **19.2B words** of real-world medical writing, annotated along several quality axes.
+
+The corpus is drawn from three heterogeneous open-web sources ([FineWeb-2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2),
+[FinePDFs](https://huggingface.co/datasets/HuggingFaceFW/finepdfs), and
+[FineWiki](https://huggingface.co/datasets/HuggingFaceFW/finewiki)), which together provide the scale, source
+diversity, and stylistic range that curated medical corpora often lack. We keep only the French medical
+content, then label every surviving document along three axes:
+
+- **Subdomain**: which of 15 medical subdomains the document belongs to, separating biomedical and
+  clinical writing (e.g. scientific papers, clinical guidelines) from consumer-facing material
+  (e.g. wellness blogs, commercial health pages).
+- **Educational quality**: how instructive the document is for medical education, scored 0–5 on an
+  additive rubric adapted from [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu).
+- **Medical-term density**: the richness of medical terminology, measured as the fraction of
+  characters that fall inside extracted medical-term spans.
+
+We release the corpus unfiltered so you can set your own thresholds on the annotation columns to fit your task.
+
+## 🆕 What's New
+
+- **v1.0** (2026-06): first release.
+
+## 🚀 How to Use
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("doctolib-lab/finemed-fr", split="train")  # fineweb-2 (default)
+ds = load_dataset("doctolib-lab/finemed-fr", "finepdfs", split="train")
+ds = load_dataset("doctolib-lab/finemed-fr", "finewiki", split="train")
+```
+
+Because the corpus is released unfiltered, downstream filtering is left to the user. For example, to retain only high-quality, term-dense documents:
+
+```python
+filtered = ds.filter(
+    lambda x: x["edu_quality_normalized_score"] >= 4 and x["medical_entity_density"] >= 0.10,
+    num_proc=8,
+)
+```
+
+## 🔧 Curation Pipeline
+
+The three source corpora have already undergone standard LLM-pretraining curation upstream (language ID,
+heuristic quality filtering, deduplication), which we inherit as a quality baseline. Beyond this baseline,
+we apply two further steps:
+
+1. **Medical prefiltering.** Medical content constitutes only a small fraction of each source and is
+   further diluted by commercial pages. We run a [multilingual domain classifier](https://huggingface.co/nvidia/multilingual-domain-classifier)
+   (a DeBERTa-v3 covering 26 domains) over the first 512 tokens of each document and retain only those
+   whose top-1 predicted label is `Health`, reducing each source to 5.3% of FineWeb-2, 7.7% of FinePDFs,
+   and 1.5% of FineWiki (by document).
+2. **Multi-axis annotation.** Every retained document is then labeled by three dedicated lightweight
+   annotators, each fine-tuned via two-stage knowledge distillation from LLM teachers (a smaller
+   teacher providing high-volume supervision, followed by a larger teacher providing high-quality
+   supervision):
+   - A [subdomain classifier](https://huggingface.co/doctolib-lab/finemed-subdomain-classifier-fr) takes the document text and URL as input and predicts one of 15 medical subdomains;
+   - An [educational-quality scorer](https://huggingface.co/doctolib-lab/finemed-edu-scorer-fr) takes the document text and regresses a 0–5 educational-quality score;
+   - A [medical-entity extractor](https://huggingface.co/doctolib-lab/finemed-entity-extractor-fr) identifies medical-term spans, whose character coverage defines the medical-term density.
+
+   Distilling each annotator from its LLM teachers, rather than applying an LLM directly across the full
+   corpus, reduces annotation cost by roughly an order of magnitude.
+
+**Subdomain.** `health_domain_classification_best_class` is one of these 15 values:
+
+| subdomain | description |
+| --------- | ----------- |
+| Clinical cases & vignettes | Single-patient narratives: presentation, evaluation, management, outcomes; case-based teaching. |
+| Clinical guidelines & pathways | Non-patient-specific recommendations, algorithms, and standards; named guidelines or consensus statements. |
+| Patient education & lifestyle | Consumer-facing explanations and how-to advice on prevention, self-care, symptoms, diet, fitness, mental well-being. |
+| Wellness, supplements & CAM | Botanicals, vitamins, supplements, complementary or alternative therapies outside mainstream clinical guidance. |
+| Public health, policy & programs | Population surveillance, epidemiology, screening, laws and regulation, financing and insurance, community guidance. |
+| Commercial & promotional | Marketing or sales content: pricing, booking, calls-to-action, affiliate/SEO, comparative ads, testimonials. |
+| Drugs, trials & regulation | Drug development and evaluation: clinical trials, approvals and labels, PK/PD, safety monitoring, pharmacovigilance. |
+| Biomedical & mechanistic science | Experimental or preclinical research: labs, omics, pathways, cell/animal models, assays, mechanisms. |
+| Medical devices, diagnostics & imaging | Device or modality descriptions and clinical use; diagnostics, wearables, sensors, imaging. |
+| Health IT, telemedicine & operations | EHR/EMR, data standards, interoperability, analytics, telemedicine, workflow, staffing, procurement, logistics. |
+| Occupational health & safety | Workplace hazards, exposures, PPE, training, and compliance with occupational regulations. |
+| Health workforce education & training | Professional curricula, CME, certification, simulation, residency/fellowship information. |
+| Health services & facilities | Neutral descriptions of care-delivery models, service lines, facility capabilities, long-term/residential care. |
+| Other health | Health-related content that is unclear or insufficient to classify under the other subdomains. |
+| Others | Not clearly health-related, too brief, or lacking detail (e.g. navigation/boilerplate). |
+
+**Medical-term classes.** `medical_entities` groups the extracted terms under these 8 keys (taxonomy adapted from UMLS):
+
+| class | covers |
+| ----- | ------ |
+| `disease` | disease, syndrome, infection, cancer, injury, symptom, clinical finding, mental disorder |
+| `drug` | prescription medication, vaccine, therapeutic compound, drug class, contrast agent |
+| `body_part` | organ, tissue, bone, muscle, blood vessel, nerve, cell, body fluid, anatomical region |
+| `medical_procedure` | surgery, diagnostic test, medical examination, laboratory test, imaging procedure |
+| `molecular_marker` | gene, protein, enzyme, receptor, genetic variant, biochemical analyte |
+| `clinical_device` | surgical tool, implant, prosthetic, diagnostic scanner, monitoring equipment |
+| `vital_function` | heart rate, blood pressure, respiratory rate, temperature, oxygen saturation |
+| `living_beings` | bacterium, virus, fungus, parasite, pathogen, model organism |
+
+**Educational quality.** `edu_quality_normalized_score` runs from 0 (not useful) to 5 (excellent) for
+medical education; `edu_quality_score` is the raw value before rounding. The exact rubric used to prompt
+the LLM annotators is in [`edu_quality_annotation_prompt.txt`](https://huggingface.co/datasets/doctolib-lab/finemed-fr/blob/main/assets/edu_quality_annotation_prompt.txt).
+
+## 📊 Dataset Statistics
+
+Each source is provided as a separate config. Per-source statistics:
+
+| config       | source               | documents  | words   | median words/doc |
+| ------------ | -------------------- | ---------: | ------: | ---------------: |
+| `fineweb-2`  | FineWeb-2 (fra_Latn) | 18,888,234 | 12.03 B |              346 |
+| `finepdfs`   | FinePDFs (fra_Latn)  |  2,137,275 |  7.16 B |              766 |
+| `finewiki`   | FineWiki (frwiki)    |     38,620 | 26.55 M |              283 |
+| **total**    |                      | 21,064,129 | 19.21 B |              369 |
+
+<!-- Average annotation values per source:
+
+| config      | mean edu score | mean density |
+| ----------- | -------------: | -----------: |
+| `fineweb-2` |           2.01 |        0.080 |
+| `finepdfs`  |           2.76 |        0.069 |
+| `finewiki`  |           3.12 |        0.140 |
+| **overall** |           2.09 |        0.079 | -->
+
+Annotation values vary substantially across subdomains. Distribution of educational-quality scores across the 15 subdomains:
+
+![Educational quality by subdomain](assets/all_edu_quality_by_subtopic_count.png)
+
+Distribution of medical-term density across the same subdomains:
+
+![Medical-term density by subdomain](assets/all_medical_entity_density_by_domain.png)
+
+Per-source versions of both plots are available in [assets/](https://huggingface.co/datasets/doctolib-lab/finemed-fr/tree/main/assets).
+
+<!-- Domain composition of each source (% of documents; **Health** is the retained fraction):
+
+| domain | FineWeb-2 | FinePDFs | FineWiki |
+| ------ | --------: | -------: | -------: |
+| Arts & Entertainment       |  9.7% |  6.5% | 18.6% |
+| Home & Garden              |  6.5% |  2.4% |  0.2% |
+| News                       |  6.1% |  4.3% |  5.8% |
+| People & Society           |  5.9% | 10.1% | 18.1% |
+| Food & Drink               |  5.6% |  5.0% |  1.7% |
+| Sports                     |  5.5% |  4.8% | 13.0% |
+| **Health** (retained)      |  **5.2%** |  **7.8%** |  **1.5%** |
+| Travel & Transportation    |  5.2% |  3.0% | 11.4% |
+| Business & Industrial      |  5.0% |  5.7% |  0.9% |
+| Jobs & Education           |  4.1% | 11.7% |  0.8% |
+| Law & Government           |  2.3% | 16.5% |  3.4% |
+| Science                    |  1.1% |  4.0% |  7.3% |
+| Others                     | 43.8% | 18.2% | 17.3% | -->
+
+## 📋 Data Fields
+
+All configs share these columns:
+
+| column | type | description |
+| ------ | ---- | ----------- |
+| `text` | string | document text |
+| `id` | string | source document id (matches the id in the source dataset) |
+| `url` | string | source URL |
+| `num_words` | int64 | whitespace word count |
+| `domain_classification_best_class` / `_best_score` / `_scores` | string / double / list | prefilter domain classifier output (the medical subset is `Health`) |
+| `health_domain_classification_best_class` / `_best_score` / `_scores` | string / double / list | 15-class medical-subdomain classifier output |
+| `edu_quality_score` / `edu_quality_normalized_score` | double / int64 | educational-quality scorer (FineWeb-Edu rubric adapted to medicine); raw score and its 0–5 rounded form |
+| `medical_entities` | struct | extracted medical terms grouped into 8 classes; each class is a deduplicated list of surface strings |
+| `medical_entity_density` | float | fraction of characters covered by those terms, measured over the document's middle 512-token window (or the whole document when it is shorter than 512 tokens) |
+
+Source-specific provenance columns:
+
+- `fineweb-2`: `dump`, `date`, `file_path`, `language`, `language_score`, `language_script`, `minhash_cluster_size`, `top_langs`
+- `finepdfs`: `dump`, `date`, `file_path`, `offset`, `token_count`, `language`, `page_average_lid`, `page_average_lid_score`, `full_doc_lid`, `full_doc_lid_score`, `per_page_languages`, `is_truncated`, `extractor`, `page_ends`
+- `finewiki`: `wikiname`, `page_id`, `title`, `date_modified`, `in_language`, `wikidata_id`, `bytes_html`, `wikitext`, `version`, `infoboxes`, `has_math`
+
+**Example record.** A full `fineweb-2` row (provenance columns differ for the other configs):
+
+```json
+{
+  "text": "Attention L'actualité thérapeutique sur le VIH évolue rapidement ... Pneumopathie bactérienne chez les patients infectés par le VIH ...",
+  "id": "<urn:uuid:2cc73ad5-d0ae-483c-8b59-78147734bcb8>",
+  "dump": "CC-MAIN-2014-10",
+  "url": "http://www.actions-traitements.org/spip.php?article1961",
+  "date": "2014-03-10T06:59:32Z",
+  "file_path": "s3://commoncrawl/crawl-data/CC-MAIN-2014-10/segments/.../CC-MAIN-...-00091-....warc.gz",
+  "language": "fra",
+  "language_score": 0.9974,
+  "language_script": "Latn",
+  "minhash_cluster_size": 7,
+  "top_langs": "{}",
+  "domain_classification_scores": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+  "domain_classification_best_class": "Health",
+  "domain_classification_best_score": 1.0,
+  "num_words": 600,
+  "health_domain_classification_scores": [0.0092, 0.0349, 0.832, 0.0, 0.0267, 0.0, 0.0001, 0.0001, 0.0001, 0.0, 0.0001, 0.0, 0.0043, 0.0933, 0.0],
+  "health_domain_classification_best_class": "Clinical guidelines & pathways",
+  "health_domain_classification_best_score": 0.832,
+  "edu_quality_score": 4.75,
+  "edu_quality_normalized_score": 5,
+  "medical_entities": {
+    "disease": ["pneumonie", "méningites", "fièvre", "sida", "..."],
+    "drug": ["traitement antirétroviral"],
+    "body_part": [], "medical_procedure": [], "molecular_marker": [],
+    "clinical_device": [], "vital_function": [],
+    "living_beings": ["Streptococcus pneumoniae", "Klebsiella pneumoniae", "VIH", "..."]
+  },
+  "medical_entity_density": 0.242
+}
+```
+
+*(a real FineWeb-2 row; `text`, `file_path`, and entity lists trimmed for display)*
+
+<!--
+## 📖 Citation
+
+```bibtex
+@misc{doctobert2026,
+  title         = {Where Does the Signal Live? A Web Data Recipe for Medical Encoder Pretraining},
+  author        = {Huang, Bofeng and Sun, Jacques and Bouchacourt, Diane and Barascud, Nicolas and Fogel, Fajwel},
+  year          = {2026},
+  eprint        = {2606.XXXXX},
+  archivePrefix = {arXiv},
+  primaryClass  = {cs.CL}
+}
+```
+-->
+
+## ⚖️ Licensing
+
+FineMed-fr inherits the licenses of its source datasets:
+
+- `fineweb-2` and `finepdfs`: ODC-BY 1.0 (as in the upstream FineWeb releases)
+- `finewiki`: CC BY-SA 4.0 (derived from Wikipedia)
+
+## ⚠️ Considerations
+
+FineMed-fr consists of public text from the web, PDFs, and Wikipedia, restricted to medical content.
+As real-world web data, it may contain personal information, and medical pages may reference protected
+health information. All such content was already publicly accessible, and we did not remove or mask it.
+The corpus has not been clinically validated and does not constitute medical advice. Users handling
+personal or health data should perform de-identification before use.
+
+## 🏛️ Acknowledgments
+
+This work was granted access to the HPC resources of IDRIS (Jean Zay) under the allocations 2025-AD011016291 and 2026-A0200617487 made by GENCI.

assets/edu_quality_annotation_prompt.txt

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+Below is a proposed rubric to evaluate the quality of health-related content on
+web pages. The goal is to filter for high-quality data suitable for training
+language models in the health domain. Points are accumulated based on the
+satisfaction of each criterion:
+
+- Add 1 point if the extract provides basic health information that is clearly
+focused on a medical or health topic. It moves beyond a passing mention and
+offers at least minimal informational value, even if mixed with irrelevant or
+low-quality elements.
+
+- Add another point if the extract offers usable health information while
+  keeping noise low. Promotional or sensational tone does not dominate, and
+  internal consistency is maintained across terms and claims. This tier reflects
+  baseline rigor without assessing external truth.
+
+- Award a third point for specificity, density, and domain precision. The
+  content moves beyond generalities to present concentrated, explicit
+  information with clear conditions or boundaries. Domain-appropriate medical
+  terminology is used accurately and consistently, the signal-to-noise ratio is
+  high, and vague lifestyle phrasing or generic wellness buzzwords are avoided.
+  Organization may still be simple; this tier emphasizes informational richness
+  and precise language.
+
+- Grant a fourth point if the extract is coherent and well-structured.
+  Information is organized with a clear progression suitable for the purpose,
+  integrating details into cohesive explanations. Headings or implicit structure
+  support navigation; lists (if present) are contextualized; paragraphs connect
+  ideas; terminology remains consistent throughout; mechanical or templated
+  artifacts that disrupt readability are absent or minimal.
+
+- Bestow a fifth point for expert synthesis or actionable guidance. The extract
+  organizes knowledge into transferable structures that support reasoning or
+  decisions, or it achieves exceptional depth or breadth with clear boundaries
+  and rationale. Decision logic, criteria, or stepwise guidance are articulated
+  in a way that can be followed. Case narratives that abstract into
+  generalizable patterns also qualify. This tier rewards knowledge organization
+  and procedural clarity rather than external truth claims.
+
+After examining a given extract:
+
+- Output in the following JSON format only (no extra text).
+- Briefly justify your total score, up to 100 words.
+- Conclude with an overall quality score as an integer from 0 to 5.
+
+{
+  "reasoning": "<Your justification, under 100 words>",
+  "score": "<0-5 integer>"
+}

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