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--- |
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language: |
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- en |
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- fr |
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- es |
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- zh |
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- de |
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- it |
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- pt |
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- ko |
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- ru |
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pretty_name: Biomed-Enriched |
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task_categories: |
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- text-classification |
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dataset_info: |
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features: |
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- name: id |
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dtype: string |
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- name: article_id |
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dtype: string |
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- name: path |
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dtype: string |
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- name: text |
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dtype: string |
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- name: language |
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dtype: string |
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- name: section_title |
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dtype: string |
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- name: domain |
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dtype: string |
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- name: document_type |
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dtype: string |
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- name: educational_score |
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dtype: float64 |
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- name: domain_scores |
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sequence: float64 |
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- name: document_type_scores |
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sequence: float64 |
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- name: language_score |
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dtype: float64 |
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- name: authors |
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sequence: string |
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- name: article_url |
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dtype: string |
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- name: license_url |
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dtype: string |
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splits: |
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- name: commercial |
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num_bytes: 126945006652 |
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num_examples: 98595508 |
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- name: noncommercial |
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num_bytes: 19998737015 |
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num_examples: 47449067 |
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download_size: 52228107549 |
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dataset_size: 146943743667 |
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configs: |
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- config_name: default |
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data_files: |
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- split: commercial |
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path: data/commercial-* |
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- split: noncommercial |
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path: data/noncommercial-* |
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--- |
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# Biomed-Enriched: A Biomedical Dataset Enriched with LLMs for Pretraining and Extracting Rare and Hidden Content |
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### Dataset Authors |
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**Rian Touchent, Nathan Godey & Eric de la Clergerie** |
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*Sorbonne Université, INRIA Paris* |
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### Overview |
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Biomed-Enriched is a PubMed-derived dataset created using a two-stage annotation process. Initially, [Llama 3.1 70B Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct) annotated 400K paragraphs for document type, domain, and educational quality. These annotations were then used to fine-tune a [smaller model](https://huggingface.co/almanach/Biomed-Enriched-classifier), which propagated the labels across the entire PubMed Central Open Access corpus. This process yielded 2M clinical case paragraphs, with over 450K high-quality paragraphs licensed for commercial use. This dataset provides a large-scale, openly available alternative to private clinical text. In continual pre-training experiments with OLMo2, curated subsets showed targeted improvements: clinical upsampling boosted MMLU ProfMed scores by ~5%, and educational quality filtering improved MedQA and MedMCQA by ~1%. Combining these methods achieved similar performance compared to standard continual-pretraining with just one-third of the training tokens, highlighting the potential for more efficient biomedical pretraining. |
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The dataset is structured into two primary splits: |
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* **Commercial** |
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* **Non-Commercial** |
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## Dataset Structure |
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### Commercial Split |
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* **text**: Textual content of the paragraphs. |
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* **path**: Precise XML path referencing original paragraph locations. |
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* **license\_url**: URL linking to the license. |
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* **authors**: Comprehensive list of authors per paragraph for proper attribution compliance. |
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### Non-Commercial Split |
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* **path**: Precise XML path referencing original paragraph locations. |
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* **license\_url**: URL linking to the license. |
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* **authors**: Comprehensive list of authors per paragraph for proper attribution compliance. |
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> **Note:** The non-commercial split does not contain text data due to licensing restrictions. However, we provide scripts to populate the `text` field from a local PMC Open Access XML dump. See below for installation and usage instructions. |
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```bash |
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pip install biomed-enriched |
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``` |
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### With Python |
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```python |
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from biomed_enriched import populate |
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DATASET_DIR = "/path/to/biomed-enriched" # input dataset |
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PMC_XML_ROOT = "/path/to/pmc/non-comm/xml" # PMC XML dump |
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OUTPUT_DIR = "/path/to/populated-biomed-enriched" # drop arg to overwrite in-place |
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populate(DATASET_DIR, PMC_XML_ROOT, output_path=OUTPUT_DIR, splits="noncommercial", num_proc=1) |
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``` |
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The call overwrites the dataset in-place, adding a new `text` column as the third column (after `article_id`, `path`). |
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### With CLI |
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```bash |
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biomed-enriched \ |
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--input /path/to/biomed-enriched \ |
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--xml-root /path/to/pmc/non-comm/xml \ |
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--num-proc 8 |
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``` |
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Add `--output DIR` if you prefer writing to a new directory instead of overwriting. |
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## Annotation Process |
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The dataset was created using a two-stage annotation framework: |
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1. **Initial Annotation by Large Language Model**: |
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* Annotated a subset of paragraphs for the following categories: |
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- **Document Type**: Categorizes the structure and purpose of the content. |
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- **Clinical Case**: Detailed report of symptoms, diagnosis, treatment, and follow-up of individual patients. |
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- **Study**: Research paragraph with methods, results, and discussion of experiments or observations. |
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- **Review**: Summary or synthesis of current knowledge on a specific topic. |
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- **Other**: Content not fitting above categories (editorials, commentaries, policy paragraphs). |
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- **Domain**: Identifies the subject area focus. |
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- **Clinical**: Content relating to patient care, clinical trials, case reports, or practice guidelines. |
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- **Biomedical**: Scientific aspects of medicine and biology. |
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- **Other**: Content mentioning biomedical topics but focusing on administrative, policy, or general communications. |
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- **Educational Quality**: Assesses pedagogical value for college-level biomedical learning on a scale from 1 (minimal value) to 5 (exceptional value) inspired by FineWeb-edu. |
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- **Score 1**: Basic information relevant to biomedical topics, may contain irrelevant content. |
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- **Score 2**: Addresses biomedical education elements but with limitations in coherence or depth. |
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- **Score 3**: Appropriate for college-level curricula, introduces key concepts with reasonable coherence. |
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- **Score 4**: Highly relevant educational content with clear writing style, minimal irrelevant information. |
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- **Score 5**: Outstanding educational value, detailed reasoning with profound insights for college-level learning. |
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2. **Annotation Scaling via Model Distillation**: |
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* These annotations were distilled into a XLM-RoBERTa-base model, enabling scalability to the entire PMC dataset. |
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## Annotation Statistics |
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Here is the distribution of educational scores per domain: |
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| Educational Score | Biomedical (n=116 221 134) | Clinical (n=2 182 784) | Other (n=15 213 051) | |
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| :---------------: | :------------------------: | :--------------------: | :------------------: | |
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| 1 | 1.8 % | 6.0 % | 60.1 % | |
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| 2 | 9.4 % | 23.4 % | 29.4 % | |
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| 3 | 10.9 % | 26.6 % | 8.3 % | |
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| 4 | 75.3 % | 44.0 % | 2.1 % | |
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| 5 | 2.6 % | – | – | |
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Here is the distribution of educational scores per document type: |
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| Educational Score | Study (n=100 387 809) | Review (n=6 811 226) | Clinical case (n=2 122 403) | Other (n=24 295 531) | |
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|:-----------------:|:---------------------:|:--------------------:|:---------------------------:|:--------------------:| |
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| 1 | 0.7 % | 0.3 % | 4.0 % | 43.4 % | |
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| 2 | 7.9 % | 1.6 % | 14.4 % | 30.9 % | |
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| 3 | 10.0 % | 5.0 % | 24.6 % | 14.6 % | |
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| 4 | 78.7 % | 86.9 % | 57.0 % | 11.1 % | |
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| 5 | 2.6 % | 6.1 % | 0.0 % | 0.0 % | |
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### Language Distribution |
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| Language | Articles | Paragraphs | Clinical Case Paragraphs | % Clinical Cases | |
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|----------|----------|------------|-------------------------|-----------------| |
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| en | 4,113,275| 131,579,445| 2,113,185 | 1.61 | |
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| es | 4,339 | 181,779 | 1,235 | 0.68 | |
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| zh-cn | 3,649 | 59,719 | 0 | 0.00 | |
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| fr | 3,410 | 173,325 | 2,586 | 1.49 | |
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| de | 2,976 | 248,608 | 51 | 0.02 | |
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| it | 2,708 | 274,819 | 521 | 0.19 | |
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| pt | 934 | 85,242 | 4,540 | 5.33 | |
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| ko | 636 | 25,535 | 0 | 0.00 | |
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| ru | 222 | 10,553 | 0 | 0.00 | |
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| id | 189 | 91,865 | 15 | 0.02 | |
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## Key Applications |
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* Improve efficiency in biomedical pretraining by focusing on high-quality, specific content. |
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* Create new biomedical subsets tailored to specific research needs based on document type and domain. |
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## Evaluation |
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Our evaluation focuses on isolating the effects of data curation rather than pursuing state-of-the-art scores on benchmarks. A more powerful foundation model would likely yield higher absolute scores but would obscure the precise impact of our dataset. We therefore selected `OLMo2-7B-stage1` as our foundation model, as this intermediate checkpoint provides strong baseline capabilities while allowing for a clear attribution of performance gains to our enrichment strategies. This model has already developed strong language modeling capabilities but precedes the knowledge-intensive tuning of stage 2, providing an ideal balance without the risk of catastrophic forgetting of instruction-following abilities during domain adaptation. Notably, the data mix used in phase 1 includes DCLM, a dataset filtered from web data using a classifier trained on instruction data, which gives OLMo2-7B relatively strong question-answering capabilities even after stage 1. |
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Each Biomed-Enriched variant was trained for exactly 33.6 billion tokens using identical hyperparameters. We follow the annealing strategy of OLMo2 used in the mid-training phase. By maintaining strict parameter parity across experiments, we created a controlled environment focused solely on measuring the effectiveness of different data curation strategies. |
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These experiments are designed to illustrate how our granular annotations enable targeted improvements in model capabilities. For instance, by specifically upsampling clinical content (`BE-Clinical` and `BE-ClinicalCase` variants), we expect to see a notable increase in performance on the MMLU Professional Medicine benchmark, underscoring the dataset's potential for developing specialized models. |
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The following variants were created for this evaluation: |
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* **BE-Base:** The complete unmodified PMC Open Access Subset serving as baseline. |
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* **BE-Educational:** Preserves all articles but removes paragraphs with educational quality scores below 3. |
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* **BE-Clinical:** Replicates articles with predominantly clinical domain content 10× in the training mix. |
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* **BE-ClinicalCase:** Replicates articles containing at least one clinical case paragraph 10× to increase exposure to clinical narratives. |
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* **BE-Prefix:** Prefixes each paragraph with its predicted annotations to allow modeling of metadata-content relationships. |
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* **BE-French:** Upsamples articles containing French text 10× to address language imbalance. |
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* **BE-All:** Combines quality filtering (score ≥ 3), upsampling of clinical content, French text, and clinical cases, plus metadata prefixing. |
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### Performance Results |
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**SOTA Models for reference** |
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| Model | MedQA | MedMCQA | PubMedQA | Anat | Clin | Bio | Med | Gen | Prof | Avg | |
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| ----------------- | :---: | :-----: | :------: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | |
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| Llama-3-8B | 59.70 | 57.47 | 74.80 | 68.89 | 74.72 | 78.47 | 61.85 | 83.00 | 70.22 | 69.90 | |
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| Meditron-70B | 57.10 | 46.80 | 76.60 | 53.30 | 66.70 | 76.30 | 63.00 | 69.00 | 71.60 | 64.49 | |
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**Benchmark Results by Dataset Variant (continual pre-training of OLMo2-7B-stage1)** |
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| Variant | MedQA | MedMCQA | PubMedQA | Anat | Clin | Bio | Med | Gen | Prof | Avg | |
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| --------------- | :------------: | :------------: | :------------: | :------------: | :------------: | :------------: | :------------: | :------------: | :------------: | :------------: | |
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| OLMo2-7B-stage1 | 45.33 | 41.14 | 75.60 | 54.81 | 63.40 | 69.44 | 53.18 | 69.00 | 59.93 | 59.09 | |
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| BE-Base | 44.85 | 41.91 | 76.40 | 57.04 | 64.15 | **70.83** | **59.54** | 69.00 | 59.93 | 60.41 | |
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| BE-Clinical | 41.95 | 39.35 | 76.60 | 53.33 | 63.40 | 65.28 | 58.38 | 66.00 | **63.97** | 58.70 | |
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| BE-ClinicalCase | 42.11 | 39.52 | 76.60 | 57.04 | 64.91 | 66.67 | **59.54** | 69.00 | 62.87 | 59.81 | |
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| BE-Prefix | 45.72 | 41.76 | **77.80** | 57.04 | 64.53 | 68.75 | 57.23 | 66.00 | 61.76 | 60.07 | |
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| BE-Educational | 45.64 | **43.08** | 77.00 | 57.04 | 65.28 | 68.06 | 56.65 | **71.00** | 58.82 | 60.29 | |
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| BE-All | **47.21** | 42.79 | 76.60 | **60.00** | **65.66** | 68.06 | 58.96 | 69.00 | 61.40 | **61.08** | |
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*Note: The first three columns represent Medical QA benchmarks. The following six (Anat, Clin, Bio, Med, Gen, Prof) are sub-tasks from MMLU Medical. MMLU abbreviations: Anat=Anatomy, Clin=Clinical Knowledge, Bio=College Biology, Med=College Medicine, Gen=Medical Genetics, Prof=Professional Medicine.* |
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### Results Analysis |
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**Overall performance.** BE-All achieved the highest average performance across benchmarks at 61.08%, surpassing BE-Base (60.41%) by a small but consistent margin (+0.67 pts, see table above). Its strongest improvements appeared in MedQA (47.21%), MMLU Anatomy (60.00%), and Clinical Knowledge (65.66%), suggesting the effectiveness of combining multiple targeted enrichment strategies. |
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**Clinical enrichment.** Clinical enrichment (BE-Clinical) significantly boosted performance on MMLU Professional Medicine benchmark (63.97%, +4.04 pts vs. BE-Base, Figure 2). This improvement was stable from early training, highlighting how clinical narratives enhance the model’s clinical reasoning abilities efficiently. |
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**Educational filtering.** Educational filtering (BE-Educational) consistently improved performance on medical question-answering tasks, notably Medical Genetics (71.00%, +2 pts), MedMCQA (43.08%, +1.17 pts), and PubMedQA (77.00%, +0.6 pts). These tasks likely benefit from the knowledge present in educationally high-quality paragraphs (Figure 2). |
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**Metadata prefixing.** Metadata prefixing (BE-Prefix) specifically improved performance on PubMedQA (77.80%, +1.4 pts vs. BE-Base). Providing explicit paragraph-level metadata helped primarily with structured document comprehension, but it had limited benefits for other tasks. |
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**General biomedical knowledge trade-off.** BE-Base performed better on College Biology (70.83%) than others. Building a biology variant (BE-Bio) could be an interesting future direction, as the current dataset does not specifically target this domain. |
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**Non-English enrichment.** BE-French showed clear improvements in French medical QA (FrenchMedMCQA), achieving 40.5% accuracy, significantly surpassing BE-Base and the OLMo2-7B-stage1 baseline (38.32%, Figure 1). These results illustrate effective adaptation to non-English contexts without modifying the underlying model architecture. |
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**Data efficiency and training stability.** As shown in Figure 2, BE-All reached robust benchmark performance using roughly one-third of the tokens required by BE-Base. Individual enrichments (Educational, Clinical) also displayed early and stable improvements, underscoring potential reductions in training time and computational cost. |
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## Licensing |
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The Biomed-Enriched annotations (document type, domain, educational quality scores, and metadata) are released under the MIT License. |
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The textual content licensing depends on the individual article licenses from PubMed Central Open Access. Each paragraph includes a `license_url` field pointing to the specific license. Users must comply with the respective license terms when using the textual data. |
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## How to Cite |
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Please cite Biomed-Enriched using: |
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``` |
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@misc{touchent2025biomedenrichedbiomedicaldatasetenriched, |
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title={Biomed-Enriched: A Biomedical Dataset Enriched with LLMs for Pretraining and Extracting Rare and Hidden Content}, |
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author={Rian Touchent and Nathan Godey and Eric de la Clergerie}, |
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year={2025}, |
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eprint={2506.20331}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2506.20331}, |
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} |
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``` |
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[Paper](https://huggingface.co/papers/2506.20331) |
