Update README.md

README.md

@@ -47,4 +47,210 @@ configs:
     path: data/commercial-*
   - split: noncommercial
     path: data/noncommercial-*
+pretty_name: Biomed-Enriched
 ---
+![Biomed-Enriched](logo.png)
+
+# Biomed-Enriched: A Biomedical Dataset Enriched with LLMs for Pretraining and Extracting Rare and Hidden Content 
+
+### Dataset Authors
+
+**Rian Touchent, Nathan Godey & Eric de la Clergerie**  
+*Sorbonne Université, INRIA Paris*
+
+### Overview
+
+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, 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.
+
+The dataset is structured into two primary splits:
+
+* **Commercial**
+* **Non-Commercial**
+
+## Dataset Structure
+
+### Commercial Split
+
+* **text**: Textual content of the paragraphs.
+* **path**: Precise XML path referencing original paragraph locations.
+* **license\_url**: URL linking to the license.
+* **authors**: Comprehensive list of authors per paragraph for proper attribution compliance.
+
+### Non-Commercial Split
+
+* **path**: Precise XML path referencing original paragraph locations.
+* **license\_url**: URL linking to the license.
+* **authors**: Comprehensive list of authors per paragraph for proper attribution compliance.
+
+> **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.
+
+```bash
+pip install biomed-enriched
+```
+
+### With Python
+```python
+from biomed_enriched import populate
+
+DATASET_DIR = "/path/to/biomed-enriched"  # input dataset
+PMC_XML_ROOT = "/path/to/pmc/non-comm/xml"          # PMC XML dump
+OUTPUT_DIR = "/path/to/populated-biomed-enriched"  # drop arg to overwrite in-place
+
+populate(DATASET_DIR, PMC_XML_ROOT, output_path=OUTPUT_DIR, splits="noncommercial", num_proc=1)
+```
+The call overwrites the dataset in-place, adding a new `text` column as the third column (after `article_id`, `path`).
+
+### With CLI
+```bash
+biomed-enriched \
+  --input /path/to/biomed-enriched \
+  --xml-root /path/to/pmc/non-comm/xml \
+  --num-proc 8
+```
+Add `--output DIR` if you prefer writing to a new directory instead of overwriting.
+
+## Annotation Process
+
+The dataset was created using a two-stage annotation framework:
+
+1. **Initial Annotation by Large Language Model**:
+
+   * Annotated a subset of paragraphs for the following categories:
+   
+     - **Document Type**: Categorizes the structure and purpose of the content.
+       - **Clinical Case**: Detailed report of symptoms, diagnosis, treatment, and follow-up of individual patients.
+       - **Study**: Research paragraph with methods, results, and discussion of experiments or observations.
+       - **Review**: Summary or synthesis of current knowledge on a specific topic.
+       - **Other**: Content not fitting above categories (editorials, commentaries, policy paragraphs).
+     - **Domain**: Identifies the subject area focus.
+       - **Clinical**: Content relating to patient care, clinical trials, case reports, or practice guidelines.
+       - **Biomedical**: Scientific aspects of medicine and biology.
+       - **Other**: Content mentioning biomedical topics but focusing on administrative, policy, or general communications.
+     - **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.
+       - **Score 1**: Basic information relevant to biomedical topics, may contain irrelevant content.
+       - **Score 2**: Addresses biomedical education elements but with limitations in coherence or depth.
+       - **Score 3**: Appropriate for college-level curricula, introduces key concepts with reasonable coherence.
+       - **Score 4**: Highly relevant educational content with clear writing style, minimal irrelevant information.
+       - **Score 5**: Outstanding educational value, detailed reasoning with profound insights for college-level learning.
+
+2. **Annotation Scaling via Model Distillation**:
+
+   * These annotations were distilled into a XLM-RoBERTa-base model, enabling scalability to the entire PMC dataset.
+
+## Annotation Statistics
+
+Here is the distribution of educational scores per domain:
+
+| Educational Score | Biomedical (n=116 221 134) | Clinical (n=2 182 784) | Other (n=15 213 051) |
+| :---------------: | :------------------------: | :--------------------: | :------------------: |
+|         1         |            1.8 %           |          6.0 %         |        60.1 %        |
+|         2         |            9.4 %           |         23.4 %         |        29.4 %        |
+|         3         |           10.9 %           |         26.6 %         |         8.3 %        |
+|         4         |           75.3 %           |         44.0 %         |         2.1 %        |
+|         5         |            2.6 %           |            –           |           –          |
+
+Here is the distribution of educational scores per document type:
+
+| Educational Score | Study (n=100 387 809) | Review (n=6 811 226) | Clinical case (n=2 122 403) | Other (n=24 295 531) |
+|:-----------------:|:---------------------:|:--------------------:|:---------------------------:|:--------------------:|
+| 1                 | 0.7 %                 | 0.3 %                | 4.0 %                       | 43.4 %               |
+| 2                 | 7.9 %                 | 1.6 %                | 14.4 %                      | 30.9 %               |
+| 3                 | 10.0 %                | 5.0 %                | 24.6 %                      | 14.6 %               |
+| 4                 | 78.7 %                | 86.9 %               | 57.0 %                      | 11.1 %               |
+| 5                 | 2.6 %                 | 6.1 %                | 0.0 %                       | 0.0 %                |
+
+### Language Distribution
+
+
+| Language | Articles | Paragraphs | Clinical Case Paragraphs | % Clinical Cases |
+|----------|----------|------------|-------------------------|-----------------|
+| en       | 4,113,275| 131,579,445| 2,113,185               | 1.61            |
+| es       | 4,339    | 181,779    | 1,235                   | 0.68            |
+| zh-cn    | 3,649    | 59,719     | 0                       | 0.00            |
+| fr       | 3,410    | 173,325    | 2,586                   | 1.49            |
+| de       | 2,976    | 248,608    | 51                      | 0.02            |
+| it       | 2,708    | 274,819    | 521                     | 0.19            |
+| pt       | 934      | 85,242     | 4,540                   | 5.33            |
+| ko       | 636      | 25,535     | 0                       | 0.00            |
+| ru       | 222      | 10,553     | 0                       | 0.00            |
+| id       | 189      | 91,865     | 15                      | 0.02            |
+
+
+## Key Applications
+
+* Improve efficiency in biomedical pretraining by focusing on high-quality, specific content.
+* Create new biomedical subsets tailored to specific research needs based on document type and domain.
+
+## Evaluation
+
+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.
+
+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.
+
+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.
+
+The following variants were created for this evaluation:
+*   **BE-Base:** The complete unmodified PMC Open Access Subset serving as baseline.
+*   **BE-Educational:** Preserves all articles but removes paragraphs with educational quality scores below 3.
+*   **BE-Clinical:** Replicates articles with predominantly clinical domain content 10�� in the training mix.
+*   **BE-ClinicalCase:** Replicates articles containing at least one clinical case paragraph 10× to increase exposure to clinical narratives.
+*   **BE-Prefix:** Prefixes each paragraph with its predicted annotations to allow modeling of metadata-content relationships.
+*   **BE-French:** Upsamples articles containing French text 10× to address language imbalance.
+*   **BE-All:** Combines quality filtering (score ≥ 3), upsampling of clinical content, French text, and clinical cases, plus metadata prefixing.
+
+### Performance Results
+
+**SOTA Models for reference**
+
+| Model             | MedQA | MedMCQA | PubMedQA | Anat  | Clin  | Bio   | Med   | Gen   | Prof  | Avg   |
+| ----------------- | :---: | :-----: | :------: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
+| Llama-3-8B        | 59.70 |  57.47  |  74.80   | 68.89 | 74.72 | 78.47 | 61.85 | 83.00 | 70.22 | 69.90 |
+| Meditron-70B      | 57.10 |  46.80  |  76.60   | 53.30 | 66.70 | 76.30 | 63.00 | 69.00 | 71.60 | 64.49 |
+
+**Benchmark Results by Dataset Variant (continual pre-training of OLMo2-7B-stage1)**
+
+| Variant         | MedQA          | MedMCQA        | PubMedQA       | Anat           | Clin           | Bio            | Med            | Gen            | Prof           | Avg            |
+| --------------- | :------------: | :------------: | :------------: | :------------: | :------------: | :------------: | :------------: | :------------: | :------------: | :------------: |
+| OLMo2-7B-stage1   | 45.33 |  41.14  |  75.60   | 54.81 | 63.40 | 69.44 | 53.18 | 69.00 | 59.93 | 59.09 |
+| BE-Base         | 44.85          | 41.91          | 76.40          | 57.04          | 64.15          | **70.83**      | **59.54**      | 69.00          | 59.93          | 60.41          |
+| BE-Clinical     | 41.95          | 39.35          | 76.60          | 53.33          | 63.40          | 65.28          | 58.38          | 66.00          | **63.97**      | 58.70          |
+| BE-ClinicalCase | 42.11          | 39.52          | 76.60          | 57.04          | 64.91          | 66.67          | **59.54**      | 69.00          | 62.87          | 59.81          |
+| BE-Prefix       | 45.72          | 41.76          | **77.80**      | 57.04          | 64.53          | 68.75          | 57.23          | 66.00          | 61.76          | 60.07          |
+| BE-Educational  | 45.64          | **43.08**      | 77.00          | 57.04          | 65.28          | 68.06          | 56.65          | **71.00**      | 58.82          | 60.29          |
+| BE-All          | **47.21**      | 42.79          | 76.60          | **60.00**      | **65.66**      | 68.06          | 58.96          | 69.00          | 61.40          | **61.08**      |
+
+*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.*
+
+### Results Analysis
+
+**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.
+
+**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.
+
+**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).
+
+**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.
+
+**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.
+
+**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.
+
+![Benchmark Results](french_medmcqa_performance.png)
+
+**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.
+
+![Benchmark Results](combined_plot.png)
+
+## Licensing
+
+The Biomed-Enriched annotations (document type, domain, educational quality scores, and metadata) are released under the MIT License.
+
+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.
+
+## How to Cite
+
+Please cite Biomed-Enriched using:
+
+```
+[coming soon]
+```