Datasets:
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README.md
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- split: noncommercial
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path: data/noncommercial-*
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pretty_name: Biomed-Enriched
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---
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1. **Initial Annotation by Large Language Model**:
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2. **Annotation Scaling via Model Distillation**:
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## Annotation Statistics
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- split: noncommercial
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path: data/noncommercial-*
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pretty_name: Biomed-Enriched
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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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---
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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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