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Update README.md

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  1. README.md +27 -17
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@@ -48,6 +48,16 @@ configs:
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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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  ![Biomed-Enriched](logo.png)
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@@ -115,27 +125,27 @@ 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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  - 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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  ![Biomed-Enriched](logo.png)
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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.
132
+ - **Study**: Research paragraph with methods, results, and discussion of experiments or observations.
133
+ - **Review**: Summary or synthesis of current knowledge on a specific topic.
134
+ - **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.
139
+ - **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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