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--- |
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library_name: transformers |
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license: mit |
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base_model: |
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- FacebookAI/xlm-roberta-base |
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--- |
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# Biomedical-Enriched Classifier |
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This is the model used to create the [**Biomed-Enriched**](https://huggingface.co/datasets/almanach/Biomed-Enriched) dataset. |
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## Model Details |
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- **Base Model:** `xlm-roberta-base` |
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- **Model Type:** Multi-task model combining multi-label classification and regression. |
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- **Description:** This model was fine-tuned to classify paragraphs from biomedical texts for their domain and document type, and predict an educational quality score via regression. |
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## Training |
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The model was trained on a set of 400,000 paragraphs from PubMed Central, which were annotated by the [Llama 3.1 70B Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct) model. |
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## Purpose |
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This classifier was created to scale the initial high-quality annotations to the entire PubMed Open Access dataset. This distillation process enabled the creation of the large-scale Biomed-Enriched dataset while maintaining annotation consistency. |
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## Model Outputs |
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The model predicts the following outputs: |
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### Domain (Classification) |
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- `Clinical` |
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- `Biomedical` |
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- `Other` |
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### Document Type (Classification) |
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- `Clinical Case` |
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- `Study` |
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- `Review` |
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- `Other` |
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### Educational Quality (Regression) |
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- A regression score from `1` (low quality) to `5` (high quality). |
