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--- |
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configs: |
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- config_name: text-corpus |
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data_files: "data/corpus.jsonl" |
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- config_name: question-answer-passages |
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data_files: |
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- split: dev |
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path: "data/dev.jsonl" |
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- split: eval |
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path: "data/eval.jsonl" |
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language: |
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- en |
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license: cc-by-nc-sa-4.0 |
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datasets: |
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- bioasq |
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task_categories: |
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- question-answering |
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- sentence-similarity |
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tags: |
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- biomedical |
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- rag |
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- pubmed |
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- bioasq |
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- biomedical-qa |
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library_name: huggingface |
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pretty_name: BioASQ 12B RAG Dataset |
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--- |
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# BioASQ 12B RAG Dataset |
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A processed version of the BioASQ 12B dataset optimized for Retrieval-Augmented Generation (RAG) applications in biomedical question answering. |
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This dataset contains two distinct subsets specifically designed for RAG applications: |
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1. **A text corpus of PubMed abstracts** ready for indexing and retrieval, containing detailed metadata and full abstract text. |
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2. **An evaluation dataset** consisting of biomedical questions, each paired with an ideal answer and a list of passage IDs that are relevant to answering the question. |
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This structure makes it ideal for building and evaluating RAG systems that retrieve relevant biomedical information from a corpus and generate accurate, evidence-based answers to complex biomedical questions. |
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The code to generate this dataset is here: https://github.com/MattMorgis/bioasq-rag |
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## Dataset Structure |
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The dataset contains three main components: |
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1. **Corpus** (`data/corpus.jsonl`): A collection of PubMed abstracts including metadata. |
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- The corpus is accessible through the "train" split of the "text-corpus" config |
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- Each document contains: |
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- `id`: PubMed ID |
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- `title`: Title of the paper |
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- `text`: Abstract text |
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- `url`: PubMed URL |
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- `publication_date`: Publication date |
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- `journal`: Journal name |
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- `authors`: List of authors |
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- `doi`: Digital Object Identifier (if available) |
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- `keywords`: Keywords |
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- `mesh_terms`: MeSH terms |
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2. **Dev Questions** (`data/dev.jsonl`): Development set of biomedical questions. |
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- The dev questions are accessible through the "dev" split of the "question-answer-passages" config |
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- Each question contains: |
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- `question_id`: Unique identifier for the question |
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- `question`: The question text |
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- `answer`: Ideal answer |
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- `relevant_passage_ids`: List of PubMed IDs for relevant abstracts |
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- `type`: Question type (e.g., factoid, list, yes/no, summary) |
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- `snippets`: Relevant snippets from abstracts |
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3. **Eval Questions** (`data/eval.jsonl`): Eval set of biomedical questions. |
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- Same structure as dev questions, accessible through the "eval" split |
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## Usage |
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This dataset is designed for training and evaluating RAG systems for biomedical question answering. |
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### Loading the Dataset |
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You can load the dataset using the Hugging Face `datasets` library. **Note that you must specify a config name**: |
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```python |
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from datasets import load_dataset |
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# Load the corpus of PubMed abstracts |
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corpus_dataset = load_dataset("mattmorgis/bioasq-12b-rag", "text-corpus") |
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# Load the question-answer dataset |
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questions_dataset = load_dataset("mattmorgis/bioasq-12b-rag", "question-answer-passages") |
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# Access the corpus data (note: the corpus is stored in the "train" split) |
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corpus_docs = corpus_dataset["train"] |
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# Access the development questions |
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dev_questions = questions_dataset["dev"] |
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# Access the eval questions |
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eval_questions = questions_dataset["eval"] |
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``` |
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### Example RAG Application |
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This dataset can be used to build a biomedical RAG system: |
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1. Index the corpus using a vector database (e.g., FAISS, Chroma) |
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2. Embed questions using a biomedical or general purpose text embedding model |
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3. Retrieve relevant documents from the corpus based on question embeddings |
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4. Generate answers using a large language model (LLM) with the retrieved context |
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### Evaluation |
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The dataset provides gold standard answers and relevant passage IDs that can be used to evaluate: |
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- Retrieval accuracy |
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- Answer quality |
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- Domain-specific knowledge incorporation |
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## Source |
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This dataset is derived from the [BioASQ Challenge](http://bioasq.org/) task 12b dataset. |
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Anastasia Krithara, Anastasios Nentidis, Konstantinos Bougiatiotis, Georgios Paliouras. BioASQ-QA: A manually curated corpus for Biomedical Question Answering. bioRxiv 2022.12.14.520213; doi: https://doi.org/10.1101/2022.12.14.520213 |
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