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README.md

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+---
+license: mit
+dataset_info:
+- config_name: documents
+  features:
+  - name: question_id
+    dtype: int64
+  - name: documents
+    sequence: string
+  splits:
+  - name: train
+    num_bytes: 18770828
+    num_examples: 1172
+  download_size: 10608695
+  dataset_size: 18770828
+- config_name: question-answer
+  features:
+  - name: id
+    dtype: int64
+  - name: question
+    dtype: string
+  - name: answer
+    dtype: string
+  - name: relevant_passage_ids
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 386574
+    num_examples: 1172
+  download_size: 252573
+  dataset_size: 386574
+configs:
+- config_name: documents
+  data_files:
+  - split: train
+    path: documents/train-*
+- config_name: question-answer
+  data_files:
+  - split: train
+    path: question-answer/train-*
+task_categories:
+- question-answering
+language:
+- en
+tags:
+- biology
+size_categories:
+- 1K<n<10K
+---
+
+This dataset is a fork of [rag-datasets/rag-mini-bioasq](https://huggingface.co/datasets/rag-datasets/rag-mini-bioasq).
+
+The use case for this dataset is to test and evaluate an LLM or a RAG application's ability to filter out irrelevant information to answer a given question.
+
+This forks filters away all questions with one or more "NaN" passages, and adds an average of 80% irrelevant passages to each question.

dataset_dict.json

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+{"splits": ["question-answer", "documents"]}

documents/data-00000-of-00001.arrow

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documents/dataset_info.json

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+{
+  "citation": "",
+  "description": "",
+  "features": {
+    "question_id": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "documents": {
+      "feature": {
+        "dtype": "string",
+        "_type": "Value"
+      },
+      "_type": "Sequence"
+    }
+  },
+  "homepage": "",
+  "license": ""
+}

documents/state.json

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+    }
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+  "_split": null
+}

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question-answer/dataset_info.json

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+  "citation": "",
+  "description": "",
+  "features": {
+    "id": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "question": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "answer": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "relevant_passage_ids": {
+      "dtype": "string",
+      "_type": "Value"
+    }
+  },
+  "homepage": "",
+  "license": ""
+}

question-answer/state.json

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+    }
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