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license: apache-2.0 |
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task_categories: |
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- question-answering |
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language: |
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- en |
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--- |
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# 📚 mmrag benchmark |
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## 📁 Files Overview |
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- `mmrag_train.json`: Training set for model training. |
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- `mmrag_dev.json`: Validation set for hyperparameter tuning and development. |
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- `mmrag_test.json`: Test set for evaluation. |
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- `processed_documents.json`: The chunks used for retrieval. |
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--- |
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## 🔍 Query Datasets: `mmrag_train.json`, `mmrag_dev.json`, `mmrag_test.json` |
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The three files are all lists of dictionaries. Each dictionary contains the following fields: |
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### 🔑 `id` |
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- **Description**: Unique query identifier, structured as `SourceDataset_queryIDinDataset`. |
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- **Example**: `ott_144`, means this query is picked from OTT-QA dataset |
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### ❓ `query` |
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- **Description**: Text of the query. |
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- **Example**: `"What is the capital of France?"` |
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### ✅ `answer` |
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- **Description**: The gold-standard answer corresponding to the query. |
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- **Example**: `"Paris"` |
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### 📑 `relevant_chunks` |
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- **Description**: Dictionary of annotated chunk IDs and their corresponding relevance scores. The context of chunks can be get from processed_documents.json. relevance score is in range of {0(irrelevant), 1(Partially relevant), 2(gold)} |
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- **Example**: ```json{"ott_23573_2": 1, "ott_114_0": 2, "m.12345_0": 0}``` |
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### 📖 `ori_context` |
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- **Description**: A list of the original document IDs related to the query. This field can help to get the relevant document provided by source dataset. |
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- **Example**: `["ott_144"]`, means all chunk IDs start with "ott_114" is from the original document. |
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### 📜 `dataset_score` |
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- **Description**: The datset-level relevance labels. With the routing score of all datasets regarding this query. |
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- **Example**: `{"tat": 0, "triviaqa": 2, "ott": 4, "kg": 1, "nq": 0}`, where 0 means there is no relevant chunks in the dataset. The higher the score is, the more relevant chunks the dataset have. |
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--- |
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## 📚 Knowledge Base: `processed_documents.json` |
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This file is a list of chunks used for document retrieval, which contains the following fields: |
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### 🔑 `id` |
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- **Description**: Unique document identifier, structured as `dataset_documentID_chunkIndex`, equivalent to `dataset_queryID_chunkIndex` |
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- **example1**: `ott_8075_0` (chunks from NQ, TriviaQA, OTT, TAT) |
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- **example2**: `m.0cpy1b_5` (chunks from documents of knowledge graph(Freebase)) |
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### 📄 `text` |
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- **Description**: Text of the document. |
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- **Example**: `A molecule editor is a computer program for creating and modifying representations of chemical structures.` |
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--- |
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## 📄 License |
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This dataset is licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). |
