---
license: apache-2.0
task_categories:
- question-answering
language:
- en
---
# 📚 mmrag benchmark

## 📁 Files Overview

- `mmrag_train.json`: Training set for model training.
- `mmrag_dev.json`: Validation set for hyperparameter tuning and development.
- `mmrag_test.json`: Test set for evaluation.
- `processed_documents.json`: The chunks used for retrieval. 

---
## 🛠 Example: How to Use mmRAG dataset

You can load and work with the mmRAG dataset using standard Python libraries like `json`. Below is a simple example of how to load and interact with the data files.

### ✅ Step 1: Load the Dataset

```python
import json

# Load query datasets
with open("mmrag_train.json", "r", encoding="utf-8") as f:
    train_data = json.load(f)

with open("mmrag_dev.json", "r", encoding="utf-8") as f:
    dev_data = json.load(f)

with open("mmrag_test.json", "r", encoding="utf-8") as f:
    test_data = json.load(f)

# Load document chunks
with open("processed_documents.json", "r", encoding="utf-8") as f:
    documents = json.load(f)
    # Load as dict if needed
    documents = {doc["id"]: doc["text"] for doc in documents}
```
### ✅ Step 2: Access Query and Document Examples

```python
# Example query
query_example = train_data[0]
print("Query:", query_example["query"])
print("Answer:", query_example["answer"])
print("Relevant Chunks:", query_example["relevant_chunks"])

# Get the text of a relevant chunk
for chunk_id, relevance in query_example["relevant_chunks"].items():
    if relevance > 0:
        print(f"Chunk ID: {chunk_id}, Relevance label: {relevance}\nText: {documents[chunk_id]}")
```
### ✅ Step 3: Get Sorted Routing Scores
The following example shows how to extract and sort the `dataset_score` field of a query to understand which dataset is most relevant to the query.

```python
# Choose a query from the dataset
query_example = train_data[0]

print("Query:", query_example["query"])
print("Answer:", query_example["answer"])

# Get dataset routing scores
routing_scores = query_example["dataset_score"]

# Sort datasets by relevance score (descending)
sorted_routing = sorted(routing_scores.items(), key=lambda x: x[1], reverse=True)

print("\nRouting Results (sorted):")
for dataset, score in sorted_routing:
    print(f"{dataset}: {score}")
```

---

## 🔍 Query Datasets: `mmrag_train.json`, `mmrag_dev.json`, `mmrag_test.json`

The three files are all lists of dictionaries. Each dictionary contains the following fields:

### 🔑 `id`

- **Description**: Unique query identifier, structured as `SourceDataset_queryIDinDataset`.
- **Example**: `ott_144`, means this query is picked from OTT-QA dataset

### ❓ `query`

- **Description**: Text of the query.
- **Example**: `"What is the capital of France?"`

### ✅ `answer`

- **Description**: The gold-standard answer corresponding to the query.
- **Example**: `"Paris"`

### 📑 `relevant_chunks`

- **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)}
- **Example**: ```json{"ott_23573_2": 1, "ott_114_0": 2, "m.12345_0": 0}```

### 📖 `ori_context`

- **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.
- **Example**: `["ott_144"]`, means all chunk IDs start with "ott_114" is from the original document.

### 📜 `dataset_score`

- **Description**: The datset-level relevance labels. With the routing score of all datasets regarding this query.
- **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.

---

## 📚 Knowledge Base: `processed_documents.json`

This file is a list of chunks used for document retrieval, which contains the following fields:

### 🔑 `id`

- **Description**: Unique document identifier, structured as `dataset_documentID_chunkIndex`, equivalent to `dataset_queryID_chunkIndex`
- **example1**: `ott_8075_0`  (chunks from NQ, TriviaQA, OTT, TAT)
- **example2**: `m.0cpy1b_5`  (chunks from documents of knowledge graph(Freebase))

### 📄 `text`

- **Description**: Text of the document.
- **Example**: `A molecule editor is a computer program for creating and modifying representations of chemical structures.`

---

## 📄 License

This dataset is licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).