README.md

metadata

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.

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🛠 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

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

# 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.

# 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}")

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🔍 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.

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📚 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.

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📄 License

This dataset is licensed under the Apache License 2.0.