README.md

metadata

language:
  - en
license: mit
configs:
  - config_name: FinDER
    data_files:
      - split: corpus
        path: FinDER/corpus.jsonl.gz
      - split: queries
        path: FinDER/queries.jsonl.gz
  - config_name: ConvFinQA
    data_files:
      - split: corpus
        path: ConvFinQA/corpus.jsonl.gz
      - split: queries
        path: ConvFinQA/queries.jsonl.gz
  - config_name: FinQA
    data_files:
      - split: corpus
        path: FinQA/corpus.jsonl.gz
      - split: queries
        path: FinQA/queries.jsonl.gz
  - config_name: FinQABench
    data_files:
      - split: corpus
        path: FinQABench/corpus.jsonl.gz
      - split: queries
        path: FinQABench/queries.jsonl.gz
  - config_name: FinanceBench
    data_files:
      - split: corpus
        path: FinanceBench/corpus.jsonl.gz
      - split: queries
        path: FinanceBench/queries.jsonl.gz
  - config_name: MultiHiertt
    data_files:
      - split: corpus
        path: MultiHeirtt/corpus.jsonl.gz
      - split: queries
        path: MultiHeirtt/queries.jsonl.gz
  - config_name: TATQA
    data_files:
      - split: corpus
        path: TATQA/corpus.jsonl.gz
      - split: queries
        path: TATQA/queries.jsonl.gz

Dataset Card for FinanceRAG

Dataset Summary

The detailed description of dataset and reference will be added after the competition in Kaggle/FinanceRAG Challenge

Datasets

1. Passage Retrieval:

   • FinDER: Involves retrieving relevant sections from 10-K Reports and financial disclosures based on Search Queries that simulate real-world questions asked by financial professionals, using domain-specific jargon and abbreviations.
   • FinQABench: Focuses on testing AI models' ability to answer Search Queries over 10-K Reports with accuracy, evaluating the system's ability to detect hallucinations and ensure factual correctness in generated answers.
   • FinanceBench: Uses Natural Queries to retrieve relevant information from public filings like 10-K and Annual Reports. The aim is to evaluate how well systems handle straightforward, real-world financial questions.

2. Tabular and Text Retrieval:

   • TATQA: Requires participants to answer Natural Queries that involve numerical reasoning over hybrid data, which combines tables and text from Financial Reports. Tasks include basic arithmetic, comparisons, and logical reasoning.
   • FinQA: Demands answering complex Natural Queries over Earnings Reports using multi-step numerical reasoning. Participants must accurately extract and calculate data from both textual and tabular sources.
   • ConvFinQA: Involves handling Conversational Queries where participants answer multi-turn questions based on Earnings Reports, maintaining context and accuracy across multiple interactions.
   • MultiHiertt: Focuses on Multi-Hop Queries, requiring participants to retrieve and reason over hierarchical tables and unstructured text from Annual Reports, making this one of the more complex reasoning tasks involving multiple steps across various document sections.

Files

For each dataset, you are provided with two files:

• corpus.jsonl - This is a JSONLines file containing the context corpus. Each line in the file represents a single document in JSON format.
• queries.jsonl - This is a JSONLines file containing the queries. Each line in this file represents one query in JSON format.

Both files follow the jsonlines format, where each line corresponds to a separate data instance in JSON format.

Here’s an expanded description including explanations for each line:

• _id: A unique identifier for the context/query.
• title: The title or headline of the context/query.
• text: The full body of the document/query, containing the main content.

How to Use

The following code demonstrates how to load a specific subset (in this case, FinDER) from the FinanceRAG dataset on Hugging Face. In this example, we are loading the corpus split, which contains the document data relevant for financial analysis.

The load_dataset function is used to retrieve the dataset, and a loop is set up to print the first document entry from the dataset, which includes fields like _id, title, and text.

Each document provides detailed descriptions from financial reports, which participants can use for tasks such as retrieval and answering financial queries.

from datasets import load_dataset

# Loading a specific subset (i.e. FinDER) and a split (corpus, queries)
dataset = load_dataset("Linq-AI-Research/FinanceRAG", "FinDER", split="corpus")

for example in dataset:
  print(example)
  break

Here is an example result of python output of FinDER from the corpus split:

{
  '_id' : 'ADBE20230004',
  'title': 'ADBE OVERVIEW',
  'text': 'Adobe is a global technology company with a mission to change the world through personalized digital experiences...'
}