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--- |
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language: |
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- en |
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license: mit |
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configs: |
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- config_name: FinDER |
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data_files: |
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- split: corpus |
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path: FinDER/corpus.jsonl.gz |
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- split: queries |
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path: FinDER/queries.jsonl.gz |
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- config_name: ConvFinQA |
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data_files: |
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- split: corpus |
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path: ConvFinQA/corpus.jsonl.gz |
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- split: queries |
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path: ConvFinQA/queries.jsonl.gz |
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- config_name: FinQA |
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data_files: |
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- split: corpus |
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path: FinQA/corpus.jsonl.gz |
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- split: queries |
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path: FinQA/queries.jsonl.gz |
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- config_name: FinQABench |
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data_files: |
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- split: corpus |
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path: FinQABench/corpus.jsonl.gz |
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- split: queries |
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path: FinQABench/queries.jsonl.gz |
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- config_name: FinanceBench |
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data_files: |
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- split: corpus |
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path: FinanceBench/corpus.jsonl.gz |
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- split: queries |
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path: FinanceBench/queries.jsonl.gz |
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- config_name: MultiHiertt |
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data_files: |
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- split: corpus |
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path: MultiHeirtt/corpus.jsonl.gz |
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- split: queries |
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path: MultiHeirtt/queries.jsonl.gz |
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- config_name: TATQA |
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data_files: |
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- split: corpus |
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path: TATQA/corpus.jsonl.gz |
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- split: queries |
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path: TATQA/queries.jsonl.gz |
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--- |
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# Dataset Card for FinanceRAG |
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## Dataset Summary |
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The detailed description of dataset and reference will be added after the competition in [Kaggle/FinanceRAG Challenge](https://www.kaggle.com/competitions/icaif-24-finance-rag-challenge) |
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## Datasets |
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1. **Passage Retrieval**: |
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- **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. |
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- **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. |
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- **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. |
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2. **Tabular and Text Retrieval**: |
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- **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. |
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- **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. |
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- **ConvFinQA**: Involves handling **Conversational Queries** where participants answer multi-turn questions based on **Earnings Reports**, maintaining context and accuracy across multiple interactions. |
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- **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. |
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## Files |
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For each dataset, you are provided with two files: |
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* **corpus.jsonl** - This is a `JSONLines` file containing the context corpus. Each line in the file represents a single document in `JSON` format. |
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* **queries.jsonl** - This is a `JSONLines` file containing the queries. Each line in this file represents one query in `JSON` format. |
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Both files follow the jsonlines format, where each line corresponds to a separate data instance in `JSON` format. |
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Here’s an expanded description including explanations for each line: |
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- **_id**: A unique identifier for the context/query. |
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- **title**: The title or headline of the context/query. |
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- **text**: The full body of the document/query, containing the main content. |
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### How to Use |
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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. |
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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`. |
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Each document provides detailed descriptions from financial reports, which participants can use for tasks such as retrieval and answering financial queries. |
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``` python |
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from datasets import load_dataset |
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# Loading a specific subset (i.e. FinDER) and a split (corpus, queries) |
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dataset = load_dataset("Linq-AI-Research/FinanceRAG", "FinDER", split="corpus") |
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for example in dataset: |
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print(example) |
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break |
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``` |
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Here is an example result of `python` output of **FinDER** from the `corpus` split: |
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```json |
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{ |
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'_id' : 'ADBE20230004', |
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'title': 'ADBE OVERVIEW', |
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'text': 'Adobe is a global technology company with a mission to change the world through personalized digital experiences...' |
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} |
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``` |
