README.md

---
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](https://www.kaggle.com/competitions/icaif-24-finance-rag-challenge)

## Datasets
![Figure 1](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F7863652%2Ffe9d7f029a218a75b06d4b866480655a%2Fimage.png?generation=1727154474415109&alt=media)

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.

``` python
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:

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