Update README.md

README.md

@@ -46,3 +46,43 @@ configs:
   - split: queries
     path: TATQA/queries.jsonl.gz
 ---
+# Dataset Card for FinanceRAG
+
+## Dataset Summary
+
+TBD.
+
+## 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**: This task 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**: Participants use **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**: This task 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
+
+``` python
+from datasets import load_dataset
+
+```