Datasets:

parsee-ai
/

finRAG

@@ -13,6 +13,8 @@ size_categories:
 This is the official Huggingface repo of the finRAG datasets published by [parsee.ai](https://parsee.ai).
 We wanted to investigate how good the current state of the art (M)LLMs are at solving the relatively simple problem of extracting revenue figures from publicly available financial reports. To test this, we created 3 different datasets, all based on the same selection of 100 randomly selected annual reports for the year 2023 of publicly listed US companies. The 3 datasets are the following:
 “Selection-text”: this dataset contains only the relevant profit & loss statement with the revenue numbers that we are looking for. It can be considered our “base-case”, as extracting the revenue numbers from this table only should be the easiest.

 This is the official Huggingface repo of the finRAG datasets published by [parsee.ai](https://parsee.ai).
+More detailed information about the 3 datasets and methodology can be found in the [sub-directories for the individual datasets](https://github.com/parsee-ai/parsee-datasets/tree/main/datasets/finrag/data).
 We wanted to investigate how good the current state of the art (M)LLMs are at solving the relatively simple problem of extracting revenue figures from publicly available financial reports. To test this, we created 3 different datasets, all based on the same selection of 100 randomly selected annual reports for the year 2023 of publicly listed US companies. The 3 datasets are the following:
 “Selection-text”: this dataset contains only the relevant profit & loss statement with the revenue numbers that we are looking for. It can be considered our “base-case”, as extracting the revenue numbers from this table only should be the easiest.