The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 159, in compute
compute_split_names_from_info_response(
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 131, in compute_split_names_from_info_response
config_info_response = get_previous_step_or_raise(kind="config-info", dataset=dataset, config=config)
File "/src/libs/libcommon/src/libcommon/simple_cache.py", line 567, in get_previous_step_or_raise
raise CachedArtifactError(
libcommon.simple_cache.CachedArtifactError: The previous step failed.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 499, in get_dataset_config_info
for split_generator in builder._split_generators(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 62, in _split_generators
self.info.features = datasets.Features.from_arrow_schema(pq.read_schema(f))
File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 2325, in read_schema
file = ParquetFile(
File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 318, in __init__
self.reader.open(
File "pyarrow/_parquet.pyx", line 1470, in pyarrow._parquet.ParquetReader.open
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 75, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 572, in get_dataset_split_names
info = get_dataset_config_info(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 504, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
finRAG Datasets
This is the official Huggingface repo of the finRAG datasets published by parsee.ai.
More detailed information about the 3 datasets and methodology can be found in the sub-directories for the individual datasets.
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.
“RAG-text”: this dataset tries to simulate a real-world RAG-application, where we chunk the original document into pieces, perform a vector search based on the question that we want to solve, and present the LLMs with the most relevant chunks. We cut off all prompts at 8k tokens for this exercise, so in case the relevant table was not contained in the prompt, we inserted it at the “first position”, to simulate a “happy path” for the vector search, as the goal of this study is not to examine how good or bad vector search is working, but rather to focus on the capabilities of the LLMs if we can guarantee that all required information to solve a task is presented to the model.
“Selection-image”: this dataset is similar to the “Selection-text” dataset in the sense that we feed to the models only an image of the relevant profit & loss statement, that contains all the necessary information to solve the problem.
The datasets contain a combined total of 10,404 rows, 37,536,847 tokens and 1,156 images.
For an in-depth explanation and evaluation of 8 state of the art models on the dataset, please refer to our study
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