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nn-auto-bench-ds

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Split (1)

train

The dataset viewer is not available for this split.

Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    ValueError
Message:      image at imagesets/0992c072-65ca-4fa3-8a3d-d1066b7bcff0/00898408-b4af-4948-8ea0-33a954ac792a.jpg doesn't have metadata in hf://datasets/nanonets/nn-auto-bench-ds@39f80b698af3d0554493b81a7063d577ecef6152/metadata.jsonl.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                File "/src/libs/libcommon/src/libcommon/utils.py", line 197, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2226, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 219, in __iter__
                  for key_example in islice(self.generate_examples_fn(**gen_kwags), shard_example_idx_start, None):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py", line 311, in _generate_examples
                  raise ValueError(
              ValueError: image at imagesets/0992c072-65ca-4fa3-8a3d-d1066b7bcff0/00898408-b4af-4948-8ea0-33a954ac792a.jpg doesn't have metadata in hf://datasets/nanonets/nn-auto-bench-ds@39f80b698af3d0554493b81a7063d577ecef6152/metadata.jsonl.

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nn-auto-bench-ds

nn-auto-bench-ds is a dataset designed for key information extraction (KIE) and serves as a benchmark dataset for nn-auto-bench.

Dataset Overview

The dataset comprises 1,000 documents, categorized into the following types:

Invoice
Receipt
Passport
Bank Statement

The documents are primarily available in English, with some also in German and Arabic. Each document is annotated for key information extraction and specific tasks. The dataset can be used to compute LLM's oneshot performance on KIE tasks.

Dataset Schema

The dataset includes the following columns:

image_path: File path to the document image.
content: OCR-extracted text from the image.
accepted: Ground truth answer.
Queried_labels: Labels, fields, or keys targeted for extraction.
Queried_col_headers: Column headers targeted for extraction.
ctx_1: OCR text from an example document.
ctx_1_image_path: File path to the example document’s image.
ctx_1_accepted: Ground truth answer for the example document.

There are total 54 unique fields/keys/labels that we want to extract from the documents.

Loading the Dataset

To load the dataset in Python using the datasets library:

from datasets import load_dataset

dataset = load_dataset("nanonets/nn-auto-bench-ds")

Data Sources

This dataset aggregates information from multiple open-source datasets, including:

This dataset is valuable for benchmarking key information extraction models and advancing research in document understanding and natural language processing (NLP).

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