Datasets:

docling-project
/

SemTabNet

Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 2 new columns ({'input', 'output'}) and 7 missing columns ({'current_hash', 'unstructured_data', 'augmentation_status', 'cell_annotations', 'structured_data', 'ud_type', 'source'}).

This happened while the json dataset builder was generating data using

hf://datasets/docling-project/SemTabNet/se_indirect_1d/train.jsonl (at revision fc268093b21a69f4a04cb45bee67b012a56de66a)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 714, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              input: string
              output: string
              to
              {'structured_data': List({'property': List(Value('string')), 'property_value': List(Value('string')), 'unit': List(Value('string')), 'subject': List(Value('string')), 'subject_value': List(Value('string')), 'predicate_hash': List(Value('string'))}), 'source': {'data_hash': Value('string'), 'document': {'languages': List(Value('string')), 'advanced': {'symbol': Value('string'), 'ticker': Value('string'), 'website': List(Value('string')), 'year': Value('int64'), 'exchange': Value('string'), 'industry': Value('string'), 'location': Value('string'), 'employees': Value('string'), 'sector': Value('string')}, 'subjects': List(Value('string')), 'publication_date': Value('string'), 'affiliations': List({'name': Value('string'), 'id': Value('string'), 'source': Value('string')}), 'collection': {'name': Value('string'), 'alias': List(Value('string')), 'type': Value('string'), 'version': Value('string')}, 'abstract': List(Value('string')), 'url_refs': List(Value('string')), 'title': Value('string'), 'type': Value('string'), 'logs': List({'date': Value('string'), 'agent': Value('string'), 'comment': Value('string'), 'type': Value('string'), 'task': Value('string')}), 'document_hash': Value('string'), 'index_tables': Value('int64')}, 'collection': Value('string'), 'sub_collection': List(Value('string')), 'entities': List(Value('string')), 'ratio_numbers': Value('float64')}, 'cell_annotations': List(List(Value('string'))), 'unstructured_data': List(List(Value('string'))), 'ud_type': Value('string'), 'augmentation_status': Value('string'), 'current_hash': Value('string')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1450, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 993, in stream_convert_to_parquet
                  builder._prepare_split(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1702, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1833, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 2 new columns ({'input', 'output'}) and 7 missing columns ({'current_hash', 'unstructured_data', 'augmentation_status', 'cell_annotations', 'structured_data', 'ud_type', 'source'}).
              
              This happened while the json dataset builder was generating data using
              
              hf://datasets/docling-project/SemTabNet/se_indirect_1d/train.jsonl (at revision fc268093b21a69f4a04cb45bee67b012a56de66a)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

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.

structured_data list	source dict	cell_annotations list	unstructured_data list	ud_type string	augmentation_status string	current_hash string
[ { "property": [ "Social: Our People & Our Communities : Corporate Charitable Contributions (excluding employee matching) ", "time" ], "property_value": [ "$2,616,880 ", "FY21 Data " ], "unit": [ "", "" ], "subject": [ "", "" ], "sub...	{ "document": { "languages": [ "en" ], "advanced": { "symbol": "NYSE: PANW", "ticker": "PANW", "website": [ "https://www.paloaltonetworks.com/" ], "year": 2022, "exchange": "NYSE", "industry": "Networking & Communication Devices", "location": "...	[ [ "header_1", "time_value", "time_value" ], [ "property", "property_value", "property_value" ], [ "property", "property_value", "property_value" ], [ "property", "property_value", "property_value" ], [ "property", "property_value", "propert...	[ [ "Social: Our People & Our Communities ", "FY21 Data ", "FY22 Data " ], [ "Corporate Charitable Contributions (excluding employee matching) ", "$2,616,880 ", "$3,187,997 " ], [ "Percentage of Employees Participating in Employee Network Groups ", "25.0% ", "27.6% " ], ...	table	augmented	fa5d002e9f5147a76ff522c22c6095ae
[ { "property": [ "Number of WeSustain teams (global) " ], "property_value": [ "Not reported " ], "unit": [ "" ], "subject": [ "" ], "subject_value": [ "" ], "predicate_hash": [ "af4726127c37d038e8ef828731095cf7" ] }, { "prope...	{ "document": { "languages": [ "en" ], "advanced": { "symbol": "NYSE: JNJ", "ticker": "JNJ", "website": [ "http://www.jnj.com/" ], "year": 2021, "exchange": "NYSE", "industry": "Drug Manufacturers - Major", "location": "New Brunswick, New Jerse...	[ [ "property", "property_value", "property_value", "property_value" ], [ "property", "property_value", "property_value", "property_value" ], [ "property", "property_value", "property_value", "property_value" ], [ "property", "property_value", "p...	[ [ "Number of WeSustain teams (global) ", "Not reported ", "78 ", "Not reported " ], [ "Number of actions taken in 2021 via the HealthyPlanet platform ", "Not reported ", "24,609 ", "Not reported " ], [ "Number of countries with WeSustain teams ", "Not reported ", ...	table	augmented	24acc81f66f6e0503545ca51ba4d676a
[ { "property": [ "GHG EMISSIONS SOURCE : Scope 2 Emissions (Market-Based) ", "time" ], "property_value": [ "1,544 ", "2021 GHG EMISSIONS (MTCO2 e ) " ], "unit": [ "", "" ], "subject": [ "", "" ], "subject_value": [ "", ...	{ "document": { "languages": [ "en" ], "advanced": { "symbol": "NASDAQ: KFRC", "ticker": "KFRC", "website": [ "http://www.kforce.com" ], "year": 2022, "exchange": "NASDAQ", "industry": "Staffing & Outsourcing Services", "location": "Tampa, Flor...	[ [ "header_1", "time_value", "time_value", "time_value" ], [ "property", "property_value", "property_value", "property_value" ], [ "property", "property_value", "property_value", "property_value" ], [ "property", "property_value", "property_valu...	[ [ "GHG EMISSIONS SOURCE ", "2021 GHG EMISSIONS (MTCO2 e ) ", "2019 GHG EMISSIONS (MTCO2 e ) ", "2020 GHG EMISSIONS (MTCO2 e ) " ], [ "Scope 2 Emissions (Market-Based) ", "1,544 ", "2,236 ", "1,815 " ], [ "Total Scope 1 & Scope 2 Emissions (Market-Based) ", ...	table	augmented	d1037cb8375977fb182a3d2168209c79
[ { "property": [ "INDICATORS : Number and percentage of workers covered by an occupational health and safety management system ", "time" ], "property_value": [ "100% ", "2020 " ], "unit": [ "", "" ], "subject": [ "", "" ], "subject_...	{ "document": { "languages": [ "en" ], "advanced": { "symbol": "ASX: YAL", "ticker": "YAL", "website": [ "https://www.yancoal.com.au/" ], "year": 2022, "exchange": "ASX", "industry": "Thermal Coal", "location": "Sydney, Australia", "emplo...	[ [ "header_1", "time_value", "time_value", "time_value", "time_value", "time_value" ], [ "property", "property_value", "property_value", "property_value", "property_value", "property_value" ], [ "property", "property_value", "property_value", "p...	[ [ "INDICATORS ", "2020 ", "2019 ", "2022 ", "2018 ", "2021 " ], [ "Number and percentage of workers covered by an occupational health and safety management system ", "100% ", "100% ", "100% ", "100% ", "100% " ], [ "Number of hours worked ", " 8,200,7...	table	augmented	b59417f6d3c97da6cd60e12acccb1ea3
[ { "property": [ "Metrics : Total number of people reached (cumulative, FY20 to current reporting FY) ", "Notes ", "time" ], "property_value": [ "46,588,226 ", "Our “1 Billion Lives” goal tracks the number of people who are reached through our health and education initiativ...	{ "document": { "languages": [ "en" ], "advanced": { "symbol": "NYSE: DELL", "ticker": "DELL", "website": [ "https://www.dell.com/en-us" ], "year": 2022, "exchange": "NYSE", "industry": "Information Technology Services", "location": "Round Rock...	[ [ "header_1", "time_value", "time_value", "time_value", "key" ], [ "property", "property_value", "property_value", "property_value", "key_value" ], [ "property", "property_value", "property_value", "property_value", "empty" ], [ "property",...	[ [ "Metrics ", "FY20 ", "FY21 ", "FY22 ", "Notes " ], [ "Total number of people reached (cumulative, FY20 to current reporting FY) ", "46,588,226 ", "93,565,402 ", "159,742,242 ", "Our “1 Billion Lives” goal tracks the number of people who are reached through our health a...	table	augmented	e31cfcb5d2e59396f41424d704564886
[ { "property": [ "Emissions Intensity : Emissions Intensity (MT CO2- e / USD Revenue) ", "time" ], "property_value": [ "0.0003 ", "2021 " ], "unit": [ "", "" ], "subject": [ "", "" ], "subject_value": [ "", "" ], ...	{ "document": { "languages": [ "en" ], "advanced": { "symbol": "NYSE: GRA", "ticker": "GRA", "website": [ "http://www.grace.com/" ], "year": 2022, "exchange": "NYSE", "industry": "Specialty Chemicals", "location": "Columbia, Maryland", "e...	[ [ "header_1", "time_value", "time_value", "time_value" ], [ "property", "property_value", "property_value", "property_value" ], [ "property", "property_value", "property_value", "property_value" ], [ "property", "property_value", "property_valu...	[ [ "Emissions Intensity ", "2021 ", "2020 ", "2019 " ], [ "Emissions Intensity (MT CO2- e / USD Revenue) ", "0.0003 ", "0.0003 ", "0.0003 " ], [ "Total Revenue (including ART Joint Venture, USD) ", "2,512,600,000 ", "2,211,900,000 ", "2,468,500,000 "...	table	augmented	3c16678348a858366676387937f237d8
[ { "property": [ "Scope 1 (direct emissions), including: ", "time" ], "property_value": [ "682,645 ", "2021 " ], "unit": [ "t of CO$_{2}$e ", "" ], "subject": [ "", "" ], "subject_value": [ "", "" ], "predicate_ha...	{ "document": { "languages": [ "en" ], "advanced": { "symbol": "ASX: POL", "ticker": "POL", "website": [ "https://polymetals.com/" ], "year": 2022, "exchange": "ASX", "industry": "Gold", "location": "Sydney, Australia", "employees": "11-5...	[ [ "empty", "unit_property", "time_value", "time_value", "time_value", "time_value" ], [ "property", "unit_value", "property_value", "property_value", "property_value", "property_value" ], [ "property", "unit_value", "property_value", "property_...	[ [ "", "Units ", "2021 ", "2020 ", "2022 ", "2019 " ], [ "Scope 1 (direct emissions), including: ", "t of CO$_{2}$e ", "682,645 ", "612,669 ", "751,486 ", "613,717 " ], [ "Combustion of fuels in stationary sources, including: ", "t of CO$_{2}$e ", "...	table	augmented	0dc9b1b27eaa9047af395184a2285ead
[ { "property": [ "Training and Development : Number of external people who received Tarkett Academy training ", "GRI ", "time" ], "property_value": [ "8,148 ", "203-2 ", "2021 " ], "unit": [ "", "", "" ], "subject": [ "", "...	{ "document": { "languages": [ "en" ], "advanced": { "symbol": "OTC: TKFTF", "ticker": "TKFTF", "website": [ "http://www.tarkett-group.com/" ], "year": 2022, "exchange": "OTC", "industry": "Building Materials Wholesale", "location": "Paris, Fra...	[ [ "key", "header_1", "time_value", "time_value", "time_value", "time_value", "time_value", "time_value" ], [ "empty", "header_1", "header_1", "header_1", "header_1", "header_1", "header_1", "header_1" ], [ "key_value", "property", "...	[ [ "GRI ", "Indicator ", "2021 ", "2019 ", "2020 ", "Variation 2022 vs. base year ", "2022 ", "Variation 2022 vs. 2021 " ], [ "", "Training and Development ", "Training and Development ", "Training and Development ", "Training and Development ", "Trainin...	table	augmented	b26ba0ecf0846ec853d03b848ce465e3
[ { "property": [ "Average training hours per year/employee ", "category" ], "property_value": [ "23.5 ", "Male " ], "unit": [ "", "" ], "subject": [ "", "" ], "subject_value": [ "", "" ], "predicate_hash": [ ...	{ "document": { "languages": [ "en" ], "advanced": { "symbol": "NYSE: CSTM", "ticker": "CSTM", "website": [ "https://www.constellium.com/" ], "year": 2022, "exchange": "NYSE", "industry": "Aluminum", "location": "Paris, France", "employee...	[ [ "empty", "key_value", "key_value", "key_value", "key_value", "key_value" ], [ "property", "property_value", "property_value", "property_value", "property_value", "property_value" ] ]	[ [ "", "Male ", "All employees ", "Managers 1 ", "Operators 2 ", "Female " ], [ "Average training hours per year/employee ", "23.5 ", "23.8 ", "23.1 ", "24.0 ", "26.1 " ] ]	table	augmented	586ddd875ba42ee5164ac53e9afb6018
[ { "property": [ "Greenhouse Gas (“GHG”) Emissions Summary (tCO2e) : GHG Emissions by Scope : Scope 3 ", "time" ], "property_value": [ "438,731 ", "2020 " ], "unit": [ "", "" ], "subject": [ "", "" ], "subject_value": [ "", ...	{ "document": { "languages": [ "en" ], "advanced": { "symbol": "NASDAQ: ETSY", "ticker": "ETSY", "website": [ "https://www.etsy.com/" ], "year": 2022, "exchange": "NASDAQ", "industry": "Specialty Retail, Other", "location": "rooklyn, New York",...	[ [ "header_1", "rubbish", "rubbish", "rubbish" ], [ "header_2", "time_value", "time_value", "time_value" ], [ "property", "property_value", "property_value", "property_value" ], [ "property", "empty", "property_value", "empty" ], [ "...	[ [ "Greenhouse Gas (“GHG”) Emissions Summary (tCO2e) ", "Etsy.com + Reverb ", "Etsy.com, Reverb, Depop ", "Etsy.com + Reverb " ], [ "GHG Emissions by Scope ", "2020 ", "2022 ", "2021 " ], [ "Scope 3 ", "438,731 ", "531,638 ", "548,900 " ], [ "Scope ...	table	augmented	55f0aacb0f325abcdf59091c016d8070
[ { "property": [ "GREENHOUSE GAS EMISSIONS : CO$_{2}$ emissions : Gross global Scope 2 emissions location-based ", "SASB code ", "time" ], "property_value": [ "20 ", "Additional ", "Data 2022 " ], "unit": [ "Metric tons CO$_{2}$-e ", "", ...	{ "document": { "languages": [ "en" ], "advanced": { "symbol": "OTC: PRSEY", "ticker": "PRSEY", "website": [ "https://www.prosafe.com/" ], "year": 2022, "exchange": "OTC", "industry": "Oil & Gas Equipment & Services", "location": "Stavanger, No...	[ [ "header_1", "unit_property", "time_value", "time_value", "key" ], [ "header_1", "header_1", "header_1", "header_1", "header_1" ], [ "header_2", "header_2", "header_2", "header_2", "header_2" ], [ "property", "unit_value", "propert...	[ [ "Accounting metric ", "Unit of measure ", "Data 2022 ", "Data 2021 ", "SASB code " ], [ "GREENHOUSE GAS EMISSIONS ", "GREENHOUSE GAS EMISSIONS ", "GREENHOUSE GAS EMISSIONS ", "GREENHOUSE GAS EMISSIONS ", "GREENHOUSE GAS EMISSIONS " ], [ "CO$_{2}$ emissi...	table	augmented	23ab652a70c23882b9777061884f2114
[ { "property": [ "INDICATORS : Total Emissions tCO2-e ", "time" ], "property_value": [ "1,983,298 ", "2019 " ], "unit": [ "", "" ], "subject": [ "", "" ], "subject_value": [ "", "" ], "predicate_hash": [ "7e...	{ "document": { "languages": [ "en" ], "advanced": { "symbol": "ASX: YAL", "ticker": "YAL", "website": [ "https://www.yancoal.com.au/" ], "year": 2022, "exchange": "ASX", "industry": "Thermal Coal", "location": "Sydney, Australia", "emplo...	[ [ "header_1", "time_value", "time_value", "time_value", "time_value", "time_value" ], [ "property", "property_value", "property_value", "property_value", "property_value", "property_value" ], [ "property", "property_value", "property_value", "p...	[ [ "INDICATORS ", "2019 ", "2018 ", "2022 ", "2021 ", "2020 " ], [ "Total Emissions tCO2-e ", "1,983,298 ", "2,114,527 ", "2,367,913 ", "2,213,876 ", "2,042,183 " ], [ "Total ROM production ", "51,574,833 ", "49,455,204 ", "46,507,466 ", "...	table	augmented	50bce7f8897edc85980d3bac0772d7bd

End of preview.

YAML Metadata Warning:The task_categories "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Dataset Card for SemTabNet

This dataset accompanies the following paper:

Title: Statements: Universal Information Extraction from Tables with Large Language Models for ESG KPIs
Authors: Lokesh Mishra, Sohayl Dhibi, Yusik Kim, Cesar Berrospi Ramis, Shubham Gupta, Michele Dolfi, Peter Staar
Venue: Accepted at the NLP4Climate workshop in the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024)

In this paper, we propose STATEMENTS as a new knowledge model for storing quantiative information in a domain agnotic, uniform structure. The task of converting a raw input (table or text) to Statements is called Statement Extraction (SE). The statement extraction task falls under the category of universal information extraction.

Code Repository: SemTabNet repository
Arxiv Paper: Statements: Universal Information Extraction from Tables with Large Language Models for ESG KPIs
Point of Contact: IBM Research DeepSearch Team

Data Splits

There are three tasks supported by this dataset. The data for each three task is split in training, validation, and testing set. Additionally, we also provide the original annotations of the raw tables which are used to construct all other data.

Task	Train	Test	Valid
SE Direct	103455	11682	5445
SE Indirect 1D	72580	8489	3821
SE Indirect 2D	93153	22839	4903

Languages

The text in the dataset is in English.

Source and Annotations

The source of this dataset and the annotation strategy is described in the paper.

Citation Information

Arxiv: https://arxiv.org/abs/2406.19102

Downloads last month: 59

Paper for docling-project/SemTabNet

Statements: Universal Information Extraction from Tables with Large Language Models for ESG KPIs

Paper • 2406.19102 • Published Jun 27, 2024 • 1