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The dataset generation failed because of a cast error
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 ({'structured_data', 'ud_type', 'source', 'augmentation_status', 'current_hash', 'cell_annotations', 'unstructured_data'}).
This happened while the json dataset builder was generating data using
hf://datasets/ds4sd/SemTabNet/se_indirect_1d/train.jsonl (at revision cb298ccfe43b8d04f8cc16b34bc0955c0c59f44a)
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 "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
input: string
output: string
to
{'structured_data': [{'property': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'property_value': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'unit': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'subject': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'subject_value': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'predicate_hash': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}], 'source': {'data_hash': Value(dtype='string', id=None), 'document': {'languages': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'advanced': {'symbol': Value(dtype='string', id=None), 'ticker': Value(dtype='string', id=None), 'website': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'year': Value(dtype='int64', id=None), 'exchange': Value(dtype='string', id=None), 'industry': Value(dtype='string', id=None), 'location': Value(dtype='string', id=None), 'employees': Value(dtype='string', id=None), 'sector': Value(dtype='string', id=None)}, 'subjects': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'publication_date': Value(dtype='string', id=None), 'affiliations': [{'name': Value(dtype='string', id=None), 'id': Value(dtype='string', id=None), 'source': Value(dtype='string', id=None)}], 'collection': {'name': Value(dtype='string', id=None), 'alias': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'type': Value(dtype='string', id=None), 'version': Value(dtype='string', id=None)}, 'abstract': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'url_refs': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'title': Value(dtype='string', id=None), 'type': Value(dtype='string', id=None), 'logs': [{'date': Value(dtype='string', id=None), 'agent': Value(dtype='string', id=None), 'comment': Value(dtype='string', id=None), 'type': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None)}], 'document_hash': Value(dtype='string', id=None), 'index_tables': Value(dtype='int64', id=None)}, 'collection': Value(dtype='string', id=None), 'sub_collection': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'entities': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'ratio_numbers': Value(dtype='float64', id=None)}, 'cell_annotations': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'unstructured_data': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'ud_type': Value(dtype='string', id=None), 'augmentation_status': Value(dtype='string', id=None), 'current_hash': Value(dtype='string', id=None)}
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 1572, 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 1136, in stream_convert_to_parquet
builder._prepare_split(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, 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 ({'structured_data', 'ud_type', 'source', 'augmentation_status', 'current_hash', 'cell_annotations', 'unstructured_data'}).
This happened while the json dataset builder was generating data using
hf://datasets/ds4sd/SemTabNet/se_indirect_1d/train.jsonl (at revision cb298ccfe43b8d04f8cc16b34bc0955c0c59f44a)
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
sequence | unstructured_data
sequence | 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": [
"",
""
],
"subject_value": [
"",
""
],
"predicate_hash": [
"973cd38ed3b12954acfcc32d5b498eb8",
"973cd38ed3b12954acfcc32d5b498eb8"
]
},
{
"property": [
"Social: Our People & Our Communities : Corporate Charitable Contributions (excluding employee matching) ",
"time"
],
"property_value": [
"$3,187,997 ",
"FY22 Data "
],
"unit": [
"",
""
],
"subject": [
"",
""
],
"subject_value": [
"",
""
],
"predicate_hash": [
"f5b9486f76e3757c7bcf1460501b0727",
"f5b9486f76e3757c7bcf1460501b0727"
]
},
{
"property": [
"Social: Our People & Our Communities : Percentage of Employees Participating in Employee Network Groups ",
"time"
],
"property_value": [
"25.0% ",
"FY21 Data "
],
"unit": [
"",
""
],
"subject": [
"",
""
],
"subject_value": [
"",
""
],
"predicate_hash": [
"00c104a10278d2a3ae70f54bcb11f995",
"00c104a10278d2a3ae70f54bcb11f995"
]
},
{
"property": [
"Social: Our People & Our Communities : Percentage of Employees Participating in Employee Network Groups ",
"time"
],
"property_value": [
"27.6% ",
"FY22 Data "
],
"unit": [
"",
""
],
"subject": [
"",
""
],
"subject_value": [
"",
""
],
"predicate_hash": [
"e838e4c7fa261e4d3c0bf87644d12e58",
"e838e4c7fa261e4d3c0bf87644d12e58"
]
},
{
"property": [
"Social: Our People & Our Communities : Average Hours of Development per Employee ",
"time"
],
"property_value": [
"12 ",
"FY21 Data "
],
"unit": [
"",
""
],
"subject": [
"",
""
],
"subject_value": [
"",
""
],
"predicate_hash": [
"b896d2c8418de39aa827c7b0265cb852",
"b896d2c8418de39aa827c7b0265cb852"
]
},
{
"property": [
"Social: Our People & Our Communities : Average Hours of Development per Employee ",
"time"
],
"property_value": [
"16 ",
"FY22 Data "
],
"unit": [
"",
""
],
"subject": [
"",
""
],
"subject_value": [
"",
""
],
"predicate_hash": [
"6a4e1f98a986d62df3cce344b14d7298",
"6a4e1f98a986d62df3cce344b14d7298"
]
},
{
"property": [
"Social: Our People & Our Communities : Employee Volunteerism - Hours ",
"time"
],
"property_value": [
"3,363 ",
"FY21 Data "
],
"unit": [
"",
""
],
"subject": [
"",
""
],
"subject_value": [
"",
""
],
"predicate_hash": [
"7b30de0f69fa2e62b943fd01e2d175e6",
"7b30de0f69fa2e62b943fd01e2d175e6"
]
},
{
"property": [
"Social: Our People & Our Communities : Employee Volunteerism - Hours ",
"time"
],
"property_value": [
"8,034 ",
"FY22 Data "
],
"unit": [
"",
""
],
"subject": [
"",
""
],
"subject_value": [
"",
""
],
"predicate_hash": [
"c957247fade600e43e870ba15a40bb38",
"c957247fade600e43e870ba15a40bb38"
]
},
{
"property": [
"Social: Our People & Our Communities : Number and rate of fatalities as a result of work-related injury ",
"time"
],
"property_value": [
"0 ",
"FY21 Data "
],
"unit": [
"",
""
],
"subject": [
"",
""
],
"subject_value": [
"",
""
],
"predicate_hash": [
"79f3a9b36d533273fc736dfb5c866710",
"79f3a9b36d533273fc736dfb5c866710"
]
},
{
"property": [
"Social: Our People & Our Communities : Number and rate of fatalities as a result of work-related injury ",
"time"
],
"property_value": [
"0 ",
"FY22 Data "
],
"unit": [
"",
""
],
"subject": [
"",
""
],
"subject_value": [
"",
""
],
"predicate_hash": [
"f3b2384af927db05434dc80358dfb20e",
"f3b2384af927db05434dc80358dfb20e"
]
},
{
"property": [
"Social: Our People & Our Communities : High-consequent work-related injuries (excluding fatalities) ",
"time"
],
"property_value": [
"0 ",
"FY21 Data "
],
"unit": [
"",
""
],
"subject": [
"",
""
],
"subject_value": [
"",
""
],
"predicate_hash": [
"59a120044c70c2d30b39326eec4f4419",
"59a120044c70c2d30b39326eec4f4419"
]
},
{
"property": [
"Social: Our People & Our Communities : High-consequent work-related injuries (excluding fatalities) ",
"time"
],
"property_value": [
"0 ",
"FY22 Data "
],
"unit": [
"",
""
],
"subject": [
"",
""
],
"subject_value": [
"",
""
],
"predicate_hash": [
"eb3d52aec0a6b4c8e5dfb838be1ef6f2",
"eb3d52aec0a6b4c8e5dfb838be1ef6f2"
]
},
{
"property": [
"Social: Our People & Our Communities : Charitable Causes Supported ",
"time"
],
"property_value": [
"2,596 ",
"FY21 Data "
],
"unit": [
"",
""
],
"subject": [
"",
""
],
"subject_value": [
"",
""
],
"predicate_hash": [
"b9662c5d19c144338fe94fc2681e09df",
"b9662c5d19c144338fe94fc2681e09df"
]
},
{
"property": [
"Social: Our People & Our Communities : Charitable Causes Supported ",
"time"
],
"property_value": [
"2,872 ",
"FY22 Data "
],
"unit": [
"",
""
],
"subject": [
"",
""
],
"subject_value": [
"",
""
],
"predicate_hash": [
"5caf53489183c458be515cdacbf3658d",
"5caf53489183c458be515cdacbf3658d"
]
},
{
"property": [
"Social: Our People & Our Communities : Charitable Matching (of employee donations and MySMILE “Rewards”) ",
"time"
],
"property_value": [
"$1,856,702 ",
"FY21 Data "
],
"unit": [
"",
""
],
"subject": [
"",
""
],
"subject_value": [
"",
""
],
"predicate_hash": [
"2a933f086be5494bc20eec8a4749e7b3",
"2a933f086be5494bc20eec8a4749e7b3"
]
},
{
"property": [
"Social: Our People & Our Communities : Charitable Matching (of employee donations and MySMILE “Rewards”) ",
"time"
],
"property_value": [
"$1,091,221 ",
"FY22 Data "
],
"unit": [
"",
""
],
"subject": [
"",
""
],
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"",
""
],
"predicate_hash": [
"17af8c723554fb1f271e3dc65bc670d5",
"17af8c723554fb1f271e3dc65bc670d5"
]
},
{
"property": [
"Social: Our People & Our Communities : Overall Employee Participation (Matching & Volunteerism) ",
"time"
],
"property_value": [
"34.5% ",
"FY21 Data "
],
"unit": [
"",
""
],
"subject": [
"",
""
],
"subject_value": [
"",
""
],
"predicate_hash": [
"2e08e1d5aba3af667ef67b9c9910c5b9",
"2e08e1d5aba3af667ef67b9c9910c5b9"
]
},
{
"property": [
"Social: Our People & Our Communities : Overall Employee Participation (Matching & Volunteerism) ",
"time"
],
"property_value": [
"30.6% ",
"FY22 Data "
],
"unit": [
"",
""
],
"subject": [
"",
""
],
"subject_value": [
"",
""
],
"predicate_hash": [
"6fd54880e0b28bf1c5a517d9782bb3ce",
"6fd54880e0b28bf1c5a517d9782bb3ce"
]
},
{
"property": [
"Social: Our People & Our Communities : Cybersecurity Academy - Partners Enrolled ",
"time"
],
"property_value": [
"1,500 ",
"FY21 Data "
],
"unit": [
"",
""
],
"subject": [
"",
""
],
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""
],
"predicate_hash": [
"cc742d45b6bae4ae2eefb81d7deca0dd",
"cc742d45b6bae4ae2eefb81d7deca0dd"
]
},
{
"property": [
"Social: Our People & Our Communities : Cybersecurity Academy - Partners Enrolled ",
"time"
],
"property_value": [
"2,200 ",
"FY22 Data "
],
"unit": [
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""
],
"subject": [
"",
""
],
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""
],
"predicate_hash": [
"f72a4b7d490dec96b60f2806ba5cef59",
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]
},
{
"property": [
"Social: Our People & Our Communities : Recordable work-related injuries (i.e. repetitive strain/stress injury (5), slip/fall/contusion (1), etc.) ",
"time"
],
"property_value": [
"7 ",
"FY21 Data "
],
"unit": [
"",
""
],
"subject": [
"",
""
],
"subject_value": [
"",
""
],
"predicate_hash": [
"d2a8d9789cc79b5cba54971e4d3b41dd",
"d2a8d9789cc79b5cba54971e4d3b41dd"
]
},
{
"property": [
"Social: Our People & Our Communities : Recordable work-related injuries (i.e. repetitive strain/stress injury (5), slip/fall/contusion (1), etc.) ",
"time"
],
"property_value": [
"4 ",
"FY22 Data "
],
"unit": [
"",
""
],
"subject": [
"",
""
],
"subject_value": [
"",
""
],
"predicate_hash": [
"89ae21c37fe424a4bf5b7984ccc6c696",
"89ae21c37fe424a4bf5b7984ccc6c696"
]
},
{
"property": [
"Social: Our People & Our Communities : Employee Donations ",
"time"
],
"property_value": [
"$1,021,599 ",
"FY21 Data "
],
"unit": [
"",
""
],
"subject": [
"",
""
],
"subject_value": [
"",
""
],
"predicate_hash": [
"1f5663671323e68bc3ecdbf778e78e1b",
"1f5663671323e68bc3ecdbf778e78e1b"
]
},
{
"property": [
"Social: Our People & Our Communities : Employee Donations ",
"time"
],
"property_value": [
"$1,115,897 ",
"FY22 Data "
],
"unit": [
"",
""
],
"subject": [
"",
""
],
"subject_value": [
"",
""
],
"predicate_hash": [
"70f2d23d7751f5a2193dca7974e1ab06",
"70f2d23d7751f5a2193dca7974e1ab06"
]
},
{
"property": [
"Social: Our People & Our Communities : Cybersecurity Academy - Countries Reached ",
"time"
],
"property_value": [
"75 ",
"FY21 Data "
],
"unit": [
"",
""
],
"subject": [
"",
""
],
"subject_value": [
"",
""
],
"predicate_hash": [
"fb04c23de8e2bd19dadd20a4a5c8dab5",
"fb04c23de8e2bd19dadd20a4a5c8dab5"
]
},
{
"property": [
"Social: Our People & Our Communities : Cybersecurity Academy - Countries Reached ",
"time"
],
"property_value": [
"86 ",
"FY22 Data "
],
"unit": [
"",
""
],
"subject": [
"",
""
],
"subject_value": [
"",
""
],
"predicate_hash": [
"5d302918b415b55f07d36f3a5b6edcf3",
"5d302918b415b55f07d36f3a5b6edcf3"
]
}
] | {
"data_hash": "4c71232d4655229f0dcdf987b15c61b7",
"document": {
"languages": [
"en"
],
"advanced": {
"symbol": "NYSE: PANW",
"ticker": "PANW",
"website": [
"https://www.paloaltonetworks.com/"
],
"year": 2022,
"exchange": "NYSE",
"industry": "Networking & Communication Devices",
"location": "Santa Clara, California",
"employees": "1001-5000 Employees",
"sector": "Technology"
},
"subjects": [
"Technology"
],
"publication_date": "2022-01-01T12:00:00.000+00:00",
"affiliations": [
{
"name": "Palo Alto Networks Inc",
"id": "palo-alto",
"source": "ir-solutions"
}
],
"collection": {
"name": "ESG Reports",
"alias": [
"esg-report"
],
"type": "Document",
"version": "1.3.0"
},
"abstract": [
"Palo Alto Networks, Inc. provides enterprise security platform to enterprises, service providers, and government entities worldwide. Its platform includes Next-Generation Firewall that delivers application, user, and content visibility and control, as well as protection against network-based cyber threats; and Threat Intelligence Cloud that offers central intelligence capabilities, as wellas automated delivery of preventative measures against cyber attacks."
],
"url_refs": [
"https://www.responsibilityreports.com/HostedData/ResponsibilityReports/PDF/NYSE_PANW_2022.pdf"
],
"title": "2022 Environmental, Social and Governance Report",
"type": "ESG report",
"logs": [
{
"date": "2023-08-17T07:02:01.636047+00:00",
"agent": "CCS",
"comment": "CCS v9.1.1 parsing of documents",
"type": "parsing",
"task": null
},
{
"date": "2023-08-21T11:26:37.184+00:00",
"agent": "CXS",
"comment": "enrich esg-report-metadata-20230628 to esg-report-20230815",
"type": "text enrichment",
"task": "enrich esg-report-metadata-20230628 to esg-report-20230815"
}
],
"document_hash": "1edb91af84c6c4c8fce5d2c69f201b2304d01a7a99b6f12b374b48f5af9d0091",
"index_tables": 5
},
"collection": "esg",
"sub_collection": [
"social"
],
"entities": [
"training hours per employee",
"number of other fatalities"
],
"ratio_numbers": 1.1428571428571428
} | [
[
"header_1",
"time_value",
"time_value"
],
[
"property",
"property_value",
"property_value"
],
[
"property",
"property_value",
"property_value"
],
[
"property",
"property_value",
"property_value"
],
[
"property",
"property_value",
"property_value"
],
[
"property",
"property_value",
"property_value"
],
[
"property",
"property_value",
"property_value"
],
[
"property",
"property_value",
"property_value"
],
[
"property",
"property_value",
"property_value"
],
[
"property",
"property_value",
"property_value"
],
[
"property",
"property_value",
"property_value"
],
[
"property",
"property_value",
"property_value"
],
[
"property",
"property_value",
"property_value"
],
[
"property",
"property_value",
"property_value"
]
] | [
[
"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% "
],
[
"Average Hours of Development per Employee ",
"12 ",
"16 "
],
[
"Employee Volunteerism - Hours ",
"3,363 ",
"8,034 "
],
[
"Number and rate of fatalities as a result of work-related injury ",
"0 ",
"0 "
],
[
"High-consequent work-related injuries (excluding fatalities) ",
"0 ",
"0 "
],
[
"Charitable Causes Supported ",
"2,596 ",
"2,872 "
],
[
"Charitable Matching (of employee donations and MySMILE “Rewards”) ",
"$1,856,702 ",
"$1,091,221 "
],
[
"Overall Employee Participation (Matching & Volunteerism) ",
"34.5% ",
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[
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[
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] | table | augmented | fa5d002e9f5147a76ff522c22c6095ae |
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"Emissions intensity tCO2-e/ROMt ",
"0.038 ",
"0.043 ",
"0.051 ",
"0.040 ",
"0.040 "
]
] | table | augmented | 50bce7f8897edc85980d3bac0772d7bd |
End of preview.
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
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