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Mirror: A Universal Framework for Various Information Extraction Tasks
https://arxiv.org/abs/2311.05419 • 5 items • Updated • 1
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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 1 new columns ({'instruction'})
This happened while the json dataset builder was generating data using
hf://datasets/Spico/Mirror_woACE/complex_tasks/HyperRED/train.jsonl (at revision c0eea6d43a0eb6aab9f492a0b7aafc45b023fe9b)
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 1869, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 580, 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 2292, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2240, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
id: string
instruction: string
schema: struct<hyper_rel: struct<notable work: list<item: string>, child: list<item: string>, shares border with: list<item: string>, winner: list<item: string>, award received: list<item: string>, part of: list<item: string>, subclass of: list<item: string>, nominated for: list<item: string>, significant event: list<item: string>, instance of: list<item: string>, parent organization: list<item: string>, head of government: list<item: string>, position held: list<item: string>, director / manager: list<item: string>, spouse: list<item: string>, capital of: list<item: string>, country: list<item: string>, place of birth: list<item: string>, located in the administrative territorial entity: list<item: string>, occupant: list<item: string>, operator: list<item: string>, owned by: list<item: string>, home venue: list<item: string>, headquarters location: list<item: string>, residence: list<item: string>, part of the series: list<item: string>, replaces: list<item: string>, original broadcaster: list<item: string>, country of citizenship: list<item: string>, educated at: list<item: string>, member of: list<item: string>, member of sports team: list<item: string>, coach of sports team: list<item: string>, performer: list<item: string>, cast member: list<item: string>, head of state: list<item: string>, employer: list<item: string>, member of political party: list<item: string>, present in work: list<item: string>, followed by: list<item: string>, adja
...
g, head: struct<text: string, span: list<item: int64>>, tail: struct<text: string, span: list<item: int64>>, qualifiers: list<item: struct<text: string, span: list<item: int64>, label: string>>>>>
child 0, hyper_rel: list<item: struct<relation: string, head: struct<text: string, span: list<item: int64>>, tail: struct<text: string, span: list<item: int64>>, qualifiers: list<item: struct<text: string, span: list<item: int64>, label: string>>>>
child 0, item: struct<relation: string, head: struct<text: string, span: list<item: int64>>, tail: struct<text: string, span: list<item: int64>>, qualifiers: list<item: struct<text: string, span: list<item: int64>, label: string>>>
child 0, relation: string
child 1, head: struct<text: string, span: list<item: int64>>
child 0, text: string
child 1, span: list<item: int64>
child 0, item: int64
child 2, tail: struct<text: string, span: list<item: int64>>
child 0, text: string
child 1, span: list<item: int64>
child 0, item: int64
child 3, qualifiers: list<item: struct<text: string, span: list<item: int64>, label: string>>
child 0, item: struct<text: string, span: list<item: int64>, label: string>
child 0, text: string
child 1, span: list<item: int64>
child 0, item: int64
child 2, label: string
text: string
bg: string
to
{'id': Value(dtype='string', id=None), 'schema': {'hyper_rel': {'notable work': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'child': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'shares border with': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'winner': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'award received': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'part of': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'subclass of': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'nominated for': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'significant event': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'instance of': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'parent organization': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'head of government': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'position held': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'director / manager': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'spouse': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'capital of': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'country': Sequence(feature=Value(dtype='string', id=N
...
one), length=-1, id=None), 'voice actor': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'connecting line': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'military branch': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'legislative body': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'manufacturer': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'stock exchange': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'sport': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'incarnation of': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'sports season of league or competition': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'narrative role': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}}, 'ans': {'hyper_rel': [{'relation': Value(dtype='string', id=None), 'head': {'text': Value(dtype='string', id=None), 'span': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)}, 'tail': {'text': Value(dtype='string', id=None), 'span': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)}, 'qualifiers': [{'text': Value(dtype='string', id=None), 'span': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'label': Value(dtype='string', id=None)}]}]}, 'text': Value(dtype='string', id=None), 'bg': 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 1392, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1041, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 924, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 999, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1740, 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 1871, 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 1 new columns ({'instruction'})
This happened while the json dataset builder was generating data using
hf://datasets/Spico/Mirror_woACE/complex_tasks/HyperRED/train.jsonl (at revision c0eea6d43a0eb6aab9f492a0b7aafc45b023fe9b)
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.
id string | schema dict | ans dict | text string | bg string |
|---|---|---|---|---|
HyperRED.train.0 | {
"hyper_rel": {
"notable work": [
"publication date",
"point in time",
"together with",
"subject has role",
"end time",
"start time",
"applies to part",
"character role",
"object has role",
"follows"
],
"child": [
"mother",
"series o... | {
"hyper_rel": [
{
"relation": "notable work",
"head": {
"text": "She",
"span": [
0,
3
]
},
"tail": {
"text": "The Fountainhead",
"span": [
49,
65
]
},
"qualifiers": [
{
"t... | She is known for her two best - selling novels , The Fountainhead ( 1943 ) and Atlas Shrugged ( 1957 ) , and for developing a philosophical system she called Objectivism . | |
HyperRED.train.1 | {
"hyper_rel": {
"notable work": [
"publication date",
"point in time",
"together with",
"subject has role",
"end time",
"start time",
"applies to part",
"character role",
"object has role",
"follows"
],
"child": [
"mother",
"series o... | {
"hyper_rel": [
{
"relation": "child",
"head": {
"text": "Zeus",
"span": [
21,
25
]
},
"tail": {
"text": "Artemis",
"span": [
81,
88
]
},
"qualifiers": [
{
"text": "Leto",... | Apollo is the son of Zeus and Leto , and has a twin sister , the chaste huntress Artemis . | |
HyperRED.train.2 | {
"hyper_rel": {
"notable work": [
"publication date",
"point in time",
"together with",
"subject has role",
"end time",
"start time",
"applies to part",
"character role",
"object has role",
"follows"
],
"child": [
"mother",
"series o... | {
"hyper_rel": [
{
"relation": "shares border with",
"head": {
"text": "Saskatchewan",
"span": [
26,
38
]
},
"tail": {
"text": "Northwest Territories",
"span": [
61,
82
]
},
"qualifiers": [
... | Alberta and its neighbour Saskatchewan were districts of the Northwest Territories until they were established as provinces on September 1 , 1905 . | |
HyperRED.train.3 | {
"hyper_rel": {
"notable work": [
"publication date",
"point in time",
"together with",
"subject has role",
"end time",
"start time",
"applies to part",
"character role",
"object has role",
"follows"
],
"child": [
"mother",
"series o... | {
"hyper_rel": [
{
"relation": "winner",
"head": {
"text": "Nobel Prize",
"span": [
21,
32
]
},
"tail": {
"text": "He",
"span": [
0,
2
]
},
"qualifiers": [
{
"text": "1921"... | He received the 1921 Nobel Prize in Physics for his " services to theoretical physics " , in particular his discovery of the law of the photoelectric effect , a pivotal step in the evolution of quantum theory . | |
HyperRED.train.4 | {
"hyper_rel": {
"notable work": [
"publication date",
"point in time",
"together with",
"subject has role",
"end time",
"start time",
"applies to part",
"character role",
"object has role",
"follows"
],
"child": [
"mother",
"series o... | {
"hyper_rel": [
{
"relation": "part of",
"head": {
"text": "francium",
"span": [
189,
197
]
},
"tail": {
"text": "alkali metals",
"span": [
4,
17
]
},
"qualifiers": [
{
"t... | The alkali metals are a group ( column ) in the periodic table consisting of the chemical elements lithium ( Li ) , sodium ( Na ) , potassium ( K ) , rubidium ( Rb ) , caesium ( Cs ) , and francium ( Fr ) . | |
HyperRED.train.5 | {
"hyper_rel": {
"notable work": [
"publication date",
"point in time",
"together with",
"subject has role",
"end time",
"start time",
"applies to part",
"character role",
"object has role",
"follows"
],
"child": [
"mother",
"series o... | {
"hyper_rel": [
{
"relation": "part of",
"head": {
"text": "francium",
"span": [
175,
183
]
},
"tail": {
"text": "alkali metals",
"span": [
22,
35
]
},
"qualifiers": [
{
"... | All of the discovered alkali metals occur in nature : in order of abundance , sodium is the most abundant , followed by potassium , lithium , rubidium , caesium , and finally francium , which is very rare due to its extremely high radioactivity ; francium occurs only in traces , the product of natural decay chains . | |
HyperRED.train.6 | {
"hyper_rel": {
"notable work": [
"publication date",
"point in time",
"together with",
"subject has role",
"end time",
"start time",
"applies to part",
"character role",
"object has role",
"follows"
],
"child": [
"mother",
"series o... | {
"hyper_rel": [
{
"relation": "part of",
"head": {
"text": "Alpha",
"span": [
0,
5
]
},
"tail": {
"text": "Greek",
"span": [
36,
41
]
},
"qualifiers": [
{
"text": "first",... | Alpha ( uppercase Α , lowercase α ; Greek : Άλφα Álpha ) is the first letter of the Greek alphabet . | |
HyperRED.train.7 | {
"hyper_rel": {
"notable work": [
"publication date",
"point in time",
"together with",
"subject has role",
"end time",
"start time",
"applies to part",
"character role",
"object has role",
"follows"
],
"child": [
"mother",
"series o... | {
"hyper_rel": [
{
"relation": "award received",
"head": {
"text": "He",
"span": [
0,
2
]
},
"tail": {
"text": "Nobel Prize",
"span": [
11,
22
]
},
"qualifiers": [
{
"text"... | He won the Nobel Prize in Literature in 1957 . | |
HyperRED.train.8 | {
"hyper_rel": {
"notable work": [
"publication date",
"point in time",
"together with",
"subject has role",
"end time",
"start time",
"applies to part",
"character role",
"object has role",
"follows"
],
"child": [
"mother",
"series o... | {
"hyper_rel": [
{
"relation": "nominated for",
"head": {
"text": "Ginsberg",
"span": [
0,
8
]
},
"tail": {
"text": "Pulitzer Prize",
"span": [
15,
29
]
},
"qualifiers": [
{
... | Ginsberg was a Pulitzer Prize finalist in 1995 for his book Cosmopolitan Greetings : Poems 1986 – 1992 . | |
HyperRED.train.9 | {
"hyper_rel": {
"notable work": [
"publication date",
"point in time",
"together with",
"subject has role",
"end time",
"start time",
"applies to part",
"character role",
"object has role",
"follows"
],
"child": [
"mother",
"series o... | {
"hyper_rel": [
{
"relation": "winner",
"head": {
"text": "Nobel Peace Prize",
"span": [
21,
38
]
},
"tail": {
"text": "He",
"span": [
0,
2
]
},
"qualifiers": [
{
"text": ... | He received the 1952 Nobel Peace Prize for his philosophy of " Reverence for Life " , expressed in many ways , but most famously in founding and sustaining the Albert Schweitzer Hospital in Lambaréné , now in Gabon , west central Africa ( then French Equatorial Africa ) . | |
HyperRED.train.10 | {
"hyper_rel": {
"notable work": [
"publication date",
"point in time",
"together with",
"subject has role",
"end time",
"start time",
"applies to part",
"character role",
"object has role",
"follows"
],
"child": [
"mother",
"series o... | {
"hyper_rel": [
{
"relation": "significant event",
"head": {
"text": "It",
"span": [
0,
2
]
},
"tail": {
"text": "Agincourt",
"span": [
46,
55
]
},
"qualifiers": [
{
"text... | It is best known as the site of the Battle of Agincourt in 1415 . | |
HyperRED.train.11 | {
"hyper_rel": {
"notable work": [
"publication date",
"point in time",
"together with",
"subject has role",
"end time",
"start time",
"applies to part",
"character role",
"object has role",
"follows"
],
"child": [
"mother",
"series o... | {
"hyper_rel": [
{
"relation": "subclass of",
"head": {
"text": "Argon",
"span": [
0,
5
]
},
"tail": {
"text": "noble gas",
"span": [
413,
422
]
},
"qualifiers": [
{
"text"... | Argon is the third most common gas in the Earth ' s atmosphere , at 0 . 934 % ( 9340 ppmv ) , making it over twice as abundant as the next most common atmospheric gas , water vapor ( which averages about 4000 ppmv , but varies greatly ) , and 23 times as abundant as the next most common non - condensing atmospheric gas... | |
HyperRED.train.12 | {
"hyper_rel": {
"notable work": [
"publication date",
"point in time",
"together with",
"subject has role",
"end time",
"start time",
"applies to part",
"character role",
"object has role",
"follows"
],
"child": [
"mother",
"series o... | {
"hyper_rel": [
{
"relation": "subclass of",
"head": {
"text": "argon - 36",
"span": [
18,
28
]
},
"tail": {
"text": "isotope",
"span": [
61,
68
]
},
"qualifiers": [
{
"te... | In the universe , argon - 36 is by far the most common argon isotope , being the preferred argon isotope produced by stellar nucleosynthesis in supernovas . | |
HyperRED.train.13 | {
"hyper_rel": {
"notable work": [
"publication date",
"point in time",
"together with",
"subject has role",
"end time",
"start time",
"applies to part",
"character role",
"object has role",
"follows"
],
"child": [
"mother",
"series o... | {
"hyper_rel": [
{
"relation": "subclass of",
"head": {
"text": "chlorine",
"span": [
177,
185
]
},
"tail": {
"text": "halogens",
"span": [
123,
131
]
},
"qualifiers": [
{
... | Many of these have been estimated based on its periodic table position as a heavier analog of iodine , and a member of the halogens – the group of elements including fluorine , chlorine and bromine . | |
HyperRED.train.14 | {
"hyper_rel": {
"notable work": [
"publication date",
"point in time",
"together with",
"subject has role",
"end time",
"start time",
"applies to part",
"character role",
"object has role",
"follows"
],
"child": [
"mother",
"series o... | {
"hyper_rel": [
{
"relation": "instance of",
"head": {
"text": "Arachnophobia",
"span": [
0,
13
]
},
"tail": {
"text": "phóbos",
"span": [
98,
104
]
},
"qualifiers": [
{
"... | Arachnophobia or arachnephobia ( from Greek ἀράχνη ( aráchnē ) , meaning " spider " , and φόβος ( phóbos ) , meaning " fear " ) is a specific phobia , the fear of spiders and other arachnids such as scorpions . | |
HyperRED.train.15 | {
"hyper_rel": {
"notable work": [
"publication date",
"point in time",
"together with",
"subject has role",
"end time",
"start time",
"applies to part",
"character role",
"object has role",
"follows"
],
"child": [
"mother",
"series o... | {
"hyper_rel": [
{
"relation": "parent organization",
"head": {
"text": "AOL",
"span": [
65,
68
]
},
"tail": {
"text": "Verizon Communications",
"span": [
19,
41
]
},
"qualifiers": [
... | On May 12 , 2015 , Verizon Communications announced plans to buy AOL for $ 50 per share in a deal valued at $ 4 . 4 billion . | |
HyperRED.train.16 | {
"hyper_rel": {
"notable work": [
"publication date",
"point in time",
"together with",
"subject has role",
"end time",
"start time",
"applies to part",
"character role",
"object has role",
"follows"
],
"child": [
"mother",
"series o... | {
"hyper_rel": [
{
"relation": "head of government",
"head": {
"text": "Canada",
"span": [
153,
159
]
},
"tail": {
"text": "Alexander Mackenzie",
"span": [
0,
19
]
},
"qualifiers": [
... | Alexander Mackenzie , PC ( January 28 , 1822 – April 17 , 1892 ) , was a building contractor and newspaper editor , and was the second Prime Minister of Canada , from November 7 , 1873 to October 8 , 1878 . | |
HyperRED.train.17 | {
"hyper_rel": {
"notable work": [
"publication date",
"point in time",
"together with",
"subject has role",
"end time",
"start time",
"applies to part",
"character role",
"object has role",
"follows"
],
"child": [
"mother",
"series o... | {
"hyper_rel": [
{
"relation": "child",
"head": {
"text": "Emperor Augustus",
"span": [
40,
56
]
},
"tail": {
"text": "Tiberius",
"span": [
131,
139
]
},
"qualifiers": [
{
... | She was the second granddaughter of the Emperor Augustus , sister - in - law , stepdaughter and daughter - in - law of the Emperor Tiberius , mother of the Emperor Caligula , maternal second cousin and sister - in - law of the Emperor Claudius and the maternal grandmother of the Emperor Nero . | |
HyperRED.train.18 | {
"hyper_rel": {
"notable work": [
"publication date",
"point in time",
"together with",
"subject has role",
"end time",
"start time",
"applies to part",
"character role",
"object has role",
"follows"
],
"child": [
"mother",
"series o... | {
"hyper_rel": [
{
"relation": "director / manager",
"head": {
"text": "Aldine Press",
"span": [
0,
12
]
},
"tail": {
"text": "Aldus Manutius",
"span": [
48,
62
]
},
"qualifiers": [
... | Aldine Press was the printing office started by Aldus Manutius in 1494 in Venice , from which were issued the celebrated Aldine editions of the classics ( Latin and Greek masterpieces plus a few more modern works ) . | |
HyperRED.train.19 | {
"hyper_rel": {
"notable work": [
"publication date",
"point in time",
"together with",
"subject has role",
"end time",
"start time",
"applies to part",
"character role",
"object has role",
"follows"
],
"child": [
"mother",
"series o... | {
"hyper_rel": [
{
"relation": "subclass of",
"head": {
"text": "farming",
"span": [
63,
70
]
},
"tail": {
"text": "aquaculture",
"span": [
23,
34
]
},
"qualifiers": [
{
"t... | According to the FAO , aquaculture " is understood to mean the farming of aquatic organisms including fish , molluscs , crustaceans and aquatic plants . | |
HyperRED.train.20 | {
"hyper_rel": {
"notable work": [
"publication date",
"point in time",
"together with",
"subject has role",
"end time",
"start time",
"applies to part",
"character role",
"object has role",
"follows"
],
"child": [
"mother",
"series o... | {
"hyper_rel": [
{
"relation": "spouse",
"head": {
"text": "Tsar Peter I",
"span": [
35,
47
]
},
"tail": {
"text": "Eudoxia Lopukhina",
"span": [
76,
93
]
},
"qualifiers": [
{
... | He was born in Moscow , the son of Tsar Peter I and the Tsar ' s first wife Eudoxia Lopukhina . | |
HyperRED.train.21 | {
"hyper_rel": {
"notable work": [
"publication date",
"point in time",
"together with",
"subject has role",
"end time",
"start time",
"applies to part",
"character role",
"object has role",
"follows"
],
"child": [
"mother",
"series o... | {
"hyper_rel": [
{
"relation": "capital of",
"head": {
"text": "Constantinople",
"span": [
182,
196
]
},
"tail": {
"text": "Byzantine emperor",
"span": [
94,
111
]
},
"qualifiers": [
... | Alexios V Doukas or Alexius V Ducas ( Greek : Ἀλέξιος Εʹ Δούκας ; d . December 1205 ) was the Byzantine emperor from 5 February to 12 April 1204 during the second and final siege of Constantinople by the participants of the Fourth Crusade . |
End of preview.
Mirror Datasets without ACE
This is the dataset used for 🪞Mirror pre-training and evaluations.
⚠️ This dataset does not contain any contents about the ACE series of datasets to prevent license breaking. If you want to download the whole dataset, please check out Spico/Mirror_ACE.
Citation
@misc{zhu_mirror_2023,
shorttitle = {Mirror},
title = {Mirror: A Universal Framework for Various Information Extraction Tasks},
author = {Zhu, Tong and Ren, Junfei and Yu, Zijian and Wu, Mengsong and Zhang, Guoliang and Qu, Xiaoye and Chen, Wenliang and Wang, Zhefeng and Huai, Baoxing and Zhang, Min},
url = {http://arxiv.org/abs/2311.05419},
doi = {10.48550/arXiv.2311.05419},
urldate = {2023-11-10},
publisher = {arXiv},
month = nov,
year = {2023},
note = {arXiv:2311.05419 [cs]},
keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language},
}
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