Hugging Face's logo

Datasets:
dhfbk
/
modafact-ita

Tasks:
Token Classification
Languages:
Italian
Tags:
Factuality Detection
Modality Detection
License:
Dataset card Viewer Files Community
Dataset Preview
Full Screen
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
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 ({'O.1', "L'"}) and 2 missing columns ({'Intenzionato', 'B-FACTUAL'}).

This happened while the csv dataset builder was generating data using

hf://datasets/dhfbk/modafact-ita/cg/multitask_seq_bio/fold_63/training_set.tsv (at revision 4a05a7fb04c377e6b2b93fcf15cc312d2acf2ae1)

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 1870, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 622, 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
              L': string
              O: string
              O.1: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 575
              to
              {'Intenzionato': Value(dtype='string', id=None), 'B-FACTUAL': Value(dtype='string', id=None), 'O': 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 1420, 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 1052, 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 1000, 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 1741, 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 1872, 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 ({'O.1', "L'"}) and 2 missing columns ({'Intenzionato', 'B-FACTUAL'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/dhfbk/modafact-ita/cg/multitask_seq_bio/fold_63/training_set.tsv (at revision 4a05a7fb04c377e6b2b93fcf15cc312d2acf2ae1)
              
              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.

Intenzionato
string
B-FACTUAL
string
O
string
a
O
O
rovesciare
B-NON_FACTUAL
B-WILL
l'
O
O
ordine
O
O
repubblicano
O
O
,
O
O
Franco
O
O
mise
B-FACTUAL
O
in
I-FACTUAL
O
atto
I-FACTUAL
O
con
O
O
altri
O
O
generali
O
O
un
I-FACTUAL
O
colpo
I-FACTUAL
O
di
I-FACTUAL
O
Stato
I-FACTUAL
O
nel
O
O
luglio
O
O
seguente
O
O
,
O
O
che
O
O
portò
B-FACTUAL
O
alla
O
O
sanguinosa
O
O
guerra
B-FACTUAL
O
civile
O
O
spagnola
O
O
.
O
O
Nel
O
O
paesaggio
O
O
sullo
O
O
sfondo
O
O
un
O
O
angelo
O
O
,
O
O
che
O
O
indica
B-FACTUAL
O
Sebastiano
O
O
,
O
O
dialoga
B-FACTUAL
O
con
O
O
san
O
O
Rocco
O
O
,
O
O
il
O
O
santo
O
O
che
O
O
con
O
O
Sebastiano
O
O
era
O
O
evocato
B-FACTUAL
O
per
O
O
proteggersi
B-NON_FACTUAL
B-FINAL
dalle
O
O
pestilenze
B-NON_FACTUAL
O
:
O
O
Il
O
O
24
O
O
ottobre
O
O
Gesja
O
O
partorì
B-FACTUAL
O
una
O
O
bambina
O
O
sana
O
O
,
O
O
ma
O
O
il
O
O
dottor
O
O
Balandin
O
O
non
O
O
mise
B-COUNTERFACTUAL
O
(
O
O
forse
O
O
di
O
O
proposito
O
O
)
O
O
i
O
O
punti
O
O
al
O
O
peritoneo
O
O
che
O
O
s'
O
O
era
O
O
strappato
B-FACTUAL
O
,
O
O
affermando
B-FACTUAL
O
che
O
O
la
O
O
ferita
O
O
si
O
O
sarebbe
O
O
risanata
B-NON_FACTUAL
O
da
O
O
sé
O
O
.
O
O
L'
O
O
efficacia
O
O
dimostrata
B-FACTUAL
O
nello
O
O
End of preview.

ModaFact - Dataset

Dataset Description

Dataset Summary

ModaFact is a textual dataset annotated with Event Factuality and Modality in Italian. ModaFact’s goal is to model in a joint way factuality and modality values of event-denoting expressions in text.

Textual data source

Original texts (sentences) have been sampled from EventNet-ITA, a dataset for Frame Parsing, consisting of annotated sentences from Wikipedia.

Statistics

Feature #
Sentences 3,039
Words 73,784
Annotations 10,445
Unique label assignments 33,029
Words per sentence (avg.) 24.28
Annotations per sentence (avg.) 3.44
Unique label assignments per sentence 10.87

Annotation

ModaFact has been originally annotated at token level, adopting the IOB2 style. Whereas for Modality the schema is unique, for Factuality we provide two representations: a fine-grained representation (FG), which specifies values over three axes (CERTAINTY, POLARITY, TIME), and a coarse-grained representation (CG), which only provides the final factuality value.

Example of fine-grained representation (FG):

Per	O
chiarire	B-POSSIBLE-POS-FUTURE-FINAL
la	O
questione	O
la	O
Santa	O
Sede	O
autorizzò	B-CERTAIN-POS-PRESENT/PAST
il	O
prelievo	B-UNDERSPECIFIED-POS-FUTURE-CONCESSIVE
di	O
campioni	O
del	O
legno	O
che	O
vennero	O
datati	B-CERTAIN-POS-PRESENT/PAST
attraverso	O
l'	O
utilizzo	B-CERTAIN-POS-PRESENT/PAST
del	O
metodo	O
del	O
carbonio-14	O
.	O

Example of coarse-grained representation (CG):

Per	O
chiarire	B-NON_FACTUAL-FINAL
la	O
questione	O
la	O
Santa	O
Sede	O
autorizzò	B-FACTUAL
il	O
prelievo	B-NON_FACTUAL-CONCESSIVE
di	O
campioni	O
del	O
legno	O
che	O
vennero	O
datati	B-FACTUAL
attraverso	O
l'	O
utilizzo	B-FACTUAL
del	O
metodo	O
del	O
carbonio-14	O
.	O

Labelset

Factuality:

  • Fine-grained

    • CERTAINTY: {CERTAIN, PROBABLE, POSSIBLE, UNDERSPECIFIED}
    • POLARITY: {POSITIVE, NEGATIVE, UNDERSPECIFIED}
    • TIME: {PRESENT/PAST, FUTURE, UNDERSPECIFIED}

  • Coarse-grained

    • {FACTUAL, NON-FACTUAL, COUNTERFACTUAL, UNDERSPECIFIED}

Modality:

  • {WILL, FINAL, CONCESSIVE, POSSIBILITY, CAPABILITY, DUTY, COERCION, EXHORTATIVE, COMMITMENT, DECISION}

Data format

According to the experimental set presented in the paper (see below, Citation Information) we provide different data formats:

  • token-level BIO sequence labelling: the dataset is formatted as a two-column tsv. The first column contains the token, the second column contains all corresponding labels (factuality and modality), concatenated with -. This format makes the dataset ready-to-train with the MaChAmp seq_bio task type.
  • token-level multi-task sequence labelling: the dataset is formatted as a three-column tsv. The first column contains the token, the second column contains all factuality labels, the third column contains the modality label. This format makes the dataset ready-to-train with the Machamp seq_bio multitask setting.
  • generative and sequence-to-sequence: the dataset is formatted as a jsonl file, containing a list of dictionaries. Each dictionary has an Input field (the sentence) and an Output field, a string composed by token=labels pairs, separated by |. This format makes the dataset ready-to train with sequence-to-sequence and causal/generative models.

Data Split

For the sake of reproducibility, we provide, for each configuration, the 5 folds used in the paper. The data split follows a 60/20/20 ratio and has been created in a stratified way. This means each train/dev/test set contains (approx) the same relative distribution of classes.

Additional Information

An instance of the mT5 model, fine-tuned on ModaFact, is available at this repo.

Licensing Information

ModaFact is released under the CC-BY-SA-4.0 License.

Citation Information

If you use ModaFact, please cite the following paper:

@inproceedings{rovera-etal-2025-modafact,
    title = "{M}oda{F}act: Multi-paradigm Evaluation for Joint Event Modality and Factuality Detection",
    author = "Rovera, Marco  and
      Cristoforetti, Serena  and
      Tonelli, Sara",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.coling-main.425/",
    pages = "6378--6396",
    }
Downloads last month
18