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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 ({'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}
- CERTAINTY: {
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
jsonlfile, 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",
}
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