This is a SetFit model that can be used for Text Classification of actions in asynchronous conversation. This particular model detects if a comment includes an accusation or not. The configuration of the model is that the model is based on averaged annotations (from 3 annotators). Metric evaluations are based on conservative ground truth (see paper). This SetFit model uses TurkuNLP/bert-base-finnish-cased-v1 as the Sentence Transformer embedding model (using word embeddings). A LogisticRegression instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

Fine-tuning a Sentence Transformer with contrastive learning.
Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Type: SetFit
Sentence Transformer body: TurkuNLP/bert-base-finnish-cased-v1
Classification head: a LogisticRegression instance
Maximum Sequence Length: 512 tokens
Number of Classes: 2 classes (action present yes/no)

Model Sources

Repository: GitHub
Paper: Paakki, H., Toivanen, P. and Kajava K. (2025). Implicit and Indirect: Detecting Face-threatening and Paired Actions in Asynchronous Online Conversations. Northern European Journal of Language Technology (NEJLT), 11(1), pp. 58-83.

Model Labels

Label

Examples

'Etunimi Sukunimi miten luulet tilanteen parantuneen kun sairaala- ja tehohoito potilaiden määrä on vain kasvanut silloisesta?\nOlet niin totaalisen puusilmäinen ja hallirusvihan vallassa, että tätä on turha jatkaa pitemmälle. Pysy terveenä ja rauhallista joulua!'
'Etunimi Sukunimi Mun mielestä paheksunta heidän hyvää tarkoittavista toimistaan on pahinta mitä voi olla. Ihmisillä voi olla tarve tehdä jotain. Monen isoisät kuolivat talvisodassa. Henkisesti heille on varmasti tärkeää olla mukana ja auttaa.'
'Kokoomustaustaisella Päivi Sillanaukeella oli tekemistä huoltovarnuusvarastomme kanssa. Hänet sivuutettiin virasta, jota hän oli hoitanut edellisen hallituksen aikana. Virkaan nimitettiin Kirsi Varhila. Maskien puutteen takia kokoomustaustainen Päivi Sillanaukee tuli julkisuuteen ja kertoi, että Huoltovarmuuskeskuksen ovet avataan ja varmuusvaraston maskit otetaan käyttöön. Maskit olivat vanhentuneita ja eivät kelvanneet sairaalakäyttöön. Tuli sellainen käsitys, että hän ei ole lojaali tälle nykyiselle hallitukselle.https://yle.fi/uutiset/3-10936361'

'"Hylkiö" unionin toimesta johon ei kuulu.'
'Etunimi Sukunimi en nyt varsinaisesti pelkästään tuota aihetta tarkoittanutkaan. Sekin on kuitenkin vähintään kyseenalaista, koska kyseessä ei ole valmis tuote, vaan hätämyyntiluvalla käytössä oleva ruiske, ja sen seurauksena on niinikään perusoikeudellinen terveydenhuollon taso turvaamattomalla tasolla.'
'Etunimi Sukunimi perustuslakia ei ole rikottu niissä asioissa mitä convoypellet väitti, kaikki mitä kaverit väittää ei ole totta .'

Evaluation

Metrics

Label	Metric
5-fold cross-valitated F1	0.65

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

python

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("Finnish-actions/SetFit-FinBERT1-Avg-accusation")
# Run inference
preds = model("Kohta on lisää lapsia sairaalassa koronan vuoksi ☹")

Downstream Use

NB. This model has been trained on data coming from Finnish language asynchronous conversations under crisis related news on Facebook. This specific model has been trained to detect whether a comment includes a question or not. It reflects only one of our annotators' label interpretations, so the best use of our models (see our paper) would be to combine a set of models we provide on our Huggingface (Finnish-actions), and use a model ensemble to provide label predictions. It needs to be noted also that the model may not be well applicable outside of its empirical context, so in downstream applications, one should always conduct an evaluation of the model applicability using manually annotated data from that specific context (see our paper for annotation instructions).

Out-of-Scope Use

Please use this model only for action detection and analysis. Uses of this model and the involved data for generative purposes (e.g. NLG) is prohibited.

Bias, Risks and Limitations

Note that the model may produce errors. Due to the size of the training dataset, model may not generalize very well even for other novel topics within the same context. Note that model predictions should not be regarded as final judgments e.g. for online moderation purposes, but each case should also be regarded individually if using model predictions to support moderation. Also, the annotations only reflect three (though experienced) annotators' interpretations, so there might be perspectives on data intepretation that have not been taken into account here.

If model is used to support moderation on social media, we recommend that final judgments should always be left for human moderators.

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	19.9323	213

Label	Training Sample Count
0	734
1	108

Framework Versions

Python: 3.11.9
SetFit: 1.1.3
Sentence Transformers: 3.2.0
Transformers: 4.44.0
PyTorch: 2.4.0+cu124
Datasets: 2.21.0
Tokenizers: 0.19.1

Citation

BibTeX

bibtex

@article{paakki-implicit-indirect,
    doi = {https://doi.org/10.3384/nejlt.2000-1533.2025.5980},
    url = {https://nejlt.ep.liu.se/article/view/5980},
    author = {Paakki, Henna and Toivanen, Pihla and Kajava, Kaisla},
    title = {Implicit and Indirect: Detecting Face-threatening and Paired Actions in Asynchronous Online Conversations},
    publisher = {Northern European Journal of Language Technology (NEJLT)},
    volume= {11},
    number= {1},
    year = {2025}
}