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 a denial or not. The configuration of the model is that the model is based on only one annotator's annotations (annotator A1). 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

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 herra senkun aloittaa keräyksen♥️'
'Etunimi Sukunimi venäjän syy hintojen nousu vai syytätkö sodastakin Suomen hallitusta ? 😖'
'Etunimi Sukunimi Olikhaan se virve'

'Etunimi Sukunimi ei riittää 3'
'" Meillä on jokaisella oikeus olla erimieltä koronasta" No ei ole. Faktuaaliset kysymykset koronan luonteesta (vaikkapa siitä, toimivatko maskit koronan leviämisen estämiseksi) eivät ole mitään mielipidekysymyksiä, jossa mikä tahansa mielipide on täysin saman arvoinen.'
'Etunimi Sukunimi Ei tarjota, ainakaan mun kotikunnassa ei saa, jos ei kuulu riskiryhmään. Itsellänikin olisi yli 6 kk kakkosrokotuksesta, mutta en saa vielä varata kolmatta.'

Evaluation

Metrics

Label	Metric
10-fold cross-validated F1	0.55

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-A1-denial")
# Run inference
preds = model("Etunimi Sukunimi 🙋‍♀️")

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	20.3115	213

Label	Training Sample Count
0	922
1	41

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}
}