README.md

---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Kremlissä sihteeristö on kiireellä kirjoitellut ”da” lappuja, jotka toimittaa
    perille Nopea Kortteliohjuspalvelu Byroo (NKB).  Aika kätevästi Venäjä ”äänestää”
    itselleen puolustettavaa aluetta.
- text: Etunimi Sukunimi tai Keisarillista Venäjää johon Suomi kuului.
- text: Etunimi Sukunimi 🙋‍♀️
- text: No jos haluaa lapsensa kotiin jättää, niin luultavasti se lupa joko lomaan
    tai kotiopetukseen tulee koululta. Oman lapsen kohdalla riitti rehtorille ilmoitus
    ja kaikki oli kunnossa ettei nuori tule kouluun. Tehtävät tulee kotiin, niin kuin
    esim. lomalle jäädessä.

metrics:
- metric
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: TurkuNLP/bert-base-finnish-cased-v1
model-index:
- name: SetFit with TurkuNLP/bert-base-finnish-cased-v1
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: metric
      value: 0.9149516574585634
      name: Metric
---

# Detect Actions in Asynchronous Conversation Comments

# SetFit with TurkuNLP/bert-base-finnish-cased-v1

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification of actions in asynchronous conversation. This particular model detects if a comment includes a request 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:

1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. 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](https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
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### Model Sources

- **Repository:** [GitHub](https://github.com/henniina/Detecting-paired-actions)
- **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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0     | <ul><li>'Luulen, että Halla-aho on vaiti.'</li><li>'Etunimi Sukunimi paikalla on pelastakaa lapset ry , SPR ja monia muita kriisityön osaajia jotka saavat siihen koulutuksen ja tuen ,kaikki vapaaehtoiset tarvitaan mutta kotimaasta käsin voi auttaa turvallisesti ja se on yhtä arvokasta ja tärkeää apua !meidän Ammattilaisten on pidettävä huolta myös maallikko auttajista !kriisityötä 30 v tehneenä yhdyn Etunimi Sukunimi kommentteihin jolla myös kokemusta ja osaamista aiheesta .'</li><li>'Etunimi Sukunimi Mutta miten sen varmistaa, että rahat menevät oikeaan kohteeseen? Kaikenlaisia huijaushuhuja pyörii esim. SPR:n aiemmissa keräyksissä. Kun nyt Suomi saisi edes niitä aseita liikkeelle. Monta onnistunutta lähetystä on jo mennyt perille yksityisten ihmisten ansiosta. Mä nostan kyllä hattua! Yksikin pelastettu lapsi on vaivan arvoista. 🥲'</li></ul> |
| 1     | <ul><li>'Entä jos tuettaisiin köyhäksi kupattuja suomalaisia?'</li><li>'Etunimi Sukunimi No jos tarkkoja ollaan niin Stalin ja Hitler jakoivat etupiirinsä ja aloittivat sodan Euroopassa hyökkäämällä Puolaan jakamalla sen. Suomella olisi ollut vastaava kohtalo Neuvostoliiton alaisuudessa 1939-1940 jos ei hanttiin olisi pistetty. Natoon pitää ehdottomasti liittyä, Ukraina on hyvä elävä esimerkki siitä mikä on venäjän naapurien kohtalo jotka jäävät liittoumien ulkopuolelle.'</li><li>'Etunimi Sukunimi Testaa itse peilin edessä. Aivasta ilman maskia ja maskin kanssa. Puhu lähellä peiliä kovaan ääneen maskin kanssa ja ilman. Ihan oma testi. Kummassa tapauksessa pisarat likaavat peiliä enemmän? Ihan oma testi. Peilin täytyy olla puhdas testiä tehdessä.'</li></ul>                                                                                            |

## Evaluation

### Metrics
| Label   | Metric |
|:--------|:-------|
| **10-fold cross-validated F1** | 0.74 |

## Uses

### Direct Use for Inference

First install the SetFit library:

```bash
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-request")
# 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.


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### Out-of-Scope Use

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## Bias, Risks and Limitations

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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 1   | 20.3800 | 213 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 758                   |
| 1     | 84                    |

<!--
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (4, 4)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 6
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- evaluation_strategy: epoch
- eval_max_steps: -1
- load_best_model_at_end: False
--> 

### 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}
}

```

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