Update README.md

README.md

@@ -44,9 +44,11 @@ model-index:
       name: Metric
 ---
 
-# SetFit with TurkuNLP/bert-base-finnish-cased-v1
+# Detect Questions in Asynchronous Conversation Comments
 
-This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [TurkuNLP/bert-base-finnish-cased-v1](https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
+## 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 question or not. The configuration of the model is that the model is based on only one annotator's annotations (annotator A3). 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:
 
@@ -60,29 +62,28 @@ The model has been trained using an efficient few-shot learning technique that i
 - **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
+- **Number of Classes:** 2 classes (action present yes/no)
 <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
 <!-- - **Language:** Unknown -->
 <!-- - **License:** Unknown -->
 
 ### Model Sources
 
-- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
-- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
-- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
-
+- **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>'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!'</li><li>'Etunimi Sukunimi perustuslakia ei ole rikottu niissä asioissa mitä convoypellet väitti, kaikki mitä kaverit väittää ei ole totta .'</li><li>'Takaisin hiilivoimaan?'</li></ul>                                                                                                   |
-| 1     | <ul><li>'"Hylkiö" unionin toimesta johon ei kuulu.'</li><li>'Etunimi Almonkari-Kuikka 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.'</li><li>'Mikään ei ole niin varmaa kuin epävarma.  KUKAAN ei millään voi tietää mitä tapahtuu koronan tai ylipäätään minkään suhteen.  Joka muuta väittää on typerys...'</li></ul> |
+| 1     | <ul><li>'"Hylkiö" unionin toimesta johon ei kuulu.'</li><li>'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.'</li><li>'Mikään ei ole niin varmaa kuin epävarma.  KUKAAN ei millään voi tietää mitä tapahtuu koronan tai ylipäätään minkään suhteen.  Joka muuta väittää on typerys...'</li></ul> |
 
 ## Evaluation
 
 ### Metrics
 | Label   | Metric |
 |:--------|:-------|
-| **all** | 0.8719 |
+| **5-fold cross-validated F1** | 0.74 |
 
 ## Uses
 
@@ -105,11 +106,22 @@ model = SetFitModel.from_pretrained("Finnish-actions/SetFit-FinBERT1-Avg-stateme
 preds = model("Kohta on lisää lapsia sairaalassa koronan vuoksi ☹")
 ```
 
-<!--
+
 ### Downstream Use
 
-*List how someone could finetune this model on their own dataset.*
--->
+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.
+
 
 <!--
 ### Out-of-Scope Use
@@ -141,6 +153,7 @@ preds = model("Kohta on lisää lapsia sairaalassa koronan vuoksi ☹")
 | 0     | 218                   |
 | 1     | 624                   |
 
+<!--
 ### Training Hyperparameters
 - batch_size: (16, 16)
 - num_epochs: (4, 4)
@@ -219,6 +232,7 @@ preds = model("Kohta on lisää lapsia sairaalassa koronan vuoksi ☹")
 | 3.8766 | 2450 | 0.0           | -               |
 | 3.9557 | 2500 | 0.0           | -               |
 | 4.0    | 2528 | -             | 0.1996          |
+-->
 
 ### Framework Versions
 - Python: 3.11.9
@@ -233,16 +247,17 @@ preds = model("Kohta on lisää lapsia sairaalassa koronan vuoksi ☹")
 
 ### BibTeX
 ```bibtex
-@article{https://doi.org/10.48550/arxiv.2209.11055,
-    doi = {10.48550/ARXIV.2209.11055},
-    url = {https://arxiv.org/abs/2209.11055},
-    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
-    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
-    title = {Efficient Few-Shot Learning Without Prompts},
-    publisher = {arXiv},
-    year = {2022},
-    copyright = {Creative Commons Attribution 4.0 International}
+@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}
 }
+
 ```
 
 <!--