Update README.md

README.md

@@ -37,9 +37,11 @@ model-index:
       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. 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.
+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:
 
@@ -60,10 +62,9 @@ The model has been trained using an efficient few-shot learning technique that i
 
 ### 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
 |:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
@@ -75,7 +76,7 @@ The model has been trained using an efficient few-shot learning technique that i
 ### Metrics
 | Label   | Metric |
 |:--------|:-------|
-| **all** | 0.9150 |
+| **10-fold cross-validated F1** | 0.74 |
 
 ## Uses
 
@@ -98,11 +99,20 @@ model = SetFitModel.from_pretrained("Finnish-actions/SetFit-FinBERT1-Avg-request
 preds = model("Etunimi Sukunimi 🙋‍♀️")
 ```
 
-<!--
+
 ### 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
@@ -134,6 +144,7 @@ preds = model("Etunimi Sukunimi 🙋‍♀️")
 | 0     | 758                   |
 | 1     | 84                    |
 
+<!--
 ### Training Hyperparameters
 - batch_size: (16, 16)
 - num_epochs: (4, 4)
@@ -153,7 +164,7 @@ preds = model("Etunimi Sukunimi 🙋‍♀️")
 - evaluation_strategy: epoch
 - eval_max_steps: -1
 - load_best_model_at_end: False
- 
+--> 
 
 ### Framework Versions
 - Python: 3.11.9
@@ -178,6 +189,7 @@ preds = model("Etunimi Sukunimi 🙋‍♀️")
     number= {1},
     year = {2025}
 }
+
 ```
 
 <!--