Update README.md

README.md

@@ -7,7 +7,9 @@ tags:
 pipeline_tag: text-classification
 ---
 
-# binbin83/setfit-MiniLM-dialog-act-fr
+# binbin83/setfit-MiniLM-dialog-act-13nov
+
+The model is a multi-class multi-label text classifier to distinguish the  different dialog act in semi-structured interview. The data used fot fine-tuning were in French.
 
 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
 
@@ -28,13 +30,66 @@ You can then run inference as follows:
 from setfit import SetFitModel
 
 # Download from Hub and run inference
-model = SetFitModel.from_pretrained("binbin83/setfit-MiniLM-dialog-act-fr")
+model = SetFitModel.from_pretrained("binbin83/setfit-MiniLM-dialog-act-13nov")
+label_dict = {'Introductory': 0, 'FollowUp': 1, 'Probing': 2, 'Specifying': 3, 'Structuring': 4, 'DirectQuestion': 5, 'Interpreting': 6, 'Ending': 7}
 # Run inference
-preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
+preds = model(["Vous pouvez continuer", "Pouvez-vous me dire précisément quel a été l'odre chronologique des événements ?"])
+labels = [[[f for f, p in zip(labels_dict, ps) if p] for ps in [pred]] for pred in preds ]
+
 ```
 
+##  Labels and training data
+Brinkmann, S., & Kvale, S (1), define classification of dialog act in interview:
+* Introductory: Can you tell me about ... (something specific)?,
+* Follow-up verbal cues: repeat back keywords to participants, ask for reflection or unpacking of point just made,
+* Probing: Can you say a little more about X? Why do you think X...? (for example, Why do you think X is that way? Why do you think X is important?),
+* Specifying: Can you give me an example of X?,
+* Indirect: How do you think other people view X?,
+* Structuring: Thank you for that. I’d like to move to another topic...
+* Direct (later stages): When you mention X, are you thinking like Y or Z?,
+* Interpreting: So, what I have gathered is that...,
+* Ending: I have asked all the questions I had, but I wanted to check whether there is something else about your experience/understanding we haven’t covered? Do you have any questions for me?,
+
+On our corpus of interviews, we humanly label 500 turn of speech using this classification. We use 0.7 to train and evaluate on 0.3.
+
+The entire corpus is composed of the following examples:  
+
+('DirectQuestion', 23), ('Probing', 15), ('Interpreting', 15), ('Specifying', 14), ('Structuring', 7), ('FollowUp', 6), ('Introductory', 5), ('Ending', 5)
+
+(1) Brinkmann, S., & Kvale, S. (2015). InterViews: Learning the Craft of Qualitative Research Interviewing. (3. ed.) SAGE Publications.
+
+
+## Training and Performances
+
+We finetune: "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
+using SetFit with CosineLossSimilarity and this parapeters: epochs = 20, batch_size=32, num_iterations = 50
+
+On the test dataset : 
+('Probing', 146), ('Specifying', 135), ('FollowUp', 134), ('DirectQuestion', 125), ('Interpreting', 44), ('Structuring', 27), ('Introductory', 12), ('Ending', 12)
+
+
+On our test dataset, we get this results: 
+{'f1': 0.35005547563028,  'f1_micro': 0.3686131386861314,  'f1_sample': 0.3120075046904315,  'accuracy': 0.19887429643527205}
+
 ## BibTeX entry and citation info
 
+
+To cite the current study:
+```bibtex
+@article{
+doi = {conference paper},
+url = {https://arxiv.org/abs/2209.11055},
+author = {Quillivic Robin, Charles Payet},
+keywords = {NLP, JADT},
+title = {Semi-Structured Interview Analysis: A French NLP Toolbox for Social Sciences},
+publisher = {JADT},
+year = {2024},
+copyright = {Creative Commons Attribution 4.0 International}
+}
+```
+
+
+To cite the setFit paper: 
 ```bibtex
 @article{https://doi.org/10.48550/arxiv.2209.11055,
 doi = {10.48550/ARXIV.2209.11055},