Update README.md

README.md

@@ -7,50 +7,50 @@ tags:
 widget: []
 metrics:
 - accuracy
+- f1
+- precision
+- recall
 pipeline_tag: text-classification
 library_name: setfit
 inference: true
-base_model: NLBSE/nlbse25_pharo
+license: mit
+datasets:
+- NLBSE/nlbse25-code-comment-classification
+language:
+- en
+base_model:
+- sentence-transformers/paraphrase-MiniLM-L3-v2
 ---
 
-# SetFit with NLBSE/nlbse25_pharo
+# Pharo comment classifier
 
-This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [NLBSE/nlbse25_pharo](https://huggingface.co/NLBSE/nlbse25_pharo) as the Sentence Transformer embedding model. A RandomForestClassifier instance is used for classification.
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Pharo code comment classification.
 
-The model has been trained using an efficient few-shot learning technique that involves:
+The model has been trained using few-shot learning 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.
+2. Training a classification head with features from the fine-tuned model.
 
-## Model Details
+## Model Description
 
-### Model Description
 - **Model Type:** SetFit
-- **Sentence Transformer body:** [NLBSE/nlbse25_pharo](https://huggingface.co/NLBSE/nlbse25_pharo)
-- **Classification head:** a RandomForestClassifier instance
-- **Maximum Sequence Length:** 128 tokens
-<!-- - **Number of Classes:** Unknown -->
-<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
+- **Classification head:** [RandomForestClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)
 
-### Model Sources
+## 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/fabiancpl/sbert-comment-classification/)
+- **Paper:** [Evaluating the Performance and Efficiency of Sentence-BERT for Code Comment Classification](https://ieeexplore.ieee.org/document/11029440)
+- **Dataset:** [HF Dataset](https://huggingface.co/datasets/NLBSE/nlbse25-code-comment-classification)
 
-## Uses
+## How to use it
 
-### Direct Use for Inference
-
-First install the SetFit library:
+First, install the depencies:
 
 ```bash
-pip install setfit
+pip install setfit scikit-learn
 ```
 
-Then you can load this model and run inference.
+Then, load the model and run inferences:
 
 ```python
 from setfit import SetFitModel
@@ -58,74 +58,17 @@ from setfit import SetFitModel
 # Download from the 🤗 Hub
 model = SetFitModel.from_pretrained("fabiancpl/nlbse25_pharo")
 # Run inference
-preds = model("I loved the spiderman movie!")
+preds = model("This function sorts a list of numbers.")
 ```
 
-<!--
-### Downstream Use
-
-*List how someone could finetune this model on their own dataset.*
--->
-
-<!--
-### Out-of-Scope Use
-
-*List how the model may foreseeably be misused and address what users ought not to do with the model.*
--->
-
-<!--
-## Bias, Risks and Limitations
-
-*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
--->
-
-<!--
-### Recommendations
-
-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
--->
-
-## Training Details
+## Cite as
 
-### Framework Versions
-- Python: 3.12.4
-- SetFit: 1.1.0
-- Sentence Transformers: 3.3.0
-- Transformers: 4.42.2
-- PyTorch: 2.5.1+cu124
-- Datasets: 3.1.0
-- Tokenizers: 0.19.1
-
-## Citation
-
-### 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}
-}
-```
-
-<!--
-## Glossary
-
-*Clearly define terms in order to be accessible across audiences.*
--->
-
-<!--
-## Model Card Authors
-
-*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
--->
-
-<!--
-## Model Card Contact
-
-*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--->
+@inproceedings{11029440,
+  author={Peña, Fabian C. and Herbold, Steffen},
+  booktitle={2025 IEEE/ACM International Workshop on Natural Language-Based Software Engineering (NLBSE)}, 
+  title={Evaluating the Performance and Efficiency of Sentence-BERT for Code Comment Classification}, 
+  year={2025},
+  pages={21-24},
+  doi={10.1109/NLBSE66842.2025.00010}}
+```