--- library_name: setfit tags: - setfit - absa - sentence-transformers - text-classification - generated_from_setfit_trainer metrics: - accuracy widget: - text: menarik yah sekin bagus nice:bagus menarik yah sekin bagus nice - text: game nya bagus iklan nya kurangin yah:game nya bagus iklan nya kurangin yah pesawatnya kadang kadang berat ringan lompat perbaiki gak pencet lompat pencet gak lompat mohon perbaiki game nya pesan game nya bagus - text: bagus suara bagus grafik bagus iklan game:game bagus suara bagus grafik bagus iklan game bikin sabar kalo stress main ya - text: udah selesai muncul iklan game gak lanjutin:game nya bagus pas main mode practice levelnya udah selesai muncul iklan game gak lanjutin ngulang udah susah main ehh ulang - text: game bagus sih kalo:game bagus sih kalo tamba level lai bole kalo katong seng loncat akang loncat deng kalo iklan seng tekang x seng tolong perbaiki ee pipeline_tag: text-classification inference: false --- # SetFit Polarity Model This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. In particular, this model is in charge of classifying aspect polarities. 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. This model was trained within the context of a larger system for ABSA, which looks like so: 1. Use a spaCy model to select possible aspect span candidates. 2. Use a SetFit model to filter these possible aspect span candidates. 3. **Use this SetFit model to classify the filtered aspect span candidates.** ## Model Details ### Model Description - **Model Type:** SetFit - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **spaCy Model:** id_core_news_trf - **SetFitABSA Aspect Model:** [jetri20/ABSA_review_game_geometry-aspect](https://huggingface.co/jetri20/ABSA_review_game_geometry-aspect) - **SetFitABSA Polarity Model:** [jetri20/ABSA_review_game_geometry-polarity](https://huggingface.co/jetri20/ABSA_review_game_geometry-polarity) - **Maximum Sequence Length:** 8192 tokens - **Number of Classes:** 2 classes ### 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) ### Model Labels | Label | Examples | |:--------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | negatif |