--- library_name: setfit tags: - setfit - absa - sentence-transformers - text-classification - generated_from_setfit_trainer metrics: - accuracy widget: - text: yang bersih. Pelayanan sangat Ramah dan:Tempat nya yang bersih. Pelayanan sangat Ramah dan makanan ny yg sangat lezat - text: Restoran dengan pelayanan yang baik di:Restoran dengan pelayanan yang baik di kota bandung, makanan yang disajikan sesuai dengan harga dan sangat enak. … - text: dan higienis dengan pelayanan sangat maksimal dan:Saya Makanan disini sangat enak dan higienis dengan pelayanan sangat maksimal dan ditunjang dengan fasilitas yang oke. Parkiran luas, tempat bersih dan nyaman. Good - text: ke sini, tempat ini makanan cepat:Saya pernah ke sini, tempat ini makanan cepat saji yang enak bersama kalian untuk makan siang cepat saji, kamarnya bersih, sirkulasi udaranya sempurna dan tentu saja memiliki internet berkecepatan tinggi, sangat direkomendasikan - text: 'Ini tempat yang bagus untuk:Ini tempat yang bagus untuk keluarga, sahabat.. Dan juga baik untuk tamu kita.. Tapi pelayanannya terlambat..' 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:** [pupugu02/absa-setfit-resto-aspect](https://huggingface.co/pupugu02/absa-setfit-resto-aspect) - **SetFitABSA Polarity Model:** [pupugu02/absa-setfit-resto-polarity](https://huggingface.co/pupugu02/absa-setfit-resto-polarity) - **Maximum Sequence Length:** 8192 tokens - **Number of Classes:** 3 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 | |:--------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | positif |