--- library_name: setfit tags: - setfit - absa - sentence-transformers - text-classification - generated_from_setfit_trainer metrics: - accuracy widget: - text: pelayanan lambat pelayan kurang:pelayanan lambat pelayan kurang ajar dan tidak sopan terlalu banyak ngerumpi ngobrol sesama pelayan akhirnya kerjaan tidak pokus dan salah kasih pesanan sudah pelayan tidak bagus pelayanya kurang ajar - text: batu bandung dengan tempat yang bagus &:Restoran cepat saji 24 jam di buah batu bandung dengan tempat yang bagus & nyaman, pelayanan yang baik, dan pelayanan yang cepat. Di sini untuk sarapan dan menghabiskan sekitar 40k hingga 50k per orang. Saya ingin pergi ke sana lagi lain kali. - text: kentang gorengnya. rasanya sangat enak berbeda:Pengalaman luar biasa makan di sini. Tidak hanya makanannya saja yang luar biasa. tempatnya sangat nyaman untuk berkumpul bersama teman dan keluarga. Jangan lupa pesan kentang gorengnya. rasanya sangat enak berbeda dengan kentang goreng di tempat lain - text: Pelayanannya bagus dan makanannya:Pelayanannya bagus dan makanannya tidak membosankan😊😊 … - text: luas. Untuk rasa seperti MCd biasa:Tempat makannya nyaman, lumayan besar, pegawainya ramah. Tempat parkirnya sungguh luas. Untuk rasa seperti MCd biasa, enak dan cukup enak. Waktu penyajiannya cukup cepat Menyukainya. 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:** [kaylaisya/absa-aspect](https://huggingface.co/kaylaisya/absa-aspect) - **SetFitABSA Polarity Model:** [kaylaisya/absa-polarity](https://huggingface.co/kaylaisya/absa-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 | | | netral | | | negatif | | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import AbsaModel # Download from the 🤗 Hub model = AbsaModel.from_pretrained( "kaylaisya/absa-aspect", "kaylaisya/absa-polarity", ) # Run inference preds = model("The food was great, but the venue is just way too busy.") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 6 | 27.2254 | 64 | | Label | Training Sample Count | |:--------|:----------------------| | konflik | 0 | | negatif | 12 | | netral | 24 | | positif | 758 | ### Training Hyperparameters - batch_size: (64, 64) - num_epochs: (1, 1) - max_steps: -1 - sampling_strategy: oversampling - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.01 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: True - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Framework Versions - Python: 3.10.12 - SetFit: 1.0.3 - Sentence Transformers: 3.0.1 - spaCy: 3.7.4 - Transformers: 4.36.2 - PyTorch: 2.3.0+cu121 - Datasets: 2.19.2 - Tokenizers: 0.15.2 ## 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} } ```