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--- |
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library_name: setfit |
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tags: |
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- setfit |
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- absa |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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metrics: |
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- accuracy |
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widget: |
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- text: pelayanan lambat pelayan kurang:pelayanan lambat pelayan kurang ajar dan tidak |
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sopan terlalu banyak ngerumpi ngobrol sesama pelayan akhirnya kerjaan tidak pokus |
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dan salah kasih pesanan sudah pelayan tidak bagus pelayanya kurang ajar |
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- text: batu bandung dengan tempat yang bagus &:Restoran cepat saji 24 jam di buah |
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batu bandung dengan tempat yang bagus & nyaman, pelayanan yang baik, dan pelayanan |
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yang cepat. Di sini untuk sarapan dan menghabiskan sekitar 40k hingga 50k per |
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orang. Saya ingin pergi ke sana lagi lain kali. |
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- text: kentang gorengnya. rasanya sangat enak berbeda:Pengalaman luar biasa makan |
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di sini. Tidak hanya makanannya saja yang luar biasa. tempatnya sangat nyaman |
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untuk berkumpul bersama teman dan keluarga. Jangan lupa pesan kentang gorengnya. |
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rasanya sangat enak berbeda dengan kentang goreng di tempat lain |
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- text: Pelayanannya bagus dan makanannya:Pelayanannya bagus dan makanannya tidak |
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membosankan😊😊 … |
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- text: luas. Untuk rasa seperti MCd biasa:Tempat makannya nyaman, lumayan besar, |
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pegawainya ramah. Tempat parkirnya sungguh luas. Untuk rasa seperti MCd biasa, |
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enak dan cukup enak. Waktu penyajiannya cukup cepat Menyukainya. |
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pipeline_tag: text-classification |
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inference: false |
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--- |
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# SetFit Polarity Model |
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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. |
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The model has been trained using an efficient few-shot learning technique that involves: |
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
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2. Training a classification head with features from the fine-tuned Sentence Transformer. |
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This model was trained within the context of a larger system for ABSA, which looks like so: |
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1. Use a spaCy model to select possible aspect span candidates. |
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2. Use a SetFit model to filter these possible aspect span candidates. |
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3. **Use this SetFit model to classify the filtered aspect span candidates.** |
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## Model Details |
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### Model Description |
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- **Model Type:** SetFit |
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<!-- - **Sentence Transformer:** [Unknown](https://huggingface.co/unknown) --> |
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- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
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- **spaCy Model:** id_core_news_trf |
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- **SetFitABSA Aspect Model:** [kaylaisya/absa-aspect](https://huggingface.co/kaylaisya/absa-aspect) |
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- **SetFitABSA Polarity Model:** [kaylaisya/absa-polarity](https://huggingface.co/kaylaisya/absa-polarity) |
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- **Maximum Sequence Length:** 8192 tokens |
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- **Number of Classes:** 3 classes |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
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- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
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### Model Labels |
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| Label | Examples | |
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|:--------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| positif | <ul><li>'enak enak, pelayanannya juga mantap,:makanannya enak enak, pelayanannya juga mantap, terbaik lah'</li><li>'Rasanya selalu enak gak:Rasanya selalu enak gak pernah berubah, higines. Anak2 pada suka makan ayam goreng mcd'</li><li>'Pelayanannya cukup ramah dan:Pelayanannya cukup ramah dan praktis. Makanannya enak dan segar, khususnya es krim. Sip, lah.'</li></ul> | |
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| netral | <ul><li>'nya unik.. harganya standar lah sesuai:Sangat strategis,ramai, cukup luas dan nyaman, pelayanan ramah dan cepat.. parkiran juga luas , akhirnya kesampaian juga cobain menu ayam gulai cukup enak&cita rasa nya unik.. harganya standar lah sesuai rasa ,salam sukses selalu ☺️'</li><li>'makan siang, tempat nya menjorok kedalam:Mampir ke sini bareng temen mau makan siang, tempat nya menjorok kedalam, tatanan design nya MCD semua standard sesuai dengan kapasitas lahan nya, tempatnya juga dijaga banget kebersihannya, pelayanannya bagus, kakak-kakak pelayannya juga …'</li><li>'mbanya bantu take tempat, gesit ketika:Ini mba2nya supuer helpfull, karena kesana serombongan ber10 orang, mbanya bantu take tempat, gesit ketika dimintai bantuan. …'</li></ul> | |
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| negatif | <ul><li>'Pelayanan DriveThru terburuk!:Pelayanan DriveThru terburuk!!! Parkiran jg sempit gak bs keluar kalo batal drive thru. Ngantri drive thru 1 jam! Gak abis pikir. Tolong deh diperbaiki. Jika memang gak bs melayani, kasi tau dan segera ditutup drpd orang menunggu lama. Tolong banget management McD buah batu diperhatikan'</li><li>'Apa-apaan ini pelayanannya, pesen coke:Apa-apaan ini pelayanannya, pesen coke float doang sampe 32 menit. Dah gitu tadi datang workernya bilang ga ready sodanya, lah aturan dari awal pas payment di kasir langsung ngomong kalo ada mulut & otak. Kek gitu lalu nyuruh aku konfir ke kasir, …'</li><li>'menu paket ,rasa ayam crispyny agak:Over all ok...\nCm kmriin pesen menu paket ,rasa ayam crispyny agak asin.. smga kedepan lebih baik lg.'</li></ul> | |
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## Uses |
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### Direct Use for Inference |
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First install the SetFit library: |
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```bash |
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pip install setfit |
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``` |
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Then you can load this model and run inference. |
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```python |
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from setfit import AbsaModel |
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# Download from the 🤗 Hub |
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model = AbsaModel.from_pretrained( |
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"kaylaisya/absa-aspect", |
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"kaylaisya/absa-polarity", |
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) |
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# Run inference |
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preds = model("The food was great, but the venue is just way too busy.") |
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``` |
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### Downstream Use |
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*List how someone could finetune this model on their own dataset.* |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:-------------|:----|:--------|:----| |
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| Word count | 6 | 27.2254 | 64 | |
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| Label | Training Sample Count | |
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|:--------|:----------------------| |
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| konflik | 0 | |
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| negatif | 12 | |
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| netral | 24 | |
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| positif | 758 | |
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### Training Hyperparameters |
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- batch_size: (64, 64) |
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- num_epochs: (1, 1) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- body_learning_rate: (2e-05, 1e-05) |
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- head_learning_rate: 0.01 |
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- loss: CosineSimilarityLoss |
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- distance_metric: cosine_distance |
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- margin: 0.25 |
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- end_to_end: False |
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- use_amp: True |
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- warmup_proportion: 0.1 |
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- seed: 42 |
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- eval_max_steps: -1 |
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- load_best_model_at_end: False |
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### Framework Versions |
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- Python: 3.10.12 |
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- SetFit: 1.0.3 |
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- Sentence Transformers: 3.0.1 |
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- spaCy: 3.7.4 |
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- Transformers: 4.36.2 |
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- PyTorch: 2.3.0+cu121 |
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- Datasets: 2.19.2 |
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- Tokenizers: 0.15.2 |
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## Citation |
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### BibTeX |
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```bibtex |
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@article{https://doi.org/10.48550/arxiv.2209.11055, |
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doi = {10.48550/ARXIV.2209.11055}, |
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url = {https://arxiv.org/abs/2209.11055}, |
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author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Efficient Few-Shot Learning Without Prompts}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
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