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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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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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base_model: firqaaa/indo-sentence-bert-base |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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widget: |
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- text: halaman 97 - 128 tidak ada , diulang halaman 65 - 96 , pembelian hari minggu |
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tanggal 24 desember sore sekitar jam 4 pembayaran menggunakan kartu atm bri bersamaan |
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dengan buku the puppeteer dan sirkus pohon |
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- text: liverpool sukses di kandang tottenham |
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- text: hai angga , untuk penerbitan tiket reschedule diharuskan melakukan pembayaran |
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dulu ya . |
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- text: sedih kalau umat diprovokasi supaya saling membenci . |
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- text: berada di lokasi strategis jalan merdeka , berseberangan agak ke samping bandung |
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indah plaza , tapat sebelah kanan jalan sebelum traffic light , parkir mobil cukup |
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luas . saus bumbu dan lain-lain disediakan cukup lengkap di lantai bawah . di |
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lantai atas suasana agak sepi . bakso cukup enak dan terjangkau harga nya tetapi |
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kuah relatif kurang dan porsi tidak terlalu besar |
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pipeline_tag: text-classification |
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inference: true |
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model-index: |
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- name: SetFit with firqaaa/indo-sentence-bert-base |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: Unknown |
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type: unknown |
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split: test |
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metrics: |
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- type: accuracy |
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value: 0.7676767676767676 |
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name: Accuracy |
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- type: precision |
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value: 0.7676767676767676 |
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name: Precision |
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- type: recall |
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value: 0.7676767676767676 |
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name: Recall |
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- type: f1 |
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value: 0.7676767676767676 |
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name: F1 |
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--- |
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# SetFit with firqaaa/indo-sentence-bert-base for indonlu/smsa |
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## Author |
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**Kelompok 3 :** |
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- Muhammad Guntur Arfianto (20/459272/PA/19933) |
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- Putri Iqlima Miftahuddini (23/531392/NUGM/01467) |
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- Alan Kurniawan (23/531301/NUGM/01382) |
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [firqaaa/indo-sentence-bert-base](https://huggingface.co/firqaaa/indo-sentence-bert-base) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. |
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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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The dataset that was used for fine-tuning this model is [indonlu](https://huggingface.co/datasets/indonlp/indonlu), specifically its subset, SmSa dataset. |
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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 body:** [firqaaa/indo-sentence-bert-base](https://huggingface.co/firqaaa/indo-sentence-bert-base) |
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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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- **Maximum Sequence Length:** 512 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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### 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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| 2 | <ul><li>'hampir semua musala di stasiun jalur ke bogor kondisi nya juga terlalu sempit dan fasilitas wudhu yang kurang . bahkan sekelas stasiun besar bogor .'</li><li>'tangkap saja pak si penyanyi gadungan itu . kerjaan nya cuma fitnah di media sosial saja .'</li><li>'saya di cgv marvel city sby mau verifikasi sms redam , tapi di informasi telkomsel trobel , menyebalkan !'</li></ul> | |
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| 1 | <ul><li>'bapak berkumis lebat itu menyebrang menggunakan zebra cross'</li><li>'kaitan kalung cantik bahan perak / silver 925'</li><li>'duo red bull mendominasi latihan bebas pertama f1 gp singapura'</li></ul> | |
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| 0 | <ul><li>'jokowi sayang dan cinta kepada rakyat nya'</li><li>'nyaman banget kalau lagi nongkrong kenyang di warung upnormal . mulai dari pilihan menu nya yang serius banget digarap , dari pelayan2 nya yang kece , sampai ke interior nya yang super . rekomendasi banget deh kalau mau mengerjakan tugas , arisan , ulang tahun , reunian di sini .'</li><li>'rasanya lumayan . sambel nya juga enak . apalagi disajikan 3 macam model begitu . terus banyak pilihan sih sebenarnya mau makan apa di sini . mau gurame , mau kakap , bawal , kerang , cumi , udang . macem-macem deh . asal jangan pesan ikan kembung saja . tidak ada di sini .'</li></ul> | |
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## Evaluation |
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### Metrics |
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| Label | Accuracy | Precision | Recall | F1 | |
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|:--------|:---------|:----------|:-------|:-------| |
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| **all** | 0.7677 | 0.7677 | 0.7677 | 0.7677 | |
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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 SetFitModel |
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# Download from the 🤗 Hub |
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model = SetFitModel.from_pretrained("TRUEnder/setfit-indosentencebert-indonlusmsa-16-shot") |
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# Run inference |
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preds = model("liverpool sukses di kandang tottenham") |
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``` |
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## Training Details |
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### Training Set Metrics |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 16 | |
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| 1 | 16 | |
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| 2 | 16 | |
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### Training Hyperparameters |
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- batch_size: (16, 2) |
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- num_epochs: (6, 16) |
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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: False |
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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: True |
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### Training Results (Epoch-to-epoch) |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:-------:|:------:|:-------------:|:---------------:| |
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| **1.0** | **96** | **0.0009** | **0.1923** | |
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| 2.0 | 192 | 0.0002 | 0.1977 | |
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| 3.0 | 288 | 0.0002 | 0.2011 | |
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| 4.0 | 384 | 0.0002 | 0.203 | |
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| 5.0 | 480 | 0.0001 | 0.2042 | |
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| 6.0 | 576 | 0.0001 | 0.2046 | |
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* The bold row denotes the saved checkpoint. |
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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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- Transformers: 4.41.2 |
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- PyTorch: 2.3.0+cu121 |
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- Datasets: 2.19.2 |
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- Tokenizers: 0.19.1 |
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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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