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---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: firqaaa/indo-sentence-bert-base
metrics:
- accuracy
- precision
- recall
- f1
widget:
- text: halaman 97 - 128 tidak ada , diulang halaman 65 - 96 , pembelian hari minggu
tanggal 24 desember sore sekitar jam 4 pembayaran menggunakan kartu atm bri bersamaan
dengan buku the puppeteer dan sirkus pohon
- text: liverpool sukses di kandang tottenham
- text: hai angga , untuk penerbitan tiket reschedule diharuskan melakukan pembayaran
dulu ya .
- text: sedih kalau umat diprovokasi supaya saling membenci .
- text: berada di lokasi strategis jalan merdeka , berseberangan agak ke samping bandung
indah plaza , tapat sebelah kanan jalan sebelum traffic light , parkir mobil cukup
luas . saus bumbu dan lain-lain disediakan cukup lengkap di lantai bawah . di
lantai atas suasana agak sepi . bakso cukup enak dan terjangkau harga nya tetapi
kuah relatif kurang dan porsi tidak terlalu besar
pipeline_tag: text-classification
inference: true
model-index:
- name: SetFit with firqaaa/indo-sentence-bert-base
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.7676767676767676
name: Accuracy
- type: precision
value: 0.7676767676767676
name: Precision
- type: recall
value: 0.7676767676767676
name: Recall
- type: f1
value: 0.7676767676767676
name: F1
---
# SetFit with firqaaa/indo-sentence-bert-base
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.
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.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [firqaaa/indo-sentence-bert-base](https://huggingface.co/firqaaa/indo-sentence-bert-base)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 3 classes
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### 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 |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 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> |
| 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> |
| 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> |
## Evaluation
### Metrics
| Label | Accuracy | Precision | Recall | F1 |
|:--------|:---------|:----------|:-------|:-------|
| **all** | 0.7677 | 0.7677 | 0.7677 | 0.7677 |
## 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 SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("TRUEnder/setfit-indosentencebert-indonlusmsa-16-shot")
# Run inference
preds = model("liverpool sukses di kandang tottenham")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 1 | 21.4792 | 64 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 16 |
| 1 | 16 |
| 2 | 16 |
### Training Hyperparameters
- batch_size: (16, 2)
- num_epochs: (6, 16)
- 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: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-------:|:------:|:-------------:|:---------------:|
| **1.0** | **96** | **0.0009** | **0.1923** |
| 2.0 | 192 | 0.0002 | 0.1977 |
| 3.0 | 288 | 0.0002 | 0.2011 |
| 4.0 | 384 | 0.0002 | 0.203 |
| 5.0 | 480 | 0.0001 | 0.2042 |
| 6.0 | 576 | 0.0001 | 0.2046 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.0+cu121
- Datasets: 2.19.2
- Tokenizers: 0.19.1
## 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}
}
```
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