---
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: 0.7171717171717171
      name: Accuracy
    - type: precision
      value: 0.7171717171717171
      name: Precision
    - type: recall
      value: 0.7171717171717171
      name: Recall
    - type: f1
      value: 0.7171717171717171
      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
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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>'nasi campur terkenal di bandung , info nya nasi campur pertama di bandung . mengandung b2 . rasa standar nasi campur . ada babi merah , babi panggang , sate babi manis , bakso goreng , jerohan manis . layanan tidak ramah , maklum masih generasi tua yang beraksi . lokasi makan lumayan bersih tapi tidak berat'</li><li>'saya di cgv marvel city sby mau verifikasi sms redam , tapi di informasi telkomsel trobel , menyebalkan !'</li><li>'indonesia itu tipe yang kalau sudah down pasti susah bangkit lagi'</li></ul> |
| 1     | <ul><li>'biru ada 4 , hijau ada 4 , merah ada 3 , kuning ada 3'</li><li>'baik terima kasih banyak'</li><li>'hai , ya , silakan kamu dapat mencoba lakukan pembayaran pdam di bukalapak .'</li></ul>                                                                                                                                                                                                                                                                                                                                      |
| 0     | <ul><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>'conggo gallrely cafe di bandung utara . cafe nya sih okok saja . yang menarik adalah produksi meja dengan kayu-kayu yang panjang dan tebal khusus untuk meja makan .'</li><li>'terima kasih mas'</li></ul>                   |

## Evaluation

### Metrics
| Label   | Accuracy | Precision | Recall | F1     |
|:--------|:---------|:----------|:-------|:-------|
| **all** | 0.7172   | 0.7172    | 0.7172 | 0.7172 |

## 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-8-shot")
# Run inference
preds = model("liverpool sukses di kandang tottenham")
```

<!--
### Downstream Use

*List how someone could finetune this model on their own dataset.*
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 3   | 22.7917 | 61  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 8                     |
| 1     | 8                     |
| 2     | 8                     |

### 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     | 24     | 0.0498        | 0.2293          |
| 2.0     | 48     | 0.0032        | 0.2033          |
| 3.0     | 72     | 0.0014        | 0.2021          |
| **4.0** | **96** | **0.001**     | **0.2009**      |
| 5.0     | 120    | 0.0009        | 0.2016          |
| 6.0     | 144    | 0.0008        | 0.2016          |

* 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->