---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: dan lembut, pai yang dibawa pulang menjadi basah di:Karena kulitnya yang tipis
    dan lembut, pai yang dibawa pulang menjadi basah di dalam kotaknya.
- text: mungkin untuk mengkritik makanannya tersebut.:Dari makanan pembuka yang kami
    makan, dim sum, dan variasi makanannya lainnya, tidak mungkin untuk mengkritik
    makanannya tersebut.
- text: di sana untuk Spesial Sabtu Malam Setengah Harga, tetapi Selasa:Saya tidak
    ada di sana untuk Spesial Sabtu Malam Setengah Harga, tetapi Selasa Malam.
- text: dan mengatur ulang meja untuk enam orang:Di sebelah kanan saya, nyonya rumah
    berdiri di dekat seorang busboy dan mendesiskan rapido, rapido ketika dia mencoba
    membersihkan dan mengatur ulang meja untuk enam orang.
- text: Jika Anda menyukai makanannya dan nilai yang:Jika Anda menyukai makanannya
    dan nilai yang Anda dapatkan dari beberapa restoran Chinatown, ini bukan tempat
    untuk Anda.
pipeline_tag: text-classification
inference: false
model-index:
- name: SetFit Polarity Model
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.6568627450980392
      name: Accuracy
---

# 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
<!-- - **Sentence Transformer:** [Unknown](https://huggingface.co/unknown) -->
- **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:** [zeroix07/indo-setfit-absa-model-aspect](https://huggingface.co/zeroix07/indo-setfit-absa-model-aspect)
- **SetFitABSA Polarity Model:** [zeroix07/indo-setfit-absa-model-polarity](https://huggingface.co/zeroix07/indo-setfit-absa-model-polarity)
- **Maximum Sequence Length:** 8192 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
|:--------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| positif | <ul><li>'faktor penebusan adalah makanannya, yang berada:Agar benar-benar adil, satu-satunya faktor penebusan adalah makanannya, yang berada di atas rata-rata, tetapi tidak dapat menutupi semua kekurangan Teodora lainnya.'</li><li>'makanannya benar-benar luar biasa:makanannya benar-benar luar biasa, dengan dapur yang sangat mumpuni yang dengan bangga akan menyiapkan apa pun yang Anda ingin makan, baik itu ada di menu atau tidak.'</li><li>'biasa, dengan dapur yang sangat mumpuni:makanannya benar-benar luar biasa, dengan dapur yang sangat mumpuni yang dengan bangga akan menyiapkan apa pun yang Anda ingin makan, baik itu ada di menu atau tidak.'</li></ul> |
| netral  | <ul><li>'itu ada di menu atau tidak.:makanannya benar-benar luar biasa, dengan dapur yang sangat mumpuni yang dengan bangga akan menyiapkan apa pun yang Anda ingin makan, baik itu ada di menu atau tidak.'</li><li>'bisa mencicipi kedua daging tersebut).:Favorit kami yang disepakati adalah orrechiete dengan sosis dan ayam (biasanya para pelayan berbaik hati membagi hidangan menjadi dua sehingga Anda bisa mencicipi kedua daging tersebut).'</li><li>'jika Anda suka pizza berkulit tipis.:Pizza adalah yang terbaik jika Anda suka pizza berkulit tipis.'</li></ul>                                                                                                     |
| negatif | <ul><li>'yang masuk ke koki.:Semua uang digunakan untuk dekorasi interior, tidak ada satupun yang masuk ke koki.'</li><li>'masuk akal meskipun layanannya buruk.:Harganya masuk akal meskipun layanannya buruk.'</li><li>'mayones, lupa roti panggang kami, meninggalkan:Mereka tidak memiliki mayones, lupa roti panggang kami, meninggalkan bahan-bahan (yaitu keju dalam telur dadar), di bawah suhu panas dan daging terlalu matang sehingga hancur di piring ketika Anda menyentuhnya.'</li></ul>                                                                                                                                                                               |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.6569   |

## 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(
    "zeroix07/indo-setfit-absa-model-aspect",
    "zeroix07/indo-setfit-absa-model-polarity",
)
# Run inference
preds = model("The food was great, but the venue is just way too busy.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 5   | 21.6519 | 45  |

| Label   | Training Sample Count |
|:--------|:----------------------|
| konflik | 0                     |
| negatif | 48                    |
| netral  | 69                    |
| positif | 64                    |

### Training Hyperparameters
- batch_size: (6, 6)
- num_epochs: (1, 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: True
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0003 | 1    | 0.2985        | -               |
| 0.0139 | 50   | 0.14          | -               |
| 0.0278 | 100  | 0.0913        | -               |
| 0.0417 | 150  | 0.0447        | -               |
| 0.0556 | 200  | 0.0932        | -               |
| 0.0694 | 250  | 0.2864        | -               |
| 0.0833 | 300  | 0.2556        | -               |
| 0.0972 | 350  | 0.1447        | -               |
| 0.1111 | 400  | 0.0084        | -               |
| 0.125  | 450  | 0.003         | -               |
| 0.1389 | 500  | 0.0035        | -               |
| 0.1528 | 550  | 0.0074        | -               |
| 0.1667 | 600  | 0.0031        | -               |
| 0.1806 | 650  | 0.0014        | -               |
| 0.1944 | 700  | 0.002         | -               |
| 0.2083 | 750  | 0.0006        | -               |
| 0.2222 | 800  | 0.0005        | -               |
| 0.2361 | 850  | 0.0005        | -               |
| 0.25   | 900  | 0.0005        | -               |
| 0.2639 | 950  | 0.0015        | -               |
| 0.2778 | 1000 | 0.0007        | -               |
| 0.2917 | 1050 | 0.0006        | -               |
| 0.3056 | 1100 | 0.0006        | -               |
| 0.3194 | 1150 | 0.0007        | -               |
| 0.3333 | 1200 | 0.0091        | -               |
| 0.3472 | 1250 | 0.0004        | -               |
| 0.3611 | 1300 | 0.0003        | -               |
| 0.375  | 1350 | 0.0005        | -               |
| 0.3889 | 1400 | 0.0006        | -               |
| 0.4028 | 1450 | 0.0434        | -               |
| 0.4167 | 1500 | 0.0006        | -               |
| 0.4306 | 1550 | 0.0003        | -               |
| 0.4444 | 1600 | 0.0005        | -               |
| 0.4583 | 1650 | 0.0004        | -               |
| 0.4722 | 1700 | 0.0021        | -               |
| 0.4861 | 1750 | 0.0012        | -               |
| 0.5    | 1800 | 0.0004        | -               |
| 0.5139 | 1850 | 0.0005        | -               |
| 0.5278 | 1900 | 0.0004        | -               |
| 0.5417 | 1950 | 0.0003        | -               |
| 0.5556 | 2000 | 0.0003        | -               |
| 0.5694 | 2050 | 0.0005        | -               |
| 0.5833 | 2100 | 0.0004        | -               |
| 0.5972 | 2150 | 0.0004        | -               |
| 0.6111 | 2200 | 0.0005        | -               |
| 0.625  | 2250 | 0.0004        | -               |
| 0.6389 | 2300 | 0.0005        | -               |
| 0.6528 | 2350 | 0.0004        | -               |
| 0.6667 | 2400 | 0.0003        | -               |
| 0.6806 | 2450 | 0.0004        | -               |
| 0.6944 | 2500 | 0.0007        | -               |
| 0.7083 | 2550 | 0.0003        | -               |
| 0.7222 | 2600 | 0.0003        | -               |
| 0.7361 | 2650 | 0.101         | -               |
| 0.75   | 2700 | 0.0003        | -               |
| 0.7639 | 2750 | 0.0004        | -               |
| 0.7778 | 2800 | 0.0004        | -               |
| 0.7917 | 2850 | 0.0003        | -               |
| 0.8056 | 2900 | 0.0004        | -               |
| 0.8194 | 2950 | 0.0899        | -               |
| 0.8333 | 3000 | 0.0003        | -               |
| 0.8472 | 3050 | 0.0002        | -               |
| 0.8611 | 3100 | 0.0002        | -               |
| 0.875  | 3150 | 0.0003        | -               |
| 0.8889 | 3200 | 0.0002        | -               |
| 0.9028 | 3250 | 0.0003        | -               |
| 0.9167 | 3300 | 0.0004        | -               |
| 0.9306 | 3350 | 0.0003        | -               |
| 0.9444 | 3400 | 0.0003        | -               |
| 0.9583 | 3450 | 0.0547        | -               |
| 0.9722 | 3500 | 0.0003        | -               |
| 0.9861 | 3550 | 0.0004        | -               |
| 1.0    | 3600 | 0.0002        | -               |

### Framework Versions
- Python: 3.10.13
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- spaCy: 3.7.4
- Transformers: 4.36.2
- PyTorch: 2.1.2
- Datasets: 2.18.0
- 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}
}
```

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