---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: menarik yah sekin bagus nice:bagus menarik yah sekin bagus nice
- text: game nya bagus iklan nya kurangin yah:game nya bagus iklan nya kurangin yah
    pesawatnya kadang kadang berat ringan lompat perbaiki gak pencet lompat pencet
    gak lompat mohon perbaiki game nya pesan game nya bagus
- text: bagus suara bagus grafik bagus iklan game:game bagus suara bagus grafik bagus
    iklan game bikin sabar kalo stress main ya
- text: udah selesai muncul iklan game gak lanjutin:game nya bagus pas main mode practice
    levelnya udah selesai muncul iklan game gak lanjutin ngulang udah susah main ehh
    ulang
- text: game bagus sih kalo:game bagus sih kalo tamba level lai bole kalo katong seng
    loncat akang loncat deng kalo iklan seng tekang x seng tolong perbaiki ee
pipeline_tag: text-classification
inference: false
---

# 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:** [jetri20/ABSA_review_game_geometry-aspect](https://huggingface.co/jetri20/ABSA_review_game_geometry-aspect)
- **SetFitABSA Polarity Model:** [jetri20/ABSA_review_game_geometry-polarity](https://huggingface.co/jetri20/ABSA_review_game_geometry-polarity)
- **Maximum Sequence Length:** 8192 tokens
- **Number of Classes:** 2 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
|:--------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| negatif | <ul><li>'geometri dash kebanyakan level nya sulit mah:sih game level nya sulit banget level clutter funk susah nya ampun level 11 nya level sulit nya level 10 sulit menyerah sabar banting hp saking sulit nya ya geometri dash kebanyakan level nya sulit mah geometri dash game stress saking susah nya'</li><li>'emang sih level iklan muncul masuk level:iklan emang sih level iklan muncul masuk level mending kayak mah'</li><li>'game apasih sampe strees:game apasih sampe strees gitu final boss level the tower susahnya ampun kenapasih kalo hijau pas udah nya jatuh mati sih cube nya cuman 1 hp ya ngeselin sih'</li></ul> |
| positif | <ul><li>'game bagus tolong kasi:game bagus tolong kasi iklan'</li><li>'game bagus lemot level:game bagus lemot level dikit kedepan nya perbaiki kalo gk ku hapus karna lemot'</li><li>'gamenya seru susah banget:gamenya seru susah banget tower'</li></ul>                                                                                                                                                                                                                                                                                                                                                                               |

## 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(
    "jetri20/ABSA_review_game_geometry-aspect",
    "jetri20/ABSA_review_game_geometry-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   | 3   | 20.6854 | 70  |

| Label   | Training Sample Count |
|:--------|:----------------------|
| konflik | 0                     |
| negatif | 173                   |
| netral  | 0                     |
| positif | 148                   |

### Training Hyperparameters
- batch_size: (4, 4)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 5
- 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: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0012 | 1    | 0.2078        | -               |
| 0.0623 | 50   | 0.4009        | -               |
| 0.1245 | 100  | 0.0204        | -               |
| 0.1868 | 150  | 0.0249        | -               |
| 0.2491 | 200  | 0.0238        | -               |
| 0.3113 | 250  | 0.016         | -               |
| 0.3736 | 300  | 0.0114        | -               |
| 0.4359 | 350  | 0.2153        | -               |
| 0.4981 | 400  | 0.0032        | -               |
| 0.5604 | 450  | 0.004         | -               |
| 0.6227 | 500  | 0.0022        | -               |
| 0.6849 | 550  | 0.2173        | -               |
| 0.7472 | 600  | 0.0019        | -               |
| 0.8095 | 650  | 0.0007        | -               |
| 0.8717 | 700  | 0.0014        | -               |
| 0.9340 | 750  | 0.0007        | -               |
| 0.9963 | 800  | 0.0012        | -               |
| 1.0    | 803  | -             | 0.338           |

### Framework Versions
- Python: 3.10.13
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- spaCy: 3.7.5
- Transformers: 4.36.2
- PyTorch: 2.1.2
- Datasets: 2.19.2
- 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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