---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: tiket:munkin karena masih baru gamenya masih banyak kurang seperti fitur sosial
    seperti room chat atau global chat untuk pemain saling berinteraksi menurut saya
    itu adalah hal yang sangat penting untuk di hadirkan dalam game ini terlalu susah
    mencari tiket dan koin emas dan upgrade karakter sehingga ketika tidak ada kupon
    peti pemain hanya dipaksa bermain tanpa dapat apa apa kecuali permata
- text: game:game nya bagus dan story yang seruu hanyaa ukuran yang terlalu besar
    jadi membutuhkan spesifikasi tinggi untuk bermain game ini
- text: udh:game nya udh seru banget tapi tinggal di tambah mode yang lain jangan
    hanya satu sebenarnya kalo mode hanya satu agak bosen masalah jaringan juga tiba
    tiba jelek udh itu aja yang lain bagus kok
- text: game:hal yang paling membuat tidak nyaman dalam bermain game ini adalah analog
    yang tidak diam ditempat atau fixed analog sehingga sangat tidak nyaman dalam
    memainkan game ini tolong banget nih developer supercell untuk menambah fitur
    pengaturan untuk mengatur fixed analog terimakasih
- text: sinyal down story:tolong di tambah fitur skip story karena tiap sinyal down
    story akan ke reset dari awal dan kami para player harus menyelesaikan story dari
    awal lagi dan itu membuat sakit kepala karena harus menyelesaikan story yang berjam
    jam karena kalau world story memaksa player harus menyelesaikan bintang 2 sebagai
    wujud kekecewaan saya
pipeline_tag: text-classification
inference: false
---

# SetFit Aspect 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 filtering aspect span candidates.

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 this SetFit model to filter these possible aspect span candidates.**
3. Use a 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:** [Funnyworld1412/review_game_absa-aspect](https://huggingface.co/Funnyworld1412/review_game_absa-aspect)
- **SetFitABSA Polarity Model:** [Funnyworld1412/review_game_absa-polarity](https://huggingface.co/Funnyworld1412/review_game_absa-polarity)
- **Maximum Sequence Length:** 512 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
|:----------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| aspect    | <ul><li>'story:saranku developer harus menciptakan sebuah story yang sangat menarik agar tidak kehilangan para player karena masalahnya banyak player yg tidak bertahan lama karena repetitif dan monoton tiap update size makin gede doang yg isinya cuma chest baru itupun sampah puzzle yg makin lama makin rumit tapi chest nya sampah story kebanyakan npc teyvat story utama punya mc dilupain gak difokusin map kalo udah kosong ya nyampah bikin size gede doang main 3 tahun rasanya monoton perkembangan buruk'</li><li>'reward:tolong ditambah lagi reward untuk gachanya untuk player lama kesulitan mendapatkan primo karena sudah tidak ada lagi quest dan eksplorasi juga sudah 100 dasar developer kapitalis game ini makin lama makin monoton dan tidak ramah untuk player lama yang kekurangan bahan untuk gacha karakter'</li><li>'event:cuman saran jangan terlalu pelit biar para player gak kabur sama game sebelah hadiah event quest di perbaiki udah nunggu event lama lama hadiah cuman gitu gitu aja sampek event selesai primogemnya buat 10 pull gacha gak cukup tingakat kesulitan beda hadiah sama saja lama lama yang main pada kabur kalok terlalu pelit dan 1 lagi jariang mohon di perbaiki untuk server indonya trimaksih'</li></ul>                                                                                                                                                                                                                                                                                                                             |
| no aspect | <ul><li>'saranku developer:saranku developer harus menciptakan sebuah story yang sangat menarik agar tidak kehilangan para player karena masalahnya banyak player yg tidak bertahan lama karena repetitif dan monoton tiap update size makin gede doang yg isinya cuma chest baru itupun sampah puzzle yg makin lama makin rumit tapi chest nya sampah story kebanyakan npc teyvat story utama punya mc dilupain gak difokusin map kalo udah kosong ya nyampah bikin size gede doang main 3 tahun rasanya monoton perkembangan buruk'</li><li>'story:saranku developer harus menciptakan sebuah story yang sangat menarik agar tidak kehilangan para player karena masalahnya banyak player yg tidak bertahan lama karena repetitif dan monoton tiap update size makin gede doang yg isinya cuma chest baru itupun sampah puzzle yg makin lama makin rumit tapi chest nya sampah story kebanyakan npc teyvat story utama punya mc dilupain gak difokusin map kalo udah kosong ya nyampah bikin size gede doang main 3 tahun rasanya monoton perkembangan buruk'</li><li>'player:saranku developer harus menciptakan sebuah story yang sangat menarik agar tidak kehilangan para player karena masalahnya banyak player yg tidak bertahan lama karena repetitif dan monoton tiap update size makin gede doang yg isinya cuma chest baru itupun sampah puzzle yg makin lama makin rumit tapi chest nya sampah story kebanyakan npc teyvat story utama punya mc dilupain gak difokusin map kalo udah kosong ya nyampah bikin size gede doang main 3 tahun rasanya monoton perkembangan buruk'</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(
    "Funnyworld1412/review_game_absa-aspect",
    "Funnyworld1412/review_game_absa-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   | 4   | 46.6389 | 94  |

| Label     | Training Sample Count |
|:----------|:----------------------|
| no aspect | 4189                  |
| aspect    | 990                   |

### Training Hyperparameters
- batch_size: (4, 4)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 1
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0004 | 1    | 0.4229        | -               |
| 0.0193 | 50   | 0.3888        | -               |
| 0.0386 | 100  | 0.268         | -               |
| 0.0579 | 150  | 0.3151        | -               |
| 0.0772 | 200  | 0.0962        | -               |
| 0.0965 | 250  | 0.2717        | -               |
| 0.1158 | 300  | 0.2986        | -               |
| 0.1351 | 350  | 0.1456        | -               |
| 0.1544 | 400  | 0.3291        | -               |
| 0.1737 | 450  | 0.4705        | -               |
| 0.1931 | 500  | 0.162         | -               |
| 0.2124 | 550  | 0.227         | -               |
| 0.2317 | 600  | 0.105         | -               |
| 0.2510 | 650  | 0.0809        | -               |
| 0.2703 | 700  | 0.0608        | -               |
| 0.2896 | 750  | 0.0804        | -               |
| 0.3089 | 800  | 0.5065        | -               |
| 0.3282 | 850  | 0.1868        | -               |
| 0.3475 | 900  | 0.2777        | -               |
| 0.3668 | 950  | 0.0483        | -               |
| 0.3861 | 1000 | 0.0174        | -               |
| 0.4054 | 1050 | 0.0361        | -               |
| 0.4247 | 1100 | 0.0208        | -               |
| 0.4440 | 1150 | 0.1162        | -               |
| 0.4633 | 1200 | 0.3258        | -               |
| 0.4826 | 1250 | 0.4762        | -               |
| 0.5019 | 1300 | 0.009         | -               |
| 0.5212 | 1350 | 0.0445        | -               |
| 0.5405 | 1400 | 0.4436        | -               |
| 0.5598 | 1450 | 0.036         | -               |
| 0.5792 | 1500 | 0.2706        | -               |
| 0.5985 | 1550 | 0.2454        | -               |
| 0.6178 | 1600 | 0.0539        | -               |
| 0.6371 | 1650 | 0.2127        | -               |
| 0.6564 | 1700 | 0.174         | -               |
| 0.6757 | 1750 | 0.0915        | -               |
| 0.6950 | 1800 | 0.3465        | -               |
| 0.7143 | 1850 | 0.2593        | -               |
| 0.7336 | 1900 | 0.205         | -               |
| 0.7529 | 1950 | 0.2425        | -               |
| 0.7722 | 2000 | 0.1797        | -               |
| 0.7915 | 2050 | 0.0083        | -               |
| 0.8108 | 2100 | 0.0973        | -               |
| 0.8301 | 2150 | 0.1209        | -               |
| 0.8494 | 2200 | 0.0049        | -               |
| 0.8687 | 2250 | 0.0028        | -               |
| 0.8880 | 2300 | 0.1165        | -               |
| 0.9073 | 2350 | 0.046         | -               |
| 0.9266 | 2400 | 0.2102        | -               |
| 0.9459 | 2450 | 0.1639        | -               |
| 0.9653 | 2500 | 0.0114        | -               |
| 0.9846 | 2550 | 0.3658        | -               |

### 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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