---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: kurangi bintang karna developer pelit dapat gold:ku kurangi bintang karna
    developer pelit dapat gold buat beli shop susah harus nunggu 6 jam untuk energi
    terisi full itupun hanya 3 biji sangat tidak memuaskan walau game cukup seru buat
    santai alangkah baiknya jika energy buat main di unlimit saja atau di update percepat
    isi energi buat play nya dan kendala di jaringan padahal sinyal wifi kencang tapi
    masih suka lag semoga cepat ada update supercell
- text: untuk grafik story dah bagus:untuk grafik story dah bagus cuman minus di sistem
    gacha dan artefak di tambah game nya tidak ramah f2p jadi banyak player yang kesusahan
    dalam mengumpulkan primogem itu doang sih
- text: gamenya asik sayangnya sinyal tiba tiba down:gamenya asik sayangnya sinyal
    tiba tiba down dan gk bisa login lagi
- text: bertarung melawan musuh joystick sering ngebug gak:saat bertarung melawan
    musuh joystick sering ngebug gak bisa di gerakin dan terkadang hanya jalan lurus
    saja tolong diperbaiki
- text: game ini 1 peti terbatas saya berharap:kekurangan game ini 1 peti terbatas
    saya berharap ini diubah menjadi seperti clash royale karena koin di game ini
    tidak bisa didapat setiap waktu kecuali top up 2 tier rank tolong di tambah sistem
    rank karena sistem rank akan membuat banyak player bersaing dan menambah keseruan
    karna ada tantangan seperti clash royale 3 sinyal bug sinyal mendadak lemah dan
    gk bisa masuk pertandingan karena game ini masih baru jadi wajar tapi tolong diperbaiki
    untuk kenyamanan pemain
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:** [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:** 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
|:---------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Negative | <ul><li>'kebanyakan npc teyvat story utama punya mc: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>'tolong ditambah lagi reward untuk gachanya untuk: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>'gitu aja sampek event selesai primogemnya buat: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>                                                                                                                                                                                                                                                                        |
| Positive | <ul><li>'gameplay nya memang menarik:gameplay nya memang menarik tapi story questnya bikin boring setiap lagi menyelesaikan quest kepala saya selalu frustasi karna dialog yang gak ngotak panjangnya mana gak bisa di skip selain itu developer selalu pelit untuk memberikan hadiah saya sudah tidak merasa senang lagi bermain game ini karna ke kikirannya puzzle nya dan questnya membuat otak saya pusing developer juga lama memberi respon saat ada bug harus tunggu viral dulu baru bug nya di benerin'</li><li>'mulai dari cerita story sound effect maupun:tolong jangan pelit lah hoyoverse sama pemain baru atau pemain yg lama yg main kembali karna pemain paling suka kalau banyak gratisan ntah itu artefak primoge character atau pun item karna jujur saja sebagai pemain baru saya merasa kurang puas sama gamenya apalagi buat upgrade character itu harus kumpulan item yg kebanyakan susah didapat bagi pemain baru itu saya kekurangan dari game ini selebihnya bagus mulai dari cerita story sound effect maupun tampilan didalam game yg lumayan bagus'</li><li>'dari cerita story sound effect maupun tampilan didalam:tolong jangan pelit lah hoyoverse sama pemain baru atau pemain yg lama yg main kembali karna pemain paling suka kalau banyak gratisan ntah itu artefak primoge character atau pun item karna jujur saja sebagai pemain baru saya merasa kurang puas sama gamenya apalagi buat upgrade character itu harus kumpulan item yg kebanyakan susah didapat bagi pemain baru itu saya kekurangan dari game ini selebihnya bagus mulai dari cerita story sound effect maupun tampilan didalam game yg lumayan bagus'</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.")
```

<!--
### 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   | 7   | 43.8444 | 96  |

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

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0020 | 1    | 0.1121        | -               |
| 0.1010 | 50   | 0.0306        | -               |
| 0.2020 | 100  | 0.0186        | -               |
| 0.3030 | 150  | 0.0862        | -               |
| 0.4040 | 200  | 0.0089        | -               |
| 0.5051 | 250  | 0.0037        | -               |
| 0.6061 | 300  | 0.0027        | -               |
| 0.7071 | 350  | 0.0154        | -               |
| 0.8081 | 400  | 0.238         | -               |
| 0.9091 | 450  | 0.0095        | -               |

### 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}
}
```

<!--
## 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.*
-->