---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: firqaaa/indo-setfit-absa-bert-base-restaurants-polarity
metrics:
- accuracy
widget:
- text: game sebenernya bagus storynya mapnya grafis pemandangan:game sebenernya bagus
    storynya mapnya grafis pemandangan alamnya bagus kesalahan game sistem farmingnya
    yg bikin frustasi player grindingnya bikin pusing yg lakuin ya sih maaf ya game
    ga sebagus
- text: story grafik kecewa reward player gak berubah:game nya bagus story grafik
    kecewa reward player gak berubah rilis nambah sih apresiasi player bermain game
    contoh nya 3x pull tarikan gacha mengenang 3 bermain game jujur mengecewakan player
    gak anggap download pikir karna game kikir
- text: hoyoverse ngurusin ni game seru d:game debes yg gwe temuin ampe gameplay seru
    story mantul map luas bgt grapik salutlah ama hoyoverse coba klo hoyoverse ngurusin
    ni game seru d
- text: penggunaan data terlalau besarr anjj:penggunaan data terlalau besarr anjj
    sekalii
- text: story rate 8 10:story rate 8 10 permainan yng bagus gacha bansos 10 100
pipeline_tag: text-classification
inference: false
---

# SetFit Polarity Model with firqaaa/indo-setfit-absa-bert-base-restaurants-polarity

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [firqaaa/indo-setfit-absa-bert-base-restaurants-polarity](https://huggingface.co/firqaaa/indo-setfit-absa-bert-base-restaurants-polarity) 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. 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 body:** [firqaaa/indo-setfit-absa-bert-base-restaurants-polarity](https://huggingface.co/firqaaa/indo-setfit-absa-bert-base-restaurants-polarity)
- **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/ABSA_review_game_genshin_impact-aspect](https://huggingface.co/Funnyworld1412/ABSA_review_game_genshin_impact-aspect)
- **SetFitABSA Polarity Model:** [Funnyworld1412/ABSA_review_game_genshin_impact-polarity](https://huggingface.co/Funnyworld1412/ABSA_review_game_genshin_impact-polarity)
- **Maximum Sequence Length:** 8192 tokens
- **Number of Classes:** 2 classes
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### 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>'kebanyakan npc teyvat story utama mc dilupain:saranku developer menciptakan story menarik kehilangan player player yg bertahan repetitif monoton update size gede doang yg isinya chest itupun sampah puzzle yg rumit chest nya sampah story kebanyakan npc teyvat story utama mc dilupain gak difokusin map kalo udah kosong ya nyampah bikin size gede doang main 3 monoton perkembangan buruk'</li><li>'tolong ditambah reward gachanya player kesulitan:tolong ditambah reward gachanya player kesulitan primo quest eksplorasi 100 dasar developer kapitalis game monoton ramah player kekurangan bahan gacha karakter'</li><li>'gitu aja sampek event selesai primogemnya 10:cuman saran pelit biar player gak kabur game sebelah hadiah event quest perbaiki udah nunggu event hadiah cuman gitu gitu aja sampek event selesai primogemnya 10 pull gacha gak tingakat kesulitan beda hadiah main kabur kalok pelit 1 jariang mohon perbaiki server indonya trimaksih'</li></ul>                                                                                     |
| positif | <ul><li>'gameplay nya menarik story:gameplay nya menarik story questnya bikin boring menyelesaikan quest kepala frustasi karna dialog gak ngotak panjangnya gak skip developer pelit hadiah senang bermain game karna kikirannya puzzle nya questnya otak pusing developer respon bug tunggu viral bug nya benerin'</li><li>'selebihnya bagus cerita story sound effect tampilan:tolong pelit hoyoverse pemain pemain yg yg main karna pemain suka gratisan ntah artefak primoge character item karna jujur pemain puas gamenya upgrade character kumpulan item yg kebanyakan susah pemain kekurangan game selebihnya bagus cerita story sound effect tampilan didalam game yg lumayan bagus'</li><li>'bagus cerita story sound effect tampilan didalam game:tolong pelit hoyoverse pemain pemain yg yg main karna pemain suka gratisan ntah artefak primoge character item karna jujur pemain puas gamenya upgrade character kumpulan item yg kebanyakan susah pemain kekurangan game selebihnya bagus cerita story sound effect 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/ABSA_review_game_genshin_impact-aspect",
    "Funnyworld1412/ABSA_review_game_genshin_impact-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   | 7   | 31.0185 | 70  |

| Label   | Training Sample Count |
|:--------|:----------------------|
| konflik | 0                     |
| negatif | 208                   |
| netral  | 0                     |
| positif | 116                   |

### Training Hyperparameters
- batch_size: (4, 4)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 10
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0006 | 1    | 0.2317        | -               |
| 0.0309 | 50   | 0.0253        | -               |
| 0.0617 | 100  | 0.0008        | -               |
| 0.0926 | 150  | 0.4789        | -               |
| 0.1235 | 200  | 0.0215        | -               |
| 0.1543 | 250  | 0.0012        | -               |
| 0.1852 | 300  | 0.1843        | -               |
| 0.2160 | 350  | 0.0014        | -               |
| 0.2469 | 400  | 0.0013        | -               |
| 0.2778 | 450  | 0.0012        | -               |
| 0.3086 | 500  | 0.0016        | -               |
| 0.3395 | 550  | 0.0004        | -               |
| 0.3704 | 600  | 0.0006        | -               |
| 0.4012 | 650  | 0.0017        | -               |
| 0.4321 | 700  | 0.0012        | -               |
| 0.4630 | 750  | 0.0005        | -               |
| 0.4938 | 800  | 0.0003        | -               |
| 0.5247 | 850  | 0.0004        | -               |
| 0.5556 | 900  | 0.0006        | -               |
| 0.5864 | 950  | 0.2368        | -               |
| 0.6173 | 1000 | 0.0003        | -               |
| 0.6481 | 1050 | 0.0005        | -               |
| 0.6790 | 1100 | 0.0006        | -               |
| 0.7099 | 1150 | 0.0008        | -               |
| 0.7407 | 1200 | 0.0924        | -               |
| 0.7716 | 1250 | 0.0003        | -               |
| 0.8025 | 1300 | 0.0003        | -               |
| 0.8333 | 1350 | 0.0003        | -               |
| 0.8642 | 1400 | 0.0006        | -               |
| 0.8951 | 1450 | 0.0005        | -               |
| 0.9259 | 1500 | 0.0004        | -               |
| 0.9568 | 1550 | 0.0003        | -               |
| 0.9877 | 1600 | 0.0002        | -               |
| 1.0    | 1620 | -             | 0.1328          |

### 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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