---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: sentence-transformers/all-mpnet-base-v2
metrics:
- accuracy
widget:
- text: gamenya seru bagus paket:gamenya seru bagus paket worth it gak lag mudah mainnya
    tugas hadiah bagus modenya sayangnya game kadang ngebug gapapa kasih
- text: tolong perbaiki analog nya pengaturan posisi:tolong perbaiki analog nya pengaturan
    posisi berpindah pindah
- text: visualisasi bagus segi graphic:visualisasi bagus segi graphic bagus ya game
    cocok sih mantra nya banyakin contoh mantra penghilang
- text: jaringan udah bagus game jaringan nya bagus:game nya udah bagus jaringan game
    nya bermasalah jaringan udah bagus game jaringan nya bagus mohon nambahin karakter
- text: kali game stuk loading server pakai jaringan:game bagus cma kendala kali game
    stuk loading server pakai jaringan wifi masuk jaringan jaringan bermasalah main
    game online lancar game susah akses tolong diperbaiki supercell detik bermain
    coc lancar masuk kendala
pipeline_tag: text-classification
inference: false
model-index:
- name: SetFit Polarity Model with sentence-transformers/all-mpnet-base-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.855072463768116
      name: Accuracy
---

# SetFit Polarity Model with sentence-transformers/all-mpnet-base-v2

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
- **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_mpnet_MiniLM-L6-aspect](https://huggingface.co/Funnyworld1412/ABSA_mpnet_MiniLM-L6-aspect)
- **SetFitABSA Polarity Model:** [Funnyworld1412/ABSA_mpnet_MiniLM-L6-polarity](https://huggingface.co/Funnyworld1412/ABSA_mpnet_MiniLM-L6-polarity)
- **Maximum Sequence Length:** 384 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
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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>'seru tolong diperbaiki pencarian lawan bermain ketemu player:kapada supercell game nya bagus seru tolong diperbaiki pencarian lawan bermain ketemu player trophy mahkotanya jaraknya dapet berpengaruh peleton akun perbedaan level'</li><li>'bugnya nakal banget y:bugnya nakal banget y coc cr aja sukanya ngebug pas match suka hitam match relog kalo udah relog lawan udah 1 2 mahkota kecewa sih bintang nya 1 aja bug nya diurus bintang lawannya kadang g setara levelnya dahlah gk suka banget kalo main 2 vs 2 temen suka banget afk coba fitur report'</li><li>'kadang g setara levelnya dahlah gk suka:bugnya nakal banget y coc cr aja sukanya ngebug pas match suka hitam match relog kalo udah relog lawan udah 1 2 mahkota kecewa sih bintang nya 1 aja bug nya diurus bintang lawannya kadang g setara levelnya dahlah gk suka banget kalo main 2 vs 2 temen suka banget afk coba fitur report'</li></ul> |
| positif | <ul><li>'kapada supercell game nya bagus seru:kapada supercell game nya bagus seru tolong diperbaiki pencarian lawan bermain ketemu player trophy mahkotanya jaraknya dapet berpengaruh peleton akun perbedaan level'</li><li>'fairrrr mending uninstall gamenya maen game yg:overall gamenya bagus pencarian match dikasih musuh yg levelnya levelku yg pertandingan fair menganggu kenyamanan pemainnya kalo nyariin musuh gapapa nyarinya kasih yg fair levelnya gaush buru buru ngasih yg gak fairrrr pas arena 4 udh dikasih musuh yg pletonnya 2 yg level 11 gak fairrrr mending uninstall gamenya maen game yg yg org gak fairr'</li><li>'gameplay menyenangkan pemain afk:gameplay menyenangkan pemain afk pertengahan menyerah 2vs2 mode mengganggu tolong tambahkan fitur lapor pemain'</li></ul>                                                                                                                         |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.8551   |

## 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_mpnet_MiniLM-L6-aspect",
    "Funnyworld1412/ABSA_mpnet_MiniLM-L6-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   | 28.3626 | 83  |

| Label   | Training Sample Count |
|:--------|:----------------------|
| negatif | 738                   |
| positif | 528                   |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0003 | 1    | 0.2658        | -               |
| 0.0158 | 50   | 0.148         | -               |
| 0.0316 | 100  | 0.3023        | -               |
| 0.0474 | 150  | 0.2508        | -               |
| 0.0632 | 200  | 0.2206        | -               |
| 0.0790 | 250  | 0.151         | -               |
| 0.0948 | 300  | 0.2409        | -               |
| 0.1106 | 350  | 0.2025        | -               |
| 0.1264 | 400  | 0.1535        | -               |
| 0.1422 | 450  | 0.1796        | -               |
| 0.1580 | 500  | 0.0979        | -               |
| 0.1738 | 550  | 0.2438        | -               |
| 0.1896 | 600  | 0.221         | -               |
| 0.2054 | 650  | 0.0416        | -               |
| 0.2212 | 700  | 0.2027        | -               |
| 0.2370 | 750  | 0.0484        | -               |
| 0.2528 | 800  | 0.0606        | -               |
| 0.2686 | 850  | 0.2689        | -               |
| 0.2844 | 900  | 0.0505        | -               |
| 0.3002 | 950  | 0.4393        | -               |
| 0.3160 | 1000 | 0.1642        | -               |
| 0.3318 | 1050 | 0.0824        | -               |
| 0.3476 | 1100 | 0.3209        | -               |
| 0.3633 | 1150 | 0.459         | -               |
| 0.3791 | 1200 | 0.0042        | -               |
| 0.3949 | 1250 | 0.2194        | -               |
| 0.4107 | 1300 | 0.0088        | -               |
| 0.4265 | 1350 | 0.0092        | -               |
| 0.4423 | 1400 | 0.2324        | -               |
| 0.4581 | 1450 | 0.0007        | -               |
| 0.4739 | 1500 | 0.0004        | -               |
| 0.4897 | 1550 | 0.0006        | -               |
| 0.5055 | 1600 | 0.0232        | -               |
| 0.5213 | 1650 | 0.2382        | -               |
| 0.5371 | 1700 | 0.0154        | -               |
| 0.5529 | 1750 | 0.0014        | -               |
| 0.5687 | 1800 | 0.0817        | -               |
| 0.5845 | 1850 | 0.0038        | -               |
| 0.6003 | 1900 | 0.2101        | -               |
| 0.6161 | 1950 | 0.0003        | -               |
| 0.6319 | 2000 | 0.0003        | -               |
| 0.6477 | 2050 | 0.0019        | -               |
| 0.6635 | 2100 | 0.0003        | -               |
| 0.6793 | 2150 | 0.0004        | -               |
| 0.6951 | 2200 | 0.0004        | -               |
| 0.7109 | 2250 | 0.0002        | -               |
| 0.7267 | 2300 | 0.2335        | -               |
| 0.7425 | 2350 | 0.0009        | -               |
| 0.7583 | 2400 | 0.0004        | -               |
| 0.7741 | 2450 | 0.018         | -               |
| 0.7899 | 2500 | 0.0002        | -               |
| 0.8057 | 2550 | 0.0003        | -               |
| 0.8215 | 2600 | 0.0628        | -               |
| 0.8373 | 2650 | 0.0002        | -               |
| 0.8531 | 2700 | 0.0006        | -               |
| 0.8689 | 2750 | 0.0003        | -               |
| 0.8847 | 2800 | 0.0002        | -               |
| 0.9005 | 2850 | 0.0008        | -               |
| 0.9163 | 2900 | 0.0001        | -               |
| 0.9321 | 2950 | 0.0001        | -               |
| 0.9479 | 3000 | 0.0003        | -               |
| 0.9637 | 3050 | 0.0001        | -               |
| 0.9795 | 3100 | 0.0007        | -               |
| 0.9953 | 3150 | 0.0003        | -               |
| 1.0    | 3165 | -             | 0.2173          |

### 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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