---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: gulungan biasa menjadi gulungan luar dalam,:dibutuhkan biaya tambahan $2 untuk
    mengubah gulungan biasa menjadi gulungan luar dalam, tetapi gulungan tersebut
    berukuran lebih dari tiga kali lipat, dan itu bukan ha dari nasi.
- text: -a-bagel (baik di:ess-a-bagel (baik di sty-town atau midtown) sejauh ini merupakan
    bagel terbaik di ny.
- text: mahal wadah ini pengelola:ketika kami sedang duduk makan makanan di bawah
    standar, manajer mulai mencaci-maki beberapa karyawan karena meletakkan wadah
    bumbu yang salah dan menjelaskan kepada mereka betapa mahal wadah ini pengelola
- text: staf sangat akomodatif.:staf sangat akomodatif.
- text: layanan luar biasa melayani:makanan india yang enak dan layanan luar biasa
    melayani
pipeline_tag: text-classification
inference: false
base_model: BAAI/bge-m3
model-index:
- name: SetFit Polarity Model with BAAI/bge-m3
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.7898320472083522
      name: Accuracy
---

# SetFit Polarity Model with BAAI/bge-m3

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) 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:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3)
- **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:** [firqaaa/indo-setfit-absa-bert-base-restaurants-aspect](https://huggingface.co/firqaaa/indo-setfit-absa-bert-base-restaurants-aspect)
- **SetFitABSA Polarity Model:** [firqaaa/indo-setfit-absa-bert-base-restaurants-polarity](https://huggingface.co/firqaaa/indo-setfit-absa-bert-base-restaurants-polarity)
- **Maximum Sequence Length:** 8192 tokens
- **Number of Classes:** 4 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
|:--------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| netral  | <ul><li>'sangat kecil sehingga reservasi adalah suatu keharusan:restoran ini sangat kecil sehingga reservasi adalah suatu keharusan.'</li><li>'di dekat seorang busboy dan mendesiskan rapido:di sebelah kanan saya, nyo rumah berdiri di dekat seorang busboy dan mendesiskan rapido, rapido ketika dia mencoba membersihkan dan mengatur ulang meja untuk enam orang.'</li><li>'dan mengatur ulang meja untuk enam orang:di sebelah kanan saya, nyo rumah berdiri di dekat seorang busboy dan mendesiskan rapido, rapido ketika dia mencoba membersihkan dan mengatur ulang meja untuk enam orang.'</li></ul> |
| negatif | <ul><li>'untuk enam orang nyonya rumah:di sebelah kanan saya, nyo rumah berdiri di dekat seorang busboy dan mendesiskan rapido, rapido ketika dia mencoba membersihkan dan mengatur ulang meja untuk enam orang nyonya rumah'</li><li>'setelah berurusan dengan pizza di bawah standar:setelah berurusan dengan pizza di bawah standar di seluruh lingkungan kensington - saya menemukan sedikit tonino.'</li><li>'mereka tidak mejikan bir, anda harus:perhatikan bahwa mereka tidak mejikan bir, anda harus membawa sendiri.'</li></ul>                                                                       |
| positif | <ul><li>'saya tidak menyukai gnocchi.:saya tidak menyukai gnocchi.'</li><li>'dari makanan pembuka yang kami makan:dari makanan pembuka yang kami makan, dim sum, dan variasi makanan lain, tidak mungkin untuk mengkritik makanan tersebut.'</li><li>'kami makan, dim sum, dan variasi:dari makanan pembuka yang kami makan, dim sum, dan variasi makanan lain, tidak mungkin untuk mengkritik makanan tersebut.'</li></ul>                                                                                                                                                                                     |
| konflik | <ul><li>'makanan enak tapi jangan:makanan enak tapi jangan datang ke sini dengan perut kosong.'</li><li>'milik pihak rumah tagihan:namun, setiap perselisihan tentang ruu itu diimbangi oleh takaran minuman keras yang anda tuangkan sendiri yang merupakan milik pihak rumah tagihan'</li><li>'layanan meja bisa menjadi sedikit:layanan meja bisa menjadi sedikit lebih penuh perhatian tetapi sebagai seseorang yang juga bekerja di industri jasa, saya mengerti mereka sedang sibuk.'</li></ul>                                                                                                           |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.7898   |

## 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(
    "firqaaa/indo-setfit-absa-bert-base-restaurants-aspect",
    "firqaaa/indo-setfit-absa-bert-base-restaurants-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   | 20.6594 | 62  |

| Label   | Training Sample Count |
|:--------|:----------------------|
| konflik | 34                    |
| negatif | 323                   |
| netral  | 258                   |
| positif | 853                   |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- 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: True
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True

### Training Results
| Epoch      | Step     | Training Loss | Validation Loss |
|:----------:|:--------:|:-------------:|:---------------:|
| 0.0000     | 1        | 0.2345        | -               |
| 0.0006     | 50       | 0.2337        | -               |
| 0.0013     | 100      | 0.267         | -               |
| 0.0019     | 150      | 0.2335        | -               |
| 0.0025     | 200      | 0.2368        | -               |
| 0.0032     | 250      | 0.2199        | -               |
| 0.0038     | 300      | 0.2325        | -               |
| 0.0045     | 350      | 0.2071        | -               |
| 0.0051     | 400      | 0.2229        | -               |
| 0.0057     | 450      | 0.1153        | -               |
| 0.0064     | 500      | 0.1771        | 0.1846          |
| 0.0070     | 550      | 0.1612        | -               |
| 0.0076     | 600      | 0.1487        | -               |
| 0.0083     | 650      | 0.147         | -               |
| 0.0089     | 700      | 0.1982        | -               |
| 0.0096     | 750      | 0.1579        | -               |
| 0.0102     | 800      | 0.1148        | -               |
| 0.0108     | 850      | 0.1008        | -               |
| 0.0115     | 900      | 0.2035        | -               |
| 0.0121     | 950      | 0.1348        | -               |
| **0.0127** | **1000** | **0.0974**    | **0.182**       |
| 0.0134     | 1050     | 0.121         | -               |
| 0.0140     | 1100     | 0.1949        | -               |
| 0.0147     | 1150     | 0.2424        | -               |
| 0.0153     | 1200     | 0.0601        | -               |
| 0.0159     | 1250     | 0.0968        | -               |
| 0.0166     | 1300     | 0.0137        | -               |
| 0.0172     | 1350     | 0.034         | -               |
| 0.0178     | 1400     | 0.1217        | -               |
| 0.0185     | 1450     | 0.0454        | -               |
| 0.0191     | 1500     | 0.0397        | 0.2216          |
| 0.0198     | 1550     | 0.0226        | -               |
| 0.0204     | 1600     | 0.0939        | -               |
| 0.0210     | 1650     | 0.0537        | -               |
| 0.0217     | 1700     | 0.0566        | -               |
| 0.0223     | 1750     | 0.162         | -               |
| 0.0229     | 1800     | 0.0347        | -               |
| 0.0236     | 1850     | 0.103         | -               |
| 0.0242     | 1900     | 0.0615        | -               |
| 0.0249     | 1950     | 0.0589        | -               |
| 0.0255     | 2000     | 0.1668        | 0.2132          |
| 0.0261     | 2050     | 0.1809        | -               |
| 0.0268     | 2100     | 0.0579        | -               |
| 0.0274     | 2150     | 0.088         | -               |
| 0.0280     | 2200     | 0.1047        | -               |
| 0.0287     | 2250     | 0.1255        | -               |
| 0.0293     | 2300     | 0.0312        | -               |
| 0.0300     | 2350     | 0.0097        | -               |
| 0.0306     | 2400     | 0.0973        | -               |
| 0.0312     | 2450     | 0.0066        | -               |
| 0.0319     | 2500     | 0.0589        | 0.2591          |
| 0.0325     | 2550     | 0.0529        | -               |
| 0.0331     | 2600     | 0.0169        | -               |
| 0.0338     | 2650     | 0.0455        | -               |
| 0.0344     | 2700     | 0.0609        | -               |
| 0.0350     | 2750     | 0.1151        | -               |
| 0.0357     | 2800     | 0.0031        | -               |
| 0.0363     | 2850     | 0.0546        | -               |
| 0.0370     | 2900     | 0.0051        | -               |
| 0.0376     | 2950     | 0.0679        | -               |
| 0.0382     | 3000     | 0.0046        | 0.2646          |
| 0.0389     | 3050     | 0.011         | -               |
| 0.0395     | 3100     | 0.0701        | -               |
| 0.0401     | 3150     | 0.0011        | -               |
| 0.0408     | 3200     | 0.011         | -               |
| 0.0414     | 3250     | 0.0026        | -               |
| 0.0421     | 3300     | 0.0027        | -               |
| 0.0427     | 3350     | 0.0012        | -               |
| 0.0433     | 3400     | 0.0454        | -               |
| 0.0440     | 3450     | 0.0011        | -               |
| 0.0446     | 3500     | 0.0012        | 0.2602          |

* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.13
- SetFit: 1.0.3
- Sentence Transformers: 2.2.2
- spaCy: 3.7.4
- Transformers: 4.36.2
- PyTorch: 2.1.2+cu121
- Datasets: 2.16.1
- Tokenizers: 0.15.0

## 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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