setfit safetensors bert absa sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:firqaaa/indo-sentence-bert-base base_model:finetune:firqaaa/indo-sentence-bert-base model-index region:us
---
base_model: firqaaa/indo-sentence-bert-base
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: timur:unggul di atas tetangga di jalan 6 timur, taj mahal juga sangat sebanding,
    dalam kualitas makanan, dengan baluchi yang terlalu dipuji (dan kurang layak).
- text: makanan:saya sangat merekomendasikan cafe st bart's untuk makanan mereka,
    suasana dan layanan yang luar biasa melayani
- text: terong parmesan:parmesan terung juga enak, dan teman saya yang besar di manhattan
    metakan bahwa tidak ada orang yang pantas mendapatkan ziti panggang yang lebih
    enak dengan saus daging terong parmesan
- text: tuna lelehan:kami memesan tuna lelehan - itu datang dengan keluar keju yang
    ha membuat sandwich tuna daging tuna
- text: manhattan metakan:parmesan terung juga enak, dan teman saya yang besar di
    manhattan metakan bahwa tidak ada orang yang pantas mendapatkan ziti panggang
    yang lebih enak dengan saus daging ziti panggang dengan saus daging
inference: false
model-index:
- name: SetFit Aspect Model with firqaaa/indo-sentence-bert-base
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.9087072065030483
      name: Accuracy
---

# SetFit Aspect Model with firqaaa/indo-sentence-bert-base

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-sentence-bert-base](https://huggingface.co/firqaaa/indo-sentence-bert-base) 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 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 body:** [firqaaa/indo-sentence-bert-base](https://huggingface.co/firqaaa/indo-sentence-bert-base)
- **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:** 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>'reservasi:restoran ini sangat kecil sehingga reservasi adalah suatu keharusan.'</li><li>'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>'busboy: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> |
| no aspect | <ul><li>'restoran:restoran ini sangat kecil sehingga reservasi adalah suatu keharusan.'</li><li>'keharusan:restoran ini sangat kecil sehingga reservasi adalah suatu keharusan.'</li><li>'sebelah kanan: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></ul>                                                                                                   |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9087   |

## 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/setfit-indo-absa-restaurants-aspect",
    "firqaaa/setfit-indo-absa-restaurants-polarity",
)
# Run inference
preds = model("The food was great, but the venue is just way too busy.")
```

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*List how someone could finetune this model on their own dataset.*
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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 2   | 19.7819 | 59  |

| Label     | Training Sample Count |
|:----------|:----------------------|
| no aspect | 2939                  |
| aspect    | 1468                  |

### 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        | 0.3135        | -               |
| 0.0001     | 50       | 0.3401        | -               |
| 0.0001     | 100      | 0.3212        | -               |
| 0.0002     | 150      | 0.3641        | -               |
| 0.0003     | 200      | 0.3317        | -               |
| 0.0004     | 250      | 0.2809        | -               |
| 0.0004     | 300      | 0.2446        | -               |
| 0.0005     | 350      | 0.284         | -               |
| 0.0006     | 400      | 0.3257        | -               |
| 0.0007     | 450      | 0.2996        | -               |
| 0.0007     | 500      | 0.209         | 0.295           |
| 0.0008     | 550      | 0.2121        | -               |
| 0.0009     | 600      | 0.2204        | -               |
| 0.0010     | 650      | 0.3023        | -               |
| 0.0010     | 700      | 0.3253        | -               |
| 0.0011     | 750      | 0.233         | -               |
| 0.0012     | 800      | 0.3131        | -               |
| 0.0013     | 850      | 0.2873        | -               |
| 0.0013     | 900      | 0.2028        | -               |
| 0.0014     | 950      | 0.2608        | -               |
| 0.0015     | 1000     | 0.2842        | 0.2696          |
| 0.0016     | 1050     | 0.2297        | -               |
| 0.0016     | 1100     | 0.266         | -               |
| 0.0017     | 1150     | 0.2771        | -               |
| 0.0018     | 1200     | 0.2347        | -               |
| 0.0019     | 1250     | 0.2539        | -               |
| 0.0019     | 1300     | 0.3409        | -               |
| 0.0020     | 1350     | 0.2925        | -               |
| 0.0021     | 1400     | 0.2608        | -               |
| 0.0021     | 1450     | 0.2792        | -               |
| 0.0022     | 1500     | 0.261         | 0.2636          |
| 0.0023     | 1550     | 0.2596        | -               |
| 0.0024     | 1600     | 0.2563        | -               |
| 0.0024     | 1650     | 0.2329        | -               |
| 0.0025     | 1700     | 0.2954        | -               |
| 0.0026     | 1750     | 0.3329        | -               |
| 0.0027     | 1800     | 0.2138        | -               |
| 0.0027     | 1850     | 0.2591        | -               |
| 0.0028     | 1900     | 0.268         | -               |
| 0.0029     | 1950     | 0.2144        | -               |
| 0.0030     | 2000     | 0.2361        | 0.2586          |
| 0.0030     | 2050     | 0.2322        | -               |
| 0.0031     | 2100     | 0.2646        | -               |
| 0.0032     | 2150     | 0.2018        | -               |
| 0.0033     | 2200     | 0.2579        | -               |
| 0.0033     | 2250     | 0.2501        | -               |
| 0.0034     | 2300     | 0.2657        | -               |
| 0.0035     | 2350     | 0.2272        | -               |
| 0.0036     | 2400     | 0.2383        | -               |
| 0.0036     | 2450     | 0.2615        | -               |
| 0.0037     | 2500     | 0.2818        | 0.2554          |
| 0.0038     | 2550     | 0.2616        | -               |
| 0.0039     | 2600     | 0.2225        | -               |
| 0.0039     | 2650     | 0.2749        | -               |
| 0.0040     | 2700     | 0.2572        | -               |
| 0.0041     | 2750     | 0.2729        | -               |
| 0.0041     | 2800     | 0.2559        | -               |
| 0.0042     | 2850     | 0.2363        | -               |
| 0.0043     | 2900     | 0.2518        | -               |
| 0.0044     | 2950     | 0.1948        | -               |
| 0.0044     | 3000     | 0.2842        | 0.2538          |
| 0.0045     | 3050     | 0.2243        | -               |
| 0.0046     | 3100     | 0.2186        | -               |
| 0.0047     | 3150     | 0.2829        | -               |
| 0.0047     | 3200     | 0.2101        | -               |
| 0.0048     | 3250     | 0.2156        | -               |
| 0.0049     | 3300     | 0.2539        | -               |
| 0.0050     | 3350     | 0.3005        | -               |
| 0.0050     | 3400     | 0.2699        | -               |
| 0.0051     | 3450     | 0.2431        | -               |
| 0.0052     | 3500     | 0.2931        | 0.2515          |
| 0.0053     | 3550     | 0.2032        | -               |
| 0.0053     | 3600     | 0.2451        | -               |
| 0.0054     | 3650     | 0.2419        | -               |
| 0.0055     | 3700     | 0.2267        | -               |
| 0.0056     | 3750     | 0.2945        | -               |
| 0.0056     | 3800     | 0.2689        | -               |
| 0.0057     | 3850     | 0.2596        | -               |
| 0.0058     | 3900     | 0.2978        | -               |
| 0.0059     | 3950     | 0.2876        | -               |
| 0.0059     | 4000     | 0.2484        | 0.2482          |
| 0.0060     | 4050     | 0.2698        | -               |
| 0.0061     | 4100     | 0.2155        | -               |
| 0.0061     | 4150     | 0.2474        | -               |
| 0.0062     | 4200     | 0.2683        | -               |
| 0.0063     | 4250     | 0.2979        | -               |
| 0.0064     | 4300     | 0.2866        | -               |
| 0.0064     | 4350     | 0.2604        | -               |
| 0.0065     | 4400     | 0.1989        | -               |
| 0.0066     | 4450     | 0.2708        | -               |
| 0.0067     | 4500     | 0.2705        | 0.2407          |
| 0.0067     | 4550     | 0.2144        | -               |
| 0.0068     | 4600     | 0.2503        | -               |
| 0.0069     | 4650     | 0.2193        | -               |
| 0.0070     | 4700     | 0.1796        | -               |
| 0.0070     | 4750     | 0.2384        | -               |
| 0.0071     | 4800     | 0.1933        | -               |
| 0.0072     | 4850     | 0.2248        | -               |
| 0.0073     | 4900     | 0.22          | -               |
| 0.0073     | 4950     | 0.2052        | -               |
| 0.0074     | 5000     | 0.2314        | 0.224           |
| 0.0075     | 5050     | 0.2279        | -               |
| 0.0076     | 5100     | 0.2198        | -               |
| 0.0076     | 5150     | 0.2332        | -               |
| 0.0077     | 5200     | 0.1666        | -               |
| 0.0078     | 5250     | 0.1949        | -               |
| 0.0079     | 5300     | 0.1802        | -               |
| 0.0079     | 5350     | 0.2496        | -               |
| 0.0080     | 5400     | 0.2399        | -               |
| 0.0081     | 5450     | 0.2042        | -               |
| 0.0082     | 5500     | 0.1859        | 0.2077          |
| 0.0082     | 5550     | 0.2216        | -               |
| 0.0083     | 5600     | 0.1227        | -               |
| 0.0084     | 5650     | 0.2351        | -               |
| 0.0084     | 5700     | 0.2735        | -               |
| 0.0085     | 5750     | 0.1008        | -               |
| 0.0086     | 5800     | 0.1568        | -               |
| 0.0087     | 5850     | 0.1211        | -               |
| 0.0087     | 5900     | 0.0903        | -               |
| 0.0088     | 5950     | 0.1473        | -               |
| 0.0089     | 6000     | 0.1167        | 0.1877          |
| 0.0090     | 6050     | 0.206         | -               |
| 0.0090     | 6100     | 0.2392        | -               |
| 0.0091     | 6150     | 0.116         | -               |
| 0.0092     | 6200     | 0.1493        | -               |
| 0.0093     | 6250     | 0.1373        | -               |
| 0.0093     | 6300     | 0.1163        | -               |
| 0.0094     | 6350     | 0.0669        | -               |
| 0.0095     | 6400     | 0.0756        | -               |
| 0.0096     | 6450     | 0.0788        | -               |
| 0.0096     | 6500     | 0.1816        | 0.1838          |
| 0.0097     | 6550     | 0.1288        | -               |
| 0.0098     | 6600     | 0.0946        | -               |
| 0.0099     | 6650     | 0.1374        | -               |
| 0.0099     | 6700     | 0.2167        | -               |
| 0.0100     | 6750     | 0.0759        | -               |
| 0.0101     | 6800     | 0.1543        | -               |
| 0.0102     | 6850     | 0.0573        | -               |
| 0.0102     | 6900     | 0.1169        | -               |
| 0.0103     | 6950     | 0.0294        | -               |
| **0.0104** | **7000** | **0.1241**    | **0.1769**      |
| 0.0104     | 7050     | 0.0803        | -               |
| 0.0105     | 7100     | 0.0139        | -               |
| 0.0106     | 7150     | 0.01          | -               |
| 0.0107     | 7200     | 0.0502        | -               |
| 0.0107     | 7250     | 0.0647        | -               |
| 0.0108     | 7300     | 0.0117        | -               |
| 0.0109     | 7350     | 0.0894        | -               |
| 0.0110     | 7400     | 0.0101        | -               |
| 0.0110     | 7450     | 0.0066        | -               |
| 0.0111     | 7500     | 0.0347        | 0.1899          |
| 0.0112     | 7550     | 0.0893        | -               |
| 0.0113     | 7600     | 0.0127        | -               |
| 0.0113     | 7650     | 0.1285        | -               |
| 0.0114     | 7700     | 0.0049        | -               |
| 0.0115     | 7750     | 0.0571        | -               |
| 0.0116     | 7800     | 0.0068        | -               |
| 0.0116     | 7850     | 0.0586        | -               |
| 0.0117     | 7900     | 0.0788        | -               |
| 0.0118     | 7950     | 0.0655        | -               |
| 0.0119     | 8000     | 0.0052        | 0.1807          |
| 0.0119     | 8050     | 0.0849        | -               |
| 0.0120     | 8100     | 0.0133        | -               |
| 0.0121     | 8150     | 0.0445        | -               |
| 0.0122     | 8200     | 0.0118        | -               |
| 0.0122     | 8250     | 0.0118        | -               |
| 0.0123     | 8300     | 0.063         | -               |
| 0.0124     | 8350     | 0.0751        | -               |
| 0.0124     | 8400     | 0.058         | -               |
| 0.0125     | 8450     | 0.002         | -               |
| 0.0126     | 8500     | 0.0058        | 0.1804          |
| 0.0127     | 8550     | 0.0675        | -               |
| 0.0127     | 8600     | 0.0067        | -               |
| 0.0128     | 8650     | 0.0087        | -               |
| 0.0129     | 8700     | 0.0028        | -               |
| 0.0130     | 8750     | 0.0626        | -               |
| 0.0130     | 8800     | 0.0563        | -               |
| 0.0131     | 8850     | 0.0012        | -               |
| 0.0132     | 8900     | 0.0067        | -               |
| 0.0133     | 8950     | 0.0011        | -               |
| 0.0133     | 9000     | 0.0105        | 0.189           |
| 0.0134     | 9050     | 0.101         | -               |
| 0.0135     | 9100     | 0.1162        | -               |
| 0.0136     | 9150     | 0.0593        | -               |
| 0.0136     | 9200     | 0.0004        | -               |
| 0.0137     | 9250     | 0.0012        | -               |
| 0.0138     | 9300     | 0.0022        | -               |
| 0.0139     | 9350     | 0.0033        | -               |
| 0.0139     | 9400     | 0.0025        | -               |
| 0.0140     | 9450     | 0.0578        | -               |
| 0.0141     | 9500     | 0.0012        | 0.1967          |

* 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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