---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
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
pipeline_tag: text-classification
inference: false
base_model: firqaaa/indo-sentence-bert-base
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
### 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 | - 'reservasi:restoran ini sangat kecil sehingga reservasi adalah suatu keharusan.'
- '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'
- '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.'
|
| no aspect | - 'restoran:restoran ini sangat kecil sehingga reservasi adalah suatu keharusan.'
- 'keharusan:restoran ini sangat kecil sehingga reservasi adalah suatu keharusan.'
- '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'
|
## 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.")
```
## 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}
}
```