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---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: firqaaa/indo-setfit-absa-bert-base-restaurants-aspect
metrics:
- accuracy
widget:
- text: basi:jujur game bagus n refresing banget klo pas masuk region sampe archon
quest kelar disayangkan basi utk kontennya diulang ulang ampe gila bener bener
membosankan login n menghabiskan resin temen ku udh gak main bosen emng marketing
game player biar ngerasain serunya game diawal tambahin konten end game gk kasih
resin tambahan biar yg dikerjain
- text: karakter:game kikir pelit medit sumpah gacha ngak dapet tahan top up game
kemarin ngak kasih 1 karakter ngebahagiain player jgn download kalo mental aman
- text: loading:nya loading screen element sampe 4 kali game sih ajg niat bikin game
ga
- text: b5 kasih b5 playstore:hadiahnya plis karakter gratis b5 kasih b5 playstore
- text: gb:update 10 gb udah 30 ditambah 10gb males
pipeline_tag: text-classification
inference: false
---
# SetFit Aspect Model with firqaaa/indo-setfit-absa-bert-base-restaurants-aspect
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-aspect](https://huggingface.co/firqaaa/indo-setfit-absa-bert-base-restaurants-aspect) 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-setfit-absa-bert-base-restaurants-aspect](https://huggingface.co/firqaaa/indo-setfit-absa-bert-base-restaurants-aspect)
- **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:** 512 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 |
|:----------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| aspect | <ul><li>'story: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>'reward:tolong ditambah reward gachanya player kesulitan primo quest eksplorasi 100 dasar developer kapitalis game monoton ramah player kekurangan bahan gacha karakter'</li><li>'event: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> |
| no aspect | <ul><li>'saranku developer: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>'story: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>'kehilangan player player: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></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 | 4 | 31.2629 | 70 |
| Label | Training Sample Count |
|:----------|:----------------------|
| no aspect | 1049 |
| aspect | 324 |
### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0001 | 1 | 0.0089 | - |
| 0.0073 | 50 | 0.7206 | - |
| 0.0146 | 100 | 0.399 | - |
| 0.0218 | 150 | 0.0596 | - |
| 0.0291 | 200 | 0.3335 | - |
| 0.0364 | 250 | 0.1854 | - |
| 0.0437 | 300 | 0.0708 | - |
| 0.0510 | 350 | 0.0161 | - |
| 0.0583 | 400 | 0.3364 | - |
| 0.0655 | 450 | 0.0949 | - |
| 0.0728 | 500 | 0.1021 | - |
| 0.0801 | 550 | 0.3917 | - |
| 0.0874 | 600 | 0.0707 | - |
| 0.0947 | 650 | 0.3885 | - |
| 0.1020 | 700 | 0.046 | - |
| 0.1092 | 750 | 0.001 | - |
| 0.1165 | 800 | 0.0024 | - |
| 0.1238 | 850 | 0.2384 | - |
| 0.1311 | 900 | 0.0215 | - |
| 0.1384 | 950 | 0.2283 | - |
| 0.1457 | 1000 | 0.4564 | - |
| 0.1529 | 1050 | 0.0017 | - |
| 0.1602 | 1100 | 0.0612 | - |
| 0.1675 | 1150 | 0.2325 | - |
| 0.1748 | 1200 | 0.0568 | - |
| 0.1821 | 1250 | 0.0096 | - |
| 0.1894 | 1300 | 0.2803 | - |
| 0.1966 | 1350 | 0.0056 | - |
| 0.2039 | 1400 | 0.0107 | - |
| 0.2112 | 1450 | 0.0042 | - |
| 0.2185 | 1500 | 0.0636 | - |
| 0.2258 | 1550 | 0.0356 | - |
| 0.2331 | 1600 | 0.2264 | - |
| 0.2403 | 1650 | 0.2335 | - |
| 0.2476 | 1700 | 0.201 | - |
| 0.2549 | 1750 | 0.0386 | - |
| 0.2622 | 1800 | 0.0032 | - |
| 0.2695 | 1850 | 0.0023 | - |
| 0.2768 | 1900 | 0.0053 | - |
| 0.2840 | 1950 | 0.0228 | - |
| 0.2913 | 2000 | 0.0006 | - |
| 0.2986 | 2050 | 0.0003 | - |
| 0.3059 | 2100 | 0.0142 | - |
| 0.3132 | 2150 | 0.099 | - |
| 0.3205 | 2200 | 0.0144 | - |
| 0.3277 | 2250 | 0.0002 | - |
| 0.3350 | 2300 | 0.0042 | - |
| 0.3423 | 2350 | 0.0359 | - |
| 0.3496 | 2400 | 0.0004 | - |
| 0.3569 | 2450 | 0.0057 | - |
| 0.3642 | 2500 | 0.0046 | - |
| 0.3714 | 2550 | 0.0015 | - |
| 0.3787 | 2600 | 0.0023 | - |
| 0.3860 | 2650 | 0.0004 | - |
| 0.3933 | 2700 | 0.0002 | - |
| 0.4006 | 2750 | 0.0002 | - |
| 0.4079 | 2800 | 0.0267 | - |
| 0.4151 | 2850 | 0.0001 | - |
| 0.4224 | 2900 | 0.0003 | - |
| 0.4297 | 2950 | 0.0037 | - |
| 0.4370 | 3000 | 0.0005 | - |
| 0.4443 | 3050 | 0.0049 | - |
| 0.4516 | 3100 | 0.2431 | - |
| 0.4588 | 3150 | 0.2577 | - |
| 0.4661 | 3200 | 0.1556 | - |
| 0.4734 | 3250 | 0.1983 | - |
| 0.4807 | 3300 | 0.0884 | - |
| 0.4880 | 3350 | 0.0003 | - |
| 0.4953 | 3400 | 0.2302 | - |
| 0.5025 | 3450 | 0.0007 | - |
| 0.5098 | 3500 | 0.0002 | - |
| 0.5171 | 3550 | 0.0001 | - |
| 0.5244 | 3600 | 0.0845 | - |
| 0.5317 | 3650 | 0.0003 | - |
| 0.5390 | 3700 | 0.0001 | - |
| 0.5462 | 3750 | 0.0001 | - |
| 0.5535 | 3800 | 0.0 | - |
| 0.5608 | 3850 | 0.0001 | - |
| 0.5681 | 3900 | 0.001 | - |
| 0.5754 | 3950 | 0.0008 | - |
| 0.5827 | 4000 | 0.002 | - |
| 0.5899 | 4050 | 0.0002 | - |
| 0.5972 | 4100 | 0.1071 | - |
| 0.6045 | 4150 | 0.0001 | - |
| 0.6118 | 4200 | 0.0001 | - |
| 0.6191 | 4250 | 0.0001 | - |
| 0.6264 | 4300 | 0.0002 | - |
| 0.6336 | 4350 | 0.0001 | - |
| 0.6409 | 4400 | 0.0 | - |
| 0.6482 | 4450 | 0.2478 | - |
| 0.6555 | 4500 | 0.0 | - |
| 0.6628 | 4550 | 0.0003 | - |
| 0.6701 | 4600 | 0.0 | - |
| 0.6773 | 4650 | 0.0002 | - |
| 0.6846 | 4700 | 0.003 | - |
| 0.6919 | 4750 | 0.0007 | - |
| 0.6992 | 4800 | 0.0006 | - |
| 0.7065 | 4850 | 0.001 | - |
| 0.7138 | 4900 | 0.0106 | - |
| 0.7210 | 4950 | 0.0001 | - |
| 0.7283 | 5000 | 0.0002 | - |
| 0.7356 | 5050 | 0.0004 | - |
| 0.7429 | 5100 | 0.0008 | - |
| 0.7502 | 5150 | 0.0508 | - |
| 0.7575 | 5200 | 0.001 | - |
| 0.7647 | 5250 | 0.0 | - |
| 0.7720 | 5300 | 0.0249 | - |
| 0.7793 | 5350 | 0.0001 | - |
| 0.7866 | 5400 | 0.1026 | - |
| 0.7939 | 5450 | 0.0 | - |
| 0.8012 | 5500 | 0.0001 | - |
| 0.8084 | 5550 | 0.0028 | - |
| 0.8157 | 5600 | 0.0008 | - |
| 0.8230 | 5650 | 0.0002 | - |
| 0.8303 | 5700 | 0.0001 | - |
| 0.8376 | 5750 | 0.0 | - |
| 0.8449 | 5800 | 0.0001 | - |
| 0.8521 | 5850 | 0.0001 | - |
| 0.8594 | 5900 | 0.0094 | - |
| 0.8667 | 5950 | 0.0001 | - |
| 0.8740 | 6000 | 0.0 | - |
| 0.8813 | 6050 | 0.0 | - |
| 0.8886 | 6100 | 0.0 | - |
| 0.8958 | 6150 | 0.0001 | - |
| 0.9031 | 6200 | 0.0002 | - |
| 0.9104 | 6250 | 0.0026 | - |
| 0.9177 | 6300 | 0.1005 | - |
| 0.9250 | 6350 | 0.0002 | - |
| 0.9323 | 6400 | 0.0004 | - |
| 0.9395 | 6450 | 0.2456 | - |
| 0.9468 | 6500 | 0.0228 | - |
| 0.9541 | 6550 | 0.022 | - |
| 0.9614 | 6600 | 0.025 | - |
| 0.9687 | 6650 | 0.0002 | - |
| 0.9760 | 6700 | 0.0003 | - |
| 0.9832 | 6750 | 0.0001 | - |
| 0.9905 | 6800 | 0.0 | - |
| 0.9978 | 6850 | 0.1145 | - |
| 1.0 | 6865 | - | 0.1868 |
### 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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