---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: level:game bagus banget sumpah udah nyelesain level 1 sampe level 8 gak sengaja
kehapus download save level 1 level 2 level 3 sampe level 8 gak save
- text: update:game nya bagus sih 1 bug error bermain geometry nya meloncat loncat
tau wi fi potato kasih game robtop 4 bintang semoga update diperbaiki d
- text: lagu:game nya bgs seru game nya gk susah pake offline cmn 1 kekurangannya
gk game trs gk ganti lagu jd nya dimatiin lgu dri nya trs pake lagu sekian ulasan
terima kasih
- text: kali:game nya seru kali mainin muncul iklan mohon ya iklannya dikurangin yg
install sabar ya main nya susah
- text: kekurangannya:game nya bgs seru game nya gk susah pake offline cmn 1 kekurangannya
gk game trs gk ganti lagu jd nya dimatiin lgu dri nya trs pake lagu sekian ulasan
terima kasih
pipeline_tag: text-classification
inference: false
---
# SetFit Aspect Model
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). 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
- **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:** [jetri20/ABSA_review_game_geometry-aspect](https://huggingface.co/jetri20/ABSA_review_game_geometry-aspect)
- **SetFitABSA Polarity Model:** [jetri20/ABSA_review_game_geometry-polarity](https://huggingface.co/jetri20/ABSA_review_game_geometry-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 |
- 'level:sih game level nya sulit banget level clutter funk susah nya ampun level 11 nya level sulit nya level 10 sulit menyerah sabar banting hp saking sulit nya ya geometri dash kebanyakan level nya sulit mah geometri dash game stress saking susah nya'
- 'iklan:iklan emang sih level iklan muncul masuk level mending kayak mah'
- 'game:game apasih sampe strees gitu final boss level the tower susahnya ampun kenapasih kalo hijau pas udah nya jatuh mati sih cube nya cuman 1 hp ya ngeselin sih'
|
| no aspect | - 'sih game level:sih game level nya sulit banget level clutter funk susah nya ampun level 11 nya level sulit nya level 10 sulit menyerah sabar banting hp saking sulit nya ya geometri dash kebanyakan level nya sulit mah geometri dash game stress saking susah nya'
- 'level clutter funk:sih game level nya sulit banget level clutter funk susah nya ampun level 11 nya level sulit nya level 10 sulit menyerah sabar banting hp saking sulit nya ya geometri dash kebanyakan level nya sulit mah geometri dash game stress saking susah nya'
- 'level:sih game level nya sulit banget level clutter funk susah nya ampun level 11 nya level sulit nya level 10 sulit menyerah sabar banting hp saking sulit nya ya geometri dash kebanyakan level nya sulit mah geometri dash game stress saking susah nya'
|
## 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(
"jetri20/ABSA_review_game_geometry-aspect",
"jetri20/ABSA_review_game_geometry-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 | 23.5963 | 67 |
| Label | Training Sample Count |
|:----------|:----------------------|
| no aspect | 754 |
| aspect | 321 |
### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0004 | 1 | 0.3713 | - |
| 0.0186 | 50 | 0.2045 | - |
| 0.0372 | 100 | 0.1548 | - |
| 0.0558 | 150 | 0.3116 | - |
| 0.0744 | 200 | 0.2066 | - |
| 0.0930 | 250 | 0.2932 | - |
| 0.1116 | 300 | 0.3138 | - |
| 0.1302 | 350 | 0.1258 | - |
| 0.1488 | 400 | 0.3442 | - |
| 0.1674 | 450 | 0.0558 | - |
| 0.1860 | 500 | 0.2819 | - |
| 0.2046 | 550 | 0.2211 | - |
| 0.2232 | 600 | 0.1269 | - |
| 0.2418 | 650 | 0.0098 | - |
| 0.2604 | 700 | 0.2395 | - |
| 0.2790 | 750 | 0.4382 | - |
| 0.2976 | 800 | 0.488 | - |
| 0.3162 | 850 | 0.6662 | - |
| 0.3348 | 900 | 0.1811 | - |
| 0.3534 | 950 | 0.2431 | - |
| 0.3720 | 1000 | 0.2032 | - |
| 0.3906 | 1050 | 0.0475 | - |
| 0.4092 | 1100 | 0.177 | - |
| 0.4278 | 1150 | 0.0556 | - |
| 0.4464 | 1200 | 0.3048 | - |
| 0.4650 | 1250 | 0.0015 | - |
| 0.4836 | 1300 | 0.0841 | - |
| 0.5022 | 1350 | 0.0105 | - |
| 0.5208 | 1400 | 0.0036 | - |
| 0.5394 | 1450 | 0.2296 | - |
| 0.5580 | 1500 | 0.0045 | - |
| 0.5766 | 1550 | 0.0134 | - |
| 0.5952 | 1600 | 0.0367 | - |
| 0.6138 | 1650 | 0.0044 | - |
| 0.6324 | 1700 | 0.0068 | - |
| 0.6510 | 1750 | 0.1408 | - |
| 0.6696 | 1800 | 0.0092 | - |
| 0.6882 | 1850 | 0.1926 | - |
| 0.7068 | 1900 | 0.0014 | - |
| 0.7254 | 1950 | 0.0003 | - |
| 0.7440 | 2000 | 0.2094 | - |
| 0.7626 | 2050 | 0.0329 | - |
| 0.7812 | 2100 | 0.0028 | - |
| 0.7999 | 2150 | 0.0144 | - |
| 0.8185 | 2200 | 0.1555 | - |
| 0.8371 | 2250 | 0.0005 | - |
| 0.8557 | 2300 | 0.0067 | - |
| 0.8743 | 2350 | 0.1485 | - |
| 0.8929 | 2400 | 0.0034 | - |
| 0.9115 | 2450 | 0.0044 | - |
| 0.9301 | 2500 | 0.2752 | - |
| 0.9487 | 2550 | 0.1342 | - |
| 0.9673 | 2600 | 0.0108 | - |
| 0.9859 | 2650 | 0.0106 | - |
| 1.0 | 2688 | - | 0.2236 |
### 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}
}
```