---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: sentence-transformers/all-MiniLM-L6-v2
metrics:
- accuracy
widget:
- text: hp:game yg grafiknya standar boros batrai bikin hp cepat panas game satunya
brawlstar ga
- text: game:game cocok indonesia gw main game dibilang berat squad buster jaringan
game berat bagus squad buster main koneksi terputus koneksi aman aman aja mohon
perbaiki jaringan
- text: sinyal:prmainannya bagus sinyal diperbaiki maen game online gak bagus2 aja
pingnya eh maen squad busters jaringannya hilang2 pas match klok sinyal udah hilang
masuk tulisan server konek muat ulang gak masuk in game saran tolong diperbaiki
ya min klok grafik gameplay udah bagus
- text: saran semoga game:gamenya bagus kendala game nya kadang kadang suka jaringan
jaringan bagus saran semoga game nya ditingkatkan disaat update
- text: gameplay:gameplay nya bagus gk match nya optimal main kadang suka lag gitu
sinyal nya bagus tolong supercell perbaiki sinyal
pipeline_tag: text-classification
inference: false
model-index:
- name: SetFit Aspect Model with sentence-transformers/all-MiniLM-L6-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.8307086614173228
name: Accuracy
---
# SetFit Aspect Model with sentence-transformers/all-MiniLM-L6-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) 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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
- **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_Roberta-large_MiniLM-L6-aspect](https://huggingface.co/Funnyworld1412/ABSA_Roberta-large_MiniLM-L6-aspect)
- **SetFitABSA Polarity Model:** [Funnyworld1412/ABSA_Roberta-large_MiniLM-L6-polarity](https://huggingface.co/Funnyworld1412/ABSA_Roberta-large_MiniLM-L6-polarity)
- **Maximum Sequence Length:** 256 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 | - 'pencarian lawan:kapada supercell game nya bagus seru tolong diperbaiki pencarian lawan bermain ketemu player trophy mahkotanya jaraknya dapet berpengaruh peleton akun perbedaan level'
- 'game:kapada supercell game nya bagus seru tolong diperbaiki pencarian lawan bermain ketemu player trophy mahkotanya jaraknya dapet berpengaruh peleton akun perbedaan level'
- 'bugnya:bugnya nakal banget y coc cr aja sukanya ngebug pas match suka hitam match relog kalo udah relog lawan udah 1 2 mahkota kecewa sih bintang nya 1 aja bug nya diurus bintang lawannya kadang g setara levelnya dahlah gk suka banget kalo main 2 vs 2 temen suka banget afk coba fitur report'
|
| no aspect | - 'player trophy mahkotanya jaraknya:kapada supercell game nya bagus seru tolong diperbaiki pencarian lawan bermain ketemu player trophy mahkotanya jaraknya dapet berpengaruh peleton akun perbedaan level'
- 'peleton akun perbedaan level:kapada supercell game nya bagus seru tolong diperbaiki pencarian lawan bermain ketemu player trophy mahkotanya jaraknya dapet berpengaruh peleton akun perbedaan level'
- 'y coc cr:bugnya nakal banget y coc cr aja sukanya ngebug pas match suka hitam match relog kalo udah relog lawan udah 1 2 mahkota kecewa sih bintang nya 1 aja bug nya diurus bintang lawannya kadang g setara levelnya dahlah gk suka banget kalo main 2 vs 2 temen suka banget afk coba fitur report'
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.8307 |
## 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_Roberta-large_MiniLM-L6-aspect",
"Funnyworld1412/ABSA_Roberta-large_MiniLM-L6-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 | 29.9357 | 80 |
| Label | Training Sample Count |
|:----------|:----------------------|
| no aspect | 3834 |
| aspect | 1266 |
### 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 |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0001 | 1 | 0.2715 | - |
| 0.0039 | 50 | 0.2364 | - |
| 0.0078 | 100 | 0.1076 | - |
| 0.0118 | 150 | 0.3431 | - |
| 0.0157 | 200 | 0.2411 | - |
| 0.0196 | 250 | 0.361 | - |
| 0.0235 | 300 | 0.2227 | - |
| 0.0275 | 350 | 0.2087 | - |
| 0.0314 | 400 | 0.1956 | - |
| 0.0353 | 450 | 0.2815 | - |
| 0.0392 | 500 | 0.1844 | - |
| 0.0431 | 550 | 0.2053 | - |
| 0.0471 | 600 | 0.2884 | - |
| 0.0510 | 650 | 0.1043 | - |
| 0.0549 | 700 | 0.2074 | - |
| 0.0588 | 750 | 0.1627 | - |
| 0.0627 | 800 | 0.3 | - |
| 0.0667 | 850 | 0.1658 | - |
| 0.0706 | 900 | 0.1582 | - |
| 0.0745 | 950 | 0.2692 | - |
| 0.0784 | 1000 | 0.1823 | - |
| 0.0824 | 1050 | 0.4098 | - |
| 0.0863 | 1100 | 0.1992 | - |
| 0.0902 | 1150 | 0.0793 | - |
| 0.0941 | 1200 | 0.3924 | - |
| 0.0980 | 1250 | 0.0339 | - |
| 0.1020 | 1300 | 0.2236 | - |
| 0.1059 | 1350 | 0.2262 | - |
| 0.1098 | 1400 | 0.111 | - |
| 0.1137 | 1450 | 0.0223 | - |
| 0.1176 | 1500 | 0.3994 | - |
| 0.1216 | 1550 | 0.0417 | - |
| 0.1255 | 1600 | 0.3319 | - |
| 0.1294 | 1650 | 0.3223 | - |
| 0.1333 | 1700 | 0.2943 | - |
| 0.1373 | 1750 | 0.1273 | - |
| 0.1412 | 1800 | 0.2863 | - |
| 0.1451 | 1850 | 0.0988 | - |
| 0.1490 | 1900 | 0.1593 | - |
| 0.1529 | 1950 | 0.2209 | - |
| 0.1569 | 2000 | 0.5017 | - |
| 0.1608 | 2050 | 0.1392 | - |
| 0.1647 | 2100 | 0.1372 | - |
| 0.1686 | 2150 | 0.3491 | - |
| 0.1725 | 2200 | 0.2693 | - |
| 0.1765 | 2250 | 0.1988 | - |
| 0.1804 | 2300 | 0.2765 | - |
| 0.1843 | 2350 | 0.238 | - |
| 0.1882 | 2400 | 0.0577 | - |
| 0.1922 | 2450 | 0.2253 | - |
| 0.1961 | 2500 | 0.16 | - |
| 0.2 | 2550 | 0.0262 | - |
| 0.2039 | 2600 | 0.0099 | - |
| 0.2078 | 2650 | 0.0132 | - |
| 0.2118 | 2700 | 0.2356 | - |
| 0.2157 | 2750 | 0.2975 | - |
| 0.2196 | 2800 | 0.154 | - |
| 0.2235 | 2850 | 0.0308 | - |
| 0.2275 | 2900 | 0.0497 | - |
| 0.2314 | 2950 | 0.0523 | - |
| 0.2353 | 3000 | 0.158 | - |
| 0.2392 | 3050 | 0.0473 | - |
| 0.2431 | 3100 | 0.208 | - |
| 0.2471 | 3150 | 0.2126 | - |
| 0.2510 | 3200 | 0.081 | - |
| 0.2549 | 3250 | 0.0134 | - |
| 0.2588 | 3300 | 0.1107 | - |
| 0.2627 | 3350 | 0.0249 | - |
| 0.2667 | 3400 | 0.0259 | - |
| 0.2706 | 3450 | 0.1008 | - |
| 0.2745 | 3500 | 0.0335 | - |
| 0.2784 | 3550 | 0.0119 | - |
| 0.2824 | 3600 | 0.2982 | - |
| 0.2863 | 3650 | 0.1516 | - |
| 0.2902 | 3700 | 0.1217 | - |
| 0.2941 | 3750 | 0.1558 | - |
| 0.2980 | 3800 | 0.0359 | - |
| 0.3020 | 3850 | 0.0215 | - |
| 0.3059 | 3900 | 0.2906 | - |
| 0.3098 | 3950 | 0.0599 | - |
| 0.3137 | 4000 | 0.1528 | - |
| 0.3176 | 4050 | 0.0144 | - |
| 0.3216 | 4100 | 0.298 | - |
| 0.3255 | 4150 | 0.0174 | - |
| 0.3294 | 4200 | 0.0093 | - |
| 0.3333 | 4250 | 0.0329 | - |
| 0.3373 | 4300 | 0.1795 | - |
| 0.3412 | 4350 | 0.0712 | - |
| 0.3451 | 4400 | 0.3703 | - |
| 0.3490 | 4450 | 0.0873 | - |
| 0.3529 | 4500 | 0.3223 | - |
| 0.3569 | 4550 | 0.0045 | - |
| 0.3608 | 4600 | 0.2188 | - |
| 0.3647 | 4650 | 0.0085 | - |
| 0.3686 | 4700 | 0.2089 | - |
| 0.3725 | 4750 | 0.0052 | - |
| 0.3765 | 4800 | 0.1459 | - |
| 0.3804 | 4850 | 0.0711 | - |
| 0.3843 | 4900 | 0.4268 | - |
| 0.3882 | 4950 | 0.1842 | - |
| 0.3922 | 5000 | 0.1661 | - |
| 0.3961 | 5050 | 0.1028 | - |
| 0.4 | 5100 | 0.067 | - |
| 0.4039 | 5150 | 0.1708 | - |
| 0.4078 | 5200 | 0.1001 | - |
| 0.4118 | 5250 | 0.065 | - |
| 0.4157 | 5300 | 0.0279 | - |
| 0.4196 | 5350 | 0.1101 | - |
| 0.4235 | 5400 | 0.1923 | - |
| 0.4275 | 5450 | 0.5491 | - |
| 0.4314 | 5500 | 0.0726 | - |
| 0.4353 | 5550 | 0.0085 | - |
| 0.4392 | 5600 | 0.194 | - |
| 0.4431 | 5650 | 0.2527 | - |
| 0.4471 | 5700 | 0.7134 | - |
| 0.4510 | 5750 | 0.4542 | - |
| 0.4549 | 5800 | 0.2779 | - |
| 0.4588 | 5850 | 0.1024 | - |
| 0.4627 | 5900 | 0.2483 | - |
| 0.4667 | 5950 | 0.0163 | - |
| 0.4706 | 6000 | 0.0095 | - |
| 0.4745 | 6050 | 0.2902 | - |
| 0.4784 | 6100 | 0.0111 | - |
| 0.4824 | 6150 | 0.0296 | - |
| 0.4863 | 6200 | 0.3792 | - |
| 0.4902 | 6250 | 0.4387 | - |
| 0.4941 | 6300 | 0.1547 | - |
| 0.4980 | 6350 | 0.0617 | - |
| 0.5020 | 6400 | 0.1384 | - |
| 0.5059 | 6450 | 0.0677 | - |
| 0.5098 | 6500 | 0.0454 | - |
| 0.5137 | 6550 | 0.0074 | - |
| 0.5176 | 6600 | 0.1994 | - |
| 0.5216 | 6650 | 0.0168 | - |
| 0.5255 | 6700 | 0.0416 | - |
| 0.5294 | 6750 | 0.1898 | - |
| 0.5333 | 6800 | 0.0207 | - |
| 0.5373 | 6850 | 0.1046 | - |
| 0.5412 | 6900 | 0.1994 | - |
| 0.5451 | 6950 | 0.0435 | - |
| 0.5490 | 7000 | 0.0149 | - |
| 0.5529 | 7050 | 0.0067 | - |
| 0.5569 | 7100 | 0.0122 | - |
| 0.5608 | 7150 | 0.2406 | - |
| 0.5647 | 7200 | 0.4473 | - |
| 0.5686 | 7250 | 0.0469 | - |
| 0.5725 | 7300 | 0.1782 | - |
| 0.5765 | 7350 | 0.3386 | - |
| 0.5804 | 7400 | 0.2804 | - |
| 0.5843 | 7450 | 0.0072 | - |
| 0.5882 | 7500 | 0.0451 | - |
| 0.5922 | 7550 | 0.0188 | - |
| 0.5961 | 7600 | 0.01 | - |
| 0.6 | 7650 | 0.0048 | - |
| 0.6039 | 7700 | 0.2349 | - |
| 0.6078 | 7750 | 0.2052 | - |
| 0.6118 | 7800 | 0.0838 | - |
| 0.6157 | 7850 | 0.3052 | - |
| 0.6196 | 7900 | 0.3667 | - |
| 0.6235 | 7950 | 0.0044 | - |
| 0.6275 | 8000 | 0.3612 | - |
| 0.6314 | 8050 | 0.2082 | - |
| 0.6353 | 8100 | 0.3384 | - |
| 0.6392 | 8150 | 0.022 | - |
| 0.6431 | 8200 | 0.0764 | - |
| 0.6471 | 8250 | 0.2879 | - |
| 0.6510 | 8300 | 0.1827 | - |
| 0.6549 | 8350 | 0.1104 | - |
| 0.6588 | 8400 | 0.2096 | - |
| 0.6627 | 8450 | 0.2103 | - |
| 0.6667 | 8500 | 0.0742 | - |
| 0.6706 | 8550 | 0.2186 | - |
| 0.6745 | 8600 | 0.0109 | - |
| 0.6784 | 8650 | 0.0326 | - |
| 0.6824 | 8700 | 0.3056 | - |
| 0.6863 | 8750 | 0.0941 | - |
| 0.6902 | 8800 | 0.3731 | - |
| 0.6941 | 8850 | 0.2185 | - |
| 0.6980 | 8900 | 0.0228 | - |
| 0.7020 | 8950 | 0.0141 | - |
| 0.7059 | 9000 | 0.2242 | - |
| 0.7098 | 9050 | 0.3303 | - |
| 0.7137 | 9100 | 0.2383 | - |
| 0.7176 | 9150 | 0.0026 | - |
| 0.7216 | 9200 | 0.1718 | - |
| 0.7255 | 9250 | 0.053 | - |
| 0.7294 | 9300 | 0.0023 | - |
| 0.7333 | 9350 | 0.221 | - |
| 0.7373 | 9400 | 0.0021 | - |
| 0.7412 | 9450 | 0.2333 | - |
| 0.7451 | 9500 | 0.0565 | - |
| 0.7490 | 9550 | 0.0271 | - |
| 0.7529 | 9600 | 0.2156 | - |
| 0.7569 | 9650 | 0.2349 | - |
| 0.7608 | 9700 | 0.0047 | - |
| 0.7647 | 9750 | 0.1273 | - |
| 0.7686 | 9800 | 0.0139 | - |
| 0.7725 | 9850 | 0.0231 | - |
| 0.7765 | 9900 | 0.0048 | - |
| 0.7804 | 9950 | 0.0022 | - |
| 0.7843 | 10000 | 0.0026 | - |
| 0.7882 | 10050 | 0.0223 | - |
| 0.7922 | 10100 | 0.5488 | - |
| 0.7961 | 10150 | 0.0281 | - |
| 0.8 | 10200 | 0.0999 | - |
| 0.8039 | 10250 | 0.2154 | - |
| 0.8078 | 10300 | 0.0109 | - |
| 0.8118 | 10350 | 0.0019 | - |
| 0.8157 | 10400 | 0.1264 | - |
| 0.8196 | 10450 | 0.0029 | - |
| 0.8235 | 10500 | 0.3785 | - |
| 0.8275 | 10550 | 0.0366 | - |
| 0.8314 | 10600 | 0.0527 | - |
| 0.8353 | 10650 | 0.2355 | - |
| 0.8392 | 10700 | 0.0833 | - |
| 0.8431 | 10750 | 0.1612 | - |
| 0.8471 | 10800 | 0.0071 | - |
| 0.8510 | 10850 | 0.1128 | - |
| 0.8549 | 10900 | 0.2521 | - |
| 0.8588 | 10950 | 0.0403 | - |
| 0.8627 | 11000 | 0.2196 | - |
| 0.8667 | 11050 | 0.1441 | - |
| 0.8706 | 11100 | 0.0295 | - |
| 0.8745 | 11150 | 0.0047 | - |
| 0.8784 | 11200 | 0.3089 | - |
| 0.8824 | 11250 | 0.1055 | - |
| 0.8863 | 11300 | 0.0064 | - |
| 0.8902 | 11350 | 0.2119 | - |
| 0.8941 | 11400 | 0.2145 | - |
| 0.8980 | 11450 | 0.0128 | - |
| 0.9020 | 11500 | 0.0086 | - |
| 0.9059 | 11550 | 0.1803 | - |
| 0.9098 | 11600 | 0.2277 | - |
| 0.9137 | 11650 | 0.0204 | - |
| 0.9176 | 11700 | 0.0105 | - |
| 0.9216 | 11750 | 0.005 | - |
| 0.9255 | 11800 | 0.0099 | - |
| 0.9294 | 11850 | 0.004 | - |
| 0.9333 | 11900 | 0.1824 | - |
| 0.9373 | 11950 | 0.0021 | - |
| 0.9412 | 12000 | 0.2231 | - |
| 0.9451 | 12050 | 0.0017 | - |
| 0.9490 | 12100 | 0.0752 | - |
| 0.9529 | 12150 | 0.0129 | - |
| 0.9569 | 12200 | 0.1644 | - |
| 0.9608 | 12250 | 0.0305 | - |
| 0.9647 | 12300 | 0.0133 | - |
| 0.9686 | 12350 | 0.0687 | - |
| 0.9725 | 12400 | 0.0039 | - |
| 0.9765 | 12450 | 0.1179 | - |
| 0.9804 | 12500 | 0.1867 | - |
| 0.9843 | 12550 | 0.0225 | - |
| 0.9882 | 12600 | 0.1914 | - |
| 0.9922 | 12650 | 0.0592 | - |
| 0.9961 | 12700 | 0.0059 | - |
| 1.0 | 12750 | 0.1016 | 0.2295 |
### 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}
}
```