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---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: gulungan biasa menjadi gulungan luar dalam,:dibutuhkan biaya tambahan $2 untuk
mengubah gulungan biasa menjadi gulungan luar dalam, tetapi gulungan tersebut
berukuran lebih dari tiga kali lipat, dan itu bukan ha dari nasi.
- text: -a-bagel (baik di:ess-a-bagel (baik di sty-town atau midtown) sejauh ini merupakan
bagel terbaik di ny.
- text: mahal wadah ini pengelola:ketika kami sedang duduk makan makanan di bawah
standar, manajer mulai mencaci-maki beberapa karyawan karena meletakkan wadah
bumbu yang salah dan menjelaskan kepada mereka betapa mahal wadah ini pengelola
- text: staf sangat akomodatif.:staf sangat akomodatif.
- text: layanan luar biasa melayani:makanan india yang enak dan layanan luar biasa
melayani
pipeline_tag: text-classification
inference: false
base_model: BAAI/bge-m3
model-index:
- name: SetFit Polarity Model with BAAI/bge-m3
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.7898320472083522
name: Accuracy
---
# SetFit Polarity Model with BAAI/bge-m3
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) 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 classifying aspect polarities.
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 a SetFit model to filter these possible aspect span candidates.
3. **Use this SetFit model to classify the filtered aspect span candidates.**
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3)
- **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:** 8192 tokens
- **Number of Classes:** 4 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
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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 |
|:--------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| netral | <ul><li>'sangat kecil sehingga reservasi adalah suatu keharusan:restoran ini sangat kecil sehingga reservasi adalah suatu keharusan.'</li><li>'di dekat seorang busboy dan mendesiskan rapido: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><li>'dan mengatur ulang meja untuk enam orang: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> |
| negatif | <ul><li>'untuk enam orang 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>'setelah berurusan dengan pizza di bawah standar:setelah berurusan dengan pizza di bawah standar di seluruh lingkungan kensington - saya menemukan sedikit tonino.'</li><li>'mereka tidak mejikan bir, anda harus:perhatikan bahwa mereka tidak mejikan bir, anda harus membawa sendiri.'</li></ul> |
| positif | <ul><li>'saya tidak menyukai gnocchi.:saya tidak menyukai gnocchi.'</li><li>'dari makanan pembuka yang kami makan:dari makanan pembuka yang kami makan, dim sum, dan variasi makanan lain, tidak mungkin untuk mengkritik makanan tersebut.'</li><li>'kami makan, dim sum, dan variasi:dari makanan pembuka yang kami makan, dim sum, dan variasi makanan lain, tidak mungkin untuk mengkritik makanan tersebut.'</li></ul> |
| konflik | <ul><li>'makanan enak tapi jangan:makanan enak tapi jangan datang ke sini dengan perut kosong.'</li><li>'milik pihak rumah tagihan:namun, setiap perselisihan tentang ruu itu diimbangi oleh takaran minuman keras yang anda tuangkan sendiri yang merupakan milik pihak rumah tagihan'</li><li>'layanan meja bisa menjadi sedikit:layanan meja bisa menjadi sedikit lebih penuh perhatian tetapi sebagai seseorang yang juga bekerja di industri jasa, saya mengerti mereka sedang sibuk.'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.7898 |
## 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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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 3 | 20.6594 | 62 |
| Label | Training Sample Count |
|:--------|:----------------------|
| konflik | 34 |
| negatif | 323 |
| netral | 258 |
| positif | 853 |
### 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 | 0.2345 | - |
| 0.0006 | 50 | 0.2337 | - |
| 0.0013 | 100 | 0.267 | - |
| 0.0019 | 150 | 0.2335 | - |
| 0.0025 | 200 | 0.2368 | - |
| 0.0032 | 250 | 0.2199 | - |
| 0.0038 | 300 | 0.2325 | - |
| 0.0045 | 350 | 0.2071 | - |
| 0.0051 | 400 | 0.2229 | - |
| 0.0057 | 450 | 0.1153 | - |
| 0.0064 | 500 | 0.1771 | 0.1846 |
| 0.0070 | 550 | 0.1612 | - |
| 0.0076 | 600 | 0.1487 | - |
| 0.0083 | 650 | 0.147 | - |
| 0.0089 | 700 | 0.1982 | - |
| 0.0096 | 750 | 0.1579 | - |
| 0.0102 | 800 | 0.1148 | - |
| 0.0108 | 850 | 0.1008 | - |
| 0.0115 | 900 | 0.2035 | - |
| 0.0121 | 950 | 0.1348 | - |
| **0.0127** | **1000** | **0.0974** | **0.182** |
| 0.0134 | 1050 | 0.121 | - |
| 0.0140 | 1100 | 0.1949 | - |
| 0.0147 | 1150 | 0.2424 | - |
| 0.0153 | 1200 | 0.0601 | - |
| 0.0159 | 1250 | 0.0968 | - |
| 0.0166 | 1300 | 0.0137 | - |
| 0.0172 | 1350 | 0.034 | - |
| 0.0178 | 1400 | 0.1217 | - |
| 0.0185 | 1450 | 0.0454 | - |
| 0.0191 | 1500 | 0.0397 | 0.2216 |
| 0.0198 | 1550 | 0.0226 | - |
| 0.0204 | 1600 | 0.0939 | - |
| 0.0210 | 1650 | 0.0537 | - |
| 0.0217 | 1700 | 0.0566 | - |
| 0.0223 | 1750 | 0.162 | - |
| 0.0229 | 1800 | 0.0347 | - |
| 0.0236 | 1850 | 0.103 | - |
| 0.0242 | 1900 | 0.0615 | - |
| 0.0249 | 1950 | 0.0589 | - |
| 0.0255 | 2000 | 0.1668 | 0.2132 |
| 0.0261 | 2050 | 0.1809 | - |
| 0.0268 | 2100 | 0.0579 | - |
| 0.0274 | 2150 | 0.088 | - |
| 0.0280 | 2200 | 0.1047 | - |
| 0.0287 | 2250 | 0.1255 | - |
| 0.0293 | 2300 | 0.0312 | - |
| 0.0300 | 2350 | 0.0097 | - |
| 0.0306 | 2400 | 0.0973 | - |
| 0.0312 | 2450 | 0.0066 | - |
| 0.0319 | 2500 | 0.0589 | 0.2591 |
| 0.0325 | 2550 | 0.0529 | - |
| 0.0331 | 2600 | 0.0169 | - |
| 0.0338 | 2650 | 0.0455 | - |
| 0.0344 | 2700 | 0.0609 | - |
| 0.0350 | 2750 | 0.1151 | - |
| 0.0357 | 2800 | 0.0031 | - |
| 0.0363 | 2850 | 0.0546 | - |
| 0.0370 | 2900 | 0.0051 | - |
| 0.0376 | 2950 | 0.0679 | - |
| 0.0382 | 3000 | 0.0046 | 0.2646 |
| 0.0389 | 3050 | 0.011 | - |
| 0.0395 | 3100 | 0.0701 | - |
| 0.0401 | 3150 | 0.0011 | - |
| 0.0408 | 3200 | 0.011 | - |
| 0.0414 | 3250 | 0.0026 | - |
| 0.0421 | 3300 | 0.0027 | - |
| 0.0427 | 3350 | 0.0012 | - |
| 0.0433 | 3400 | 0.0454 | - |
| 0.0440 | 3450 | 0.0011 | - |
| 0.0446 | 3500 | 0.0012 | 0.2602 |
* 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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