README.md

---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: cointegrated/rubert-tiny2
metrics:
- accuracy
widget:
- text: а л а палтуса запеченного – х о:П о п р о б о в а л а палтуса запеченного
    – х о р о ш , д а и к р а с и в о с м о т р и т с я н а т а р е л к е .
- text: 'с курицей , лосось со шпинатным соусом , чай облепиховый:При каждом новом
    посещении я стараюсь пробовать новые блюда из меню , особенно мне понравились
    : цезарь с курицей , лосось со шпинатным соусом , чай облепиховый и тирамису от
    шеф повара .'
- text: ', но качество еды ее не украсило:Свадьба , конечно , прошла весело , но качество
    еды ее не украсило .'
- text: найти уютное недорогое местечко в районе метро:Думаю , если стоит задача найти
    уютное недорогое местечко в районе метро московская , то это наверно один из лучших
    вариантов .
- text: они начали разнообразить кухню мясными блюдами ,:Хочется , чтобы мой отзыв
    дошел до администрации , и они начали разнообразить кухню мясными блюдами , гарнирами
    , интересными салатами и супами .
pipeline_tag: text-classification
inference: false
---

# SetFit Polarity Model with cointegrated/rubert-tiny2

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) 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:** [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **spaCy Model:** ru_core_news_lg
- **SetFitABSA Aspect Model:** [isolation-forest/setfit-absa-aspect](https://huggingface.co/isolation-forest/setfit-absa-aspect)
- **SetFitABSA Polarity Model:** [isolation-forest/setfit-absa-polarity](https://huggingface.co/isolation-forest/setfit-absa-polarity)
- **Maximum Sequence Length:** 2048 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
|:---------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Positive | <ul><li>'И порции " достойные ":И порции " достойные " .'</li><li>'Салаты вообще оказались вкуснейшими:Салаты вообще оказались вкуснейшими .'</li><li>'порадовала , большая пивная тарелка , действительно оказалась:Кухня порадовала , большая пивная тарелка , действительно оказалась большой и вкусной !'</li></ul>                                                                                                                                                                                                                                                                                                    |
| Negative | <ul><li>'Потом официантка как будто пропала:Потом официантка как будто пропала , было не дозваться , чтобы что - то дозаказать , очень долго приходилось ждать , в итоге посчитали неправильно , в счет внесли на 2 пункта больше , чем мы заказывали .'</li><li>'Обслуживание не впечатлило .:Обслуживание не впечатлило .'</li><li>'приятно удивлена " китайским интерьером " - диванчики:Была приятно удивлена " китайским интерьером " - диванчики как в бистро , скатерти на столах по типу а - ля столовая , европейские светильники / люстры , в общем в плане интерьера китайского никакого абсолютно !'</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(
    "isolation-forest/setfit-absa-aspect",
    "isolation-forest/setfit-absa-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   | 28.4766 | 92  |

| Label    | Training Sample Count |
|:---------|:----------------------|
| Negative | 128                   |
| Positive | 128                   |

### Training Hyperparameters
- batch_size: (16, 2)
- num_epochs: (1, 16)
- 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: 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0005 | 1    | 0.2196        | -               |
| 0.0242 | 50   | 0.2339        | -               |
| 0.0484 | 100  | 0.2258        | -               |
| 0.0727 | 150  | 0.246         | -               |
| 0.0969 | 200  | 0.1963        | -               |
| 0.1211 | 250  | 0.18          | -               |
| 0.1453 | 300  | 0.1176        | -               |
| 0.1696 | 350  | 0.0588        | -               |
| 0.1938 | 400  | 0.0482        | -               |
| 0.2180 | 450  | 0.1131        | -               |
| 0.2422 | 500  | 0.0134        | -               |
| 0.2665 | 550  | 0.0415        | -               |
| 0.2907 | 600  | 0.0144        | -               |
| 0.3149 | 650  | 0.012         | -               |
| 0.3391 | 700  | 0.0091        | -               |
| 0.3634 | 750  | 0.0055        | -               |
| 0.3876 | 800  | 0.0054        | -               |
| 0.4118 | 850  | 0.0055        | -               |
| 0.4360 | 900  | 0.0072        | -               |
| 0.4603 | 950  | 0.0094        | -               |
| 0.4845 | 1000 | 0.0054        | -               |
| 0.5087 | 1050 | 0.0045        | -               |
| 0.5329 | 1100 | 0.003         | -               |
| 0.5572 | 1150 | 0.0067        | -               |
| 0.5814 | 1200 | 0.0041        | -               |
| 0.6056 | 1250 | 0.0048        | -               |
| 0.6298 | 1300 | 0.0053        | -               |
| 0.6541 | 1350 | 0.0048        | -               |
| 0.6783 | 1400 | 0.0038        | -               |
| 0.7025 | 1450 | 0.0037        | -               |
| 0.7267 | 1500 | 0.0031        | -               |
| 0.7510 | 1550 | 0.0038        | -               |
| 0.7752 | 1600 | 0.0032        | -               |
| 0.7994 | 1650 | 0.0039        | -               |
| 0.8236 | 1700 | 0.0032        | -               |
| 0.8479 | 1750 | 0.0023        | -               |
| 0.8721 | 1800 | 0.0029        | -               |
| 0.8963 | 1850 | 0.0041        | -               |
| 0.9205 | 1900 | 0.0026        | -               |
| 0.9448 | 1950 | 0.0027        | -               |
| 0.9690 | 2000 | 0.0035        | -               |
| 0.9932 | 2050 | 0.003         | -               |

### Framework Versions
- Python: 3.10.13
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- spaCy: 3.7.2
- Transformers: 4.39.3
- PyTorch: 2.1.2
- Datasets: 2.18.0
- 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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