---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Военная кафедра
- text: Какие льготы есть у выпускников колледжа?
- text: Какие этапы включает приемная кампания?
- text: Сколько продлится приемная кампания
- text: Какие требования для поступления в ВУЦ?
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: cointegrated/rubert-tiny2
model-index:
- name: SetFit with cointegrated/rubert-tiny2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 1.0
      name: Accuracy
---

# SetFit with cointegrated/rubert-tiny2

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. 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.

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.

## 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
- **Maximum Sequence Length:** 2048 tokens
- **Number of Classes:** 7 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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                                                                                                                                                                          |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 3     | <ul><li>'Есть ли военная кафедра'</li><li>'Какие документы нужны для поступления в ВУЦ?'</li><li>'Как проходит отбор на военную кафедру'</li></ul>                                |
| 2     | <ul><li>'Как подтвердить свои достижения?'</li><li>'Расскажи о дополнительных баллах при поступлении'</li><li>'Можно ли получить баллы за волонтерскую деятельность?'</li></ul>   |
| 1     | <ul><li>'Нужно ли заверять копии документов?'</li><li>'Как узнать, что мои документы приняты?'</li><li>'Можно ли подать документы по почте?'</li></ul>                            |
| 0     | <ul><li>'Как долго длится приемная кампания'</li><li>'Продолжительность приемной кампании'</li><li>'Когда заканчивается приемная кампания?'</li></ul>                             |
| 4     | <ul><li>'Какие документы нужны для поступления после колледжа?'</li><li>'Как поступить в Вуз после колледжа'</li><li>'Какие сроки подачи документов после колледжа?'</li></ul>    |
| 6     | <ul><li>'Как подать заявление на общежитие?'</li><li>'Кто претендует на предоставление общежития'</li><li>'Как оплачивать проживание в общежитии?'</li></ul>                      |
| 5     | <ul><li>'Какие ошибки чаще всего допускают на экзаменах?'</li><li>'Какие вступительные испытания нужно сдавать?'</li><li>'Как подготовиться к вступительным экзаменам?'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 1.0      |

## 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 SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("Maxim01/Intent_Classification_Test")
# Run inference
preds = model("Военная кафедра")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 2   | 5.3333 | 8   |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 9                     |
| 1     | 11                    |
| 2     | 6                     |
| 3     | 9                     |
| 4     | 10                    |
| 5     | 11                    |
| 6     | 10                    |

### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0030 | 1    | 0.1199        | -               |
| 0.1515 | 50   | 0.1727        | -               |
| 0.3030 | 100  | 0.0936        | -               |
| 0.4545 | 150  | 0.0599        | -               |
| 0.6061 | 200  | 0.0529        | -               |
| 0.7576 | 250  | 0.0436        | -               |
| 0.9091 | 300  | 0.0359        | -               |

### Framework Versions
- Python: 3.11.12
- SetFit: 1.1.2
- Sentence Transformers: 3.4.1
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Datasets: 3.5.1
- Tokenizers: 0.21.1

## 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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