---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: робот может бегать
- text: пора показать лапу
- text: привяжи робота 1
- text: часто вращается
- text: примите положение лежа
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: google/embeddinggemma-300M
---

# SetFit with google/embeddinggemma-300M

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [google/embeddinggemma-300M](https://huggingface.co/google/embeddinggemma-300M) 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:** [google/embeddinggemma-300M](https://huggingface.co/google/embeddinggemma-300M)
- **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:** 14 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/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                                                                                          |
|:-------------------|:--------------------------------------------------------------------------------------------------|
| help               | <ul><li>'помощь'</li><li>'помоги'</li><li>'помогите'</li></ul>                                    |
| silence            | <ul><li>'тишина'</li><li>'молчи'</li><li>'молчите'</li></ul>                                      |
| bind               | <ul><li>'привяжи робота'</li><li>'привяжи панду'</li><li>'привяжи робота 1'</li></ul>             |
| unbind             | <ul><li>'отвяжи робота'</li><li>'отвяжи панду'</li><li>'отвяжите робота'</li></ul>                |
| report_command     | <ul><li>'исправить команду'</li><li>'исправь команду'</li><li>'исправьте команду'</li></ul>       |
| give_paw           | <ul><li>'лапу'</li><li>'дай лапу'</li><li>'дать лапу'</li></ul>                                   |
| stand_at_attention | <ul><li>'равняйсь'</li><li>'равняйся'</li><li>'равняться'</li></ul>                               |
| dismiss            | <ul><li>'отставить'</li><li>'отставь'</li><li>'встать'</li></ul>                                  |
| lie_down           | <ul><li>'лежать'</li><li>'лечь'</li><li>'ложиться'</li></ul>                                      |
| rotate             | <ul><li>'кувыркнуться'</li><li>'кувыркнись'</li><li>'кувыркаться'</li></ul>                       |
| run                | <ul><li>'бежать'</li><li>'беги'</li><li>'бегать'</li></ul>                                        |
| stop_running       | <ul><li>'остановиться'</li><li>'остановись'</li><li>'останавливаться'</li></ul>                   |
| reconnect_joystick | <ul><li>'подключить джойстик'</li><li>'подключи джойстик'</li><li>'подключать джойстик'</li></ul> |
| unknown            | <ul><li>'привет'</li><li>'как дела'</li><li>'что происходит'</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 SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("tmpb84tfylb/panda_commands")
# Run inference
preds = model("часто вращается")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 1   | 2.3808 | 7   |

| Label              | Training Sample Count |
|:-------------------|:----------------------|
| bind               | 55                    |
| dismiss            | 160                   |
| give_paw           | 104                   |
| help               | 22                    |
| lie_down           | 172                   |
| reconnect_joystick | 135                   |
| report_command     | 50                    |
| rotate             | 137                   |
| run                | 106                   |
| silence            | 27                    |
| stand_at_attention | 88                    |
| stop_running       | 135                   |
| unbind             | 37                    |
| unknown            | 479                   |

### Training Hyperparameters
- batch_size: (256, 256)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0037 | 1    | 0.2375        | -               |
| 0.1873 | 50   | 0.0728        | -               |
| 0.3745 | 100  | 0.009         | -               |
| 0.5618 | 150  | 0.005         | -               |
| 0.7491 | 200  | 0.0038        | -               |
| 0.9363 | 250  | 0.0028        | -               |

### Framework Versions
- Python: 3.11.14
- SetFit: 1.1.3
- Sentence Transformers: 5.2.2
- Transformers: 4.57.6
- PyTorch: 2.9.1+cu128
- Datasets: 4.5.0
- Tokenizers: 0.22.2

## 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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