---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: For example, there is no better entartainment for group of friends than visiting
    sport games and matches.
- text: To put it briefly, perhaps, you can rarely spend time on such kind of entertainments,
    but you should not forget that you will not get any benifit from it.
- text: ' Watching sports helps people to develop their social life.'
- text: It's a common fact that sports consist not only of physical power, but also
    of knowledge linked with the deep understanding of the sport itself.
- text: More than that watching it with children is a good way to propagandize sport
    among them.
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: Qwen/Qwen3-Embedding-0.6B
model-index:
- name: SetFit with Qwen/Qwen3-Embedding-0.6B
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.7959183673469388
      name: Accuracy
---

# SetFit with Qwen/Qwen3-Embedding-0.6B

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) 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:** [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 32768 tokens
- **Number of Classes:** 2 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                                                                                                                                                                                                                                                                                                                        |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| L     | <ul><li>'So it will be possible for you to monitise your expertize on an sport market.'</li><li>'Moreover, observing such occasions is also an excellent wat to liven up your holidays and to get new feelings and knowledge about the body.'</li><li>'i claim that it brings you, your family and friends closer.'</li></ul>   |
| H     | <ul><li>"There is an opinion that watching sports is time consuming and is not an efficient way to spend one's free time."</li><li>'It develops a logical thinking and concentration.'</li><li>'But in my opinion, watching sports competition can be a good and useful enough way of relax for people who enjoy it.'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.7959   |

## 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("Zlovoblachko/dim2_Qwen_setfit_model")
# Run inference
preds = model(" Watching sports helps people to develop their social life.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 4   | 18.0633 | 48  |

| Label | Training Sample Count |
|:------|:----------------------|
| L     | 150                   |
| H     | 150                   |

### 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: 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0004 | 1    | 0.2694        | -               |
| 0.0177 | 50   | 0.2589        | -               |
| 0.0353 | 100  | 0.2489        | -               |
| 0.0530 | 150  | 0.1486        | -               |
| 0.0706 | 200  | 0.0375        | -               |
| 0.0883 | 250  | 0.0014        | -               |
| 0.1059 | 300  | 0.0           | -               |
| 0.1236 | 350  | 0.0           | -               |
| 0.1412 | 400  | 0.0           | -               |
| 0.1589 | 450  | 0.0           | -               |
| 0.1766 | 500  | 0.0           | -               |
| 0.1942 | 550  | 0.0           | -               |
| 0.2119 | 600  | 0.0           | -               |
| 0.2295 | 650  | 0.0           | -               |
| 0.2472 | 700  | 0.0           | -               |
| 0.2648 | 750  | 0.0           | -               |
| 0.2825 | 800  | 0.0           | -               |
| 0.3001 | 850  | 0.0           | -               |
| 0.3178 | 900  | 0.0           | -               |
| 0.3355 | 950  | 0.0           | -               |
| 0.3531 | 1000 | 0.0           | -               |
| 0.3708 | 1050 | 0.0           | -               |
| 0.3884 | 1100 | 0.0           | -               |
| 0.4061 | 1150 | 0.0           | -               |
| 0.4237 | 1200 | 0.0           | -               |
| 0.4414 | 1250 | 0.0           | -               |
| 0.4590 | 1300 | 0.0           | -               |
| 0.4767 | 1350 | 0.0           | -               |
| 0.4944 | 1400 | 0.0           | -               |
| 0.5120 | 1450 | 0.0           | -               |
| 0.5297 | 1500 | 0.0           | -               |
| 0.5473 | 1550 | 0.0           | -               |
| 0.5650 | 1600 | 0.0           | -               |
| 0.5826 | 1650 | 0.0           | -               |
| 0.6003 | 1700 | 0.0           | -               |
| 0.6179 | 1750 | 0.0           | -               |
| 0.6356 | 1800 | 0.0           | -               |
| 0.6532 | 1850 | 0.0           | -               |
| 0.6709 | 1900 | 0.0           | -               |
| 0.6886 | 1950 | 0.0           | -               |
| 0.7062 | 2000 | 0.0           | -               |
| 0.7239 | 2050 | 0.0           | -               |
| 0.7415 | 2100 | 0.0           | -               |
| 0.7592 | 2150 | 0.0           | -               |
| 0.7768 | 2200 | 0.0           | -               |
| 0.7945 | 2250 | 0.0           | -               |
| 0.8121 | 2300 | 0.0           | -               |
| 0.8298 | 2350 | 0.0           | -               |
| 0.8475 | 2400 | 0.0           | -               |
| 0.8651 | 2450 | 0.0           | -               |
| 0.8828 | 2500 | 0.0           | -               |
| 0.9004 | 2550 | 0.0           | -               |
| 0.9181 | 2600 | 0.0           | -               |
| 0.9357 | 2650 | 0.0           | -               |
| 0.9534 | 2700 | 0.0           | -               |
| 0.9710 | 2750 | 0.0           | -               |
| 0.9887 | 2800 | 0.0           | -               |

### Framework Versions
- Python: 3.11.13
- SetFit: 1.1.3
- Sentence Transformers: 5.0.0
- Transformers: 4.55.0
- PyTorch: 2.6.0+cu124
- Datasets: 4.0.0
- Tokenizers: 0.21.4

## 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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