---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 'Code Is an Afterthought [source: Hacker News] [topic: engineering]'
- text: 'Iran has accepted terms of ceasefire, per NYT www.... [source: bluesky links]
    [topic: feeds]'
- text: 'Someone at BrowserStack is Leaking Users'' Email Address [source: Terence
    Eden''s Blog] [topic: engineering]'
- text: 'Touchscreens expose a major spatial reasoning gap in LLM agents [source:
    Reddit Home] [topic: AI]'
- text: 'New Strides Made on Deceptively Simple ''Lonely Runner'' Problem [source:
    Hacker News: Newest] [topic: other]'
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/all-MiniLM-L6-v2
---

# SetFit with sentence-transformers/all-MiniLM-L6-v2

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) 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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 256 tokens
- **Number of Classes:** 2 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                                                                                                                                                                                                                                                                                                                                                                                           |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0     | <ul><li>'Sad Story of My Google Workspace Account Suspension [source: Hacker News] [topic: engineering]'</li><li>'“CEO said a thing!” [source: marcus-on-ai] [topic: Leadership / Corporate Culture]'</li><li>'How to Be Silicon Valley [source: Paul Graham: Essays] [topic: startup]'</li></ul>                                                                                                  |
| 1     | <ul><li>'Organizing in Hard Times: Lessons from Read This When Things Fall Apart [source: bluesky links] [topic: leadership|philosophy|startup]'</li><li>'Iran-Linked Hackers Sabotaging US Energy and Water Infrastructure [source: bluesky links] [topic: security]'</li><li>'Live Rocket Telemetry and Logging in Two Weeks [source: Hacker News] [topic: Observability / Telemetry]'</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("setfit_model_id")
# Run inference
preds = model("Code Is an Afterthought [source: Hacker News] [topic: engineering]")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 6   | 12.5652 | 27  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 87                    |
| 1     | 74                    |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- 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
- evaluation_strategy: epoch
- eval_max_steps: -1
- load_best_model_at_end: True

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0025 | 1    | 0.4129        | -               |
| 0.1241 | 50   | 0.2716        | -               |
| 0.2481 | 100  | 0.2432        | -               |
| 0.3722 | 150  | 0.2218        | -               |
| 0.4963 | 200  | 0.1869        | -               |
| 0.6203 | 250  | 0.1302        | -               |
| 0.7444 | 300  | 0.0617        | -               |
| 0.8685 | 350  | 0.0343        | -               |
| 0.9926 | 400  | 0.022         | -               |
| 1.0    | 403  | -             | 0.2546          |

### Framework Versions
- Python: 3.13.9
- SetFit: 1.1.3
- Sentence Transformers: 5.4.0
- Transformers: 4.50.3
- PyTorch: 2.11.0
- Datasets: 4.8.4
- 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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