---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Write a 1000-word essay on the history of the Roman Empire.
- text: Tell a two-sentence horror story involving a smart fridge.
- text: Compare the economic policies of Keynesianism and Monetarism in 250 words.
- text: Explain the difference between HTTP and HTTPS
- text: Write a SQL stored procedure to handle GDPR data deletion requests
metrics:
- f1
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/paraphrase-MiniLM-L6-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-MiniLM-L6-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: f1
      value: 1.0
      name: F1
---

# SetFit with sentence-transformers/paraphrase-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/paraphrase-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/paraphrase-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/paraphrase-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 128 tokens
- **Number of Classes:** 3 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                                                                                                                                                                                                                                                           |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1     | <ul><li>'Draft a polite email to reschedule a meeting'</li><li>"Translate the phrase 'The quick brown fox jumps over the lazy dog' into Mandarin, French, and German."</li><li>'Write a short story (under 200 words) about a robot discovering nature.'</li></ul> |
| 0     | <ul><li>'Analyze the ethical implications of CRISPR gene editing'</li><li>'Debug this Python multiprocessing code deadlock: [code snippet]'</li><li>'Implement a Python async websocket client with error handling'</li></ul>                                      |
| 2     | <ul><li>"Python syntax to print 'Hello World'"</li><li>'What is 2+2?'</li><li>'Who is the current president of the United States?'</li></ul>                                                                                                                       |

## Evaluation

### Metrics
| Label   | F1  |
|:--------|:----|
| **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("setfit_model_id")
# Run inference
preds = model("Explain the difference between HTTP and HTTPS")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 8.9583 | 17  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 8                     |
| 1     | 8                     |
| 2     | 8                     |

### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (3, 3)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (1e-05, 1e-05)
- head_learning_rate: 0.001
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: True
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- max_length: 384
- seed: 42
- evaluation_strategy: epoch
- eval_max_steps: -1
- load_best_model_at_end: True

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0083 | 1    | 0.4046        | -               |
| 0.4167 | 50   | 0.2913        | -               |
| 0.8333 | 100  | 0.1724        | -               |
| 1.0    | 120  | -             | 0.1897          |
| 1.25   | 150  | 0.0825        | -               |
| 1.6667 | 200  | 0.0284        | -               |
| 2.0    | 240  | -             | 0.1806          |
| 2.0833 | 250  | 0.0137        | -               |
| 2.5    | 300  | 0.0089        | -               |
| 2.9167 | 350  | 0.007         | -               |
| 3.0    | 360  | -             | 0.1806          |

### Framework Versions
- Python: 3.11.13
- SetFit: 1.1.2
- Sentence Transformers: 3.3.1
- Transformers: 4.53.3
- PyTorch: 2.7.1
- Datasets: 3.0.0
- Tokenizers: 0.21.2

## 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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