---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Remote work is allowed provided the candidate has adequate home systems to
    support the high internet data demands required for this position
- text: · High School degree required, though we will consider candidates with equivalent
    education or experience · Experience and verifiable competence in building systems
    including HVAC, steam, gas, electrical, plumbing, repair work and/or equivalent
    training are required
- text: 'Beyond a light and engaging work environment, team members receive the following
    benefits: Competitive salary PPO, HSA, and life insurance options 401k plan Open
    vacation policy (discretionary time-off) DIY schedule for balancing personal and
    professional responsibilities Equipment and tools for you to do your job  Tracker
    is an equal opportunity employer'
- text: Job descriptionA leading real estate firm in New Jersey is seeking an administrative
    Marketing Coordinator with some experience in graphic design
- text: QualificationsPortfolio of published articles (electronic and print)Excellent
    writing and editing skills in EnglishEvidence of collaboration with clients and
    within an office environmentHands-on experience with MailChimp, WordPress, SEO
    tools, Microsoft Suite, and social mediaFamiliarity with web publicationsPhotography
    skills preferred
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/paraphrase-mpnet-base-v2
---

# SetFit with sentence-transformers/paraphrase-mpnet-base-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| skip  | <ul><li>'Additional tasks may be assigned based on organizationalneeds and priorities.Culture:At the Kids’ Book Bank, we are a small but mighty team dedicated to getting more books to more children and fostering a love of reading'</li><li>'Founded in Sweden in 1907, today SKF is publicly traded on the Nasdaq Stockholm with annual sales in 2020 of approximately $10 billion'</li><li>'Compensation:$55,000-$75,000/year'</li></ul>                                                                                                                      |
| keep  | <ul><li>'Requirements Must have at least 2 years Arizona or Colorado civil litigation experience, knowledge of both state and federal procedural rules, superior organizational skills, strong attention to detail and the ability to provide secretarial/administrative support to experienced trial attorneys'</li><li>'Experience with EMR systems Knowledge of Microsoft products (Word, Excel, Outlook)'</li><li>"Requirements:A bachelor's degree in exercise science, kinesiology, sports science, or a related field preferred but not required"</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("Remote work is allowed provided the candidate has adequate home systems to support the high internet data demands required for this position")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 1   | 24.1078 | 84  |

| Label | Training Sample Count |
|:------|:----------------------|
| skip  | 52                    |
| keep  | 50                    |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (4, 4)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-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: True

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0039 | 1    | 0.3866        | -               |
| 0.1961 | 50   | 0.2075        | -               |
| 0.3922 | 100  | 0.0179        | -               |
| 0.5882 | 150  | 0.0005        | -               |
| 0.7843 | 200  | 0.0003        | -               |
| 0.9804 | 250  | 0.0002        | -               |
| 1.0    | 255  | -             | 0.1885          |
| 1.1765 | 300  | 0.0002        | -               |
| 1.3725 | 350  | 0.0001        | -               |
| 1.5686 | 400  | 0.0001        | -               |
| 1.7647 | 450  | 0.0001        | -               |
| 1.9608 | 500  | 0.0001        | -               |
| 2.0    | 510  | -             | 0.1909          |
| 2.1569 | 550  | 0.0001        | -               |
| 2.3529 | 600  | 0.0001        | -               |
| 2.5490 | 650  | 0.0001        | -               |
| 2.7451 | 700  | 0.0001        | -               |
| 2.9412 | 750  | 0.0001        | -               |
| 3.0    | 765  | -             | 0.1904          |
| 3.1373 | 800  | 0.0001        | -               |
| 3.3333 | 850  | 0.0001        | -               |
| 3.5294 | 900  | 0.0001        | -               |
| 3.7255 | 950  | 0.0001        | -               |
| 3.9216 | 1000 | 0.0001        | -               |
| 4.0    | 1020 | -             | 0.1910          |

### Framework Versions
- Python: 3.12.3
- SetFit: 1.1.3
- Sentence Transformers: 5.2.0
- Transformers: 4.57.3
- PyTorch: 2.5.1+cu124
- Datasets: 4.4.2
- 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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