---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: Chapman hits winning double as Blue Jays complete sweep of Red Sox with 3-2
    victory
- text: Opinion | The Election No One Seems to Want Is Coming Right at Us
- text: How to watch The Real Housewives of Miami new episode free Jan. 10
- text: Vitamin Sea Brewing set to open 2nd brewery and taproom in Mass.
- text: Opinion | When the World Feels Dark, Seek Out Delight
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-mpnet-base-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.7060702875399361
      name: Accuracy
---

# 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:** 9 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                                                                                                                                                                                                                                                                                                            |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 3     | <ul><li>'A Reinvented True Detective Plays It Cool'</li><li>"It's owl season in Massachusetts. Here's how to spot them"</li><li>'Taylor Swift class at Harvard: Professor needs to hire more teaching assistants'</li></ul>                                                                                         |
| 6     | <ul><li>'Springfield Mayor Domenic Sarno tests positive for COVID-19'</li><li>'How to Take Care of Your Skin in the Fall and Winter'</li><li>'Subbing plant-based milk for dairy options is a healthy decision'</li></ul>                                                                                           |
| 2     | <ul><li>'Mattel Has a New Cherokee Barbie. Not Everyone Is Happy About It.'</li><li>'Who Is Alan Garber, Harvards Interim President?'</li><li>'Springfield Marine training in Japan near Mount Fuji (Photos)'</li></ul>                                                                                             |
| 0     | <ul><li>'Heres which Northampton businesses might soon get all-alcohol liquor licenses'</li><li>'People in Business: Jan. 15, 2024'</li><li>'Come Home With Memories, Not a Shocking Phone Bill'</li></ul>                                                                                                          |
| 7     | <ul><li>'3 Patriots vs. Chiefs predictions'</li><li>'Tuskegee vs. Alabama State  How to watch college football'</li><li>'WMass Boys Basketball Season Stats Leaders: Who leads the region by class?'</li></ul>                                                                                                      |
| 8     | <ul><li>'Biting Cold Sweeping U.S. Hits the South With an Unfamiliar Freeze'</li><li>'Some Sunday storms and sun - Boston News, Weather, Sports'</li><li>'More snow on the way in Mass. on Tuesday with slippery evening commute'</li></ul>                                                                         |
| 4     | <ul><li>'title'</li><li>'This sentence is label'</li><li>'This sentence is label'</li></ul>                                                                                                                                                                                                                         |
| 1     | <ul><li>'Two cars crash through former Boston Market in Saugus'</li><li>'U.S. Naval Officer Who Helped China Is Sentenced to 2 Years in Prison'</li><li>'American Airlines flight attendant arrested after allegedly filming teenage girl in bathroom on flight to Boston - Boston News, Weather, Sports'</li></ul> |
| 5     | <ul><li>'Opinion | Why Wasnt DeSantis the Guy?'</li><li>'Reports Say Pope Francis Is Evicting U.S. Cardinal From His Vatican Home'</li><li>'Biden Says Its Self-Evident That Trump Supported an Insurrection'</li></ul>                                                                                             |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.7061   |

## 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("Kevinger/setfit-hub-report")
# Run inference
preds = model("Opinion | When the World Feels Dark, Seek Out Delight")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 1   | 7.2993 | 21  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 16                    |
| 1     | 16                    |
| 2     | 16                    |
| 3     | 16                    |
| 4     | 9                     |
| 5     | 16                    |
| 6     | 16                    |
| 7     | 16                    |
| 8     | 16                    |

### Training Hyperparameters
- batch_size: (16, 2)
- num_epochs: (1, 16)
- 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0010 | 1    | 0.3619        | -               |
| 0.0481 | 50   | 0.097         | -               |
| 0.0962 | 100  | 0.0327        | -               |
| 0.1442 | 150  | 0.0044        | -               |
| 0.1923 | 200  | 0.0013        | -               |
| 0.2404 | 250  | 0.0011        | -               |
| 0.2885 | 300  | 0.001         | -               |
| 0.3365 | 350  | 0.0008        | -               |
| 0.3846 | 400  | 0.001         | -               |
| 0.4327 | 450  | 0.0006        | -               |
| 0.4808 | 500  | 0.0008        | -               |
| 0.5288 | 550  | 0.0005        | -               |
| 0.5769 | 600  | 0.0012        | -               |
| 0.625  | 650  | 0.0005        | -               |
| 0.6731 | 700  | 0.0006        | -               |
| 0.7212 | 750  | 0.0004        | -               |
| 0.7692 | 800  | 0.0005        | -               |
| 0.8173 | 850  | 0.0005        | -               |
| 0.8654 | 900  | 0.0006        | -               |
| 0.9135 | 950  | 0.0014        | -               |
| 0.9615 | 1000 | 0.0006        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.16.1
- Tokenizers: 0.15.0

## 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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