---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: Sonos speakers are up to 25 percent off, plus the rest of this week's best
    tech deals | Engadget - Engadget
- text: Judy Blume says her quote about being 'behind' J.K. Rowling was 'taken out
    of context' as she clarifies support for the trans community - Yahoo Entertainment
- text: Mock Draft Monday | Here's who CBS Sports has the Commanders taking in the
    first round - Washington Commanders
- text: GIANT 130-foot asteroid rushing towards Earth TODAY at 42404 kmph, NASA warns
    - HT Tech
- text: Jonathan Majors & Manager Entertainment 360 Part Ways; Actor Facing Domestic
    Violence Allegations In NYC - Deadline
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: 0.8577235772357723
      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:** 6 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                                                                                                                                                                                                                                                                                                                |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 4     | <ul><li>'The Super Mario Bros. Movie Expected To Pass $1 Billion, Biggest Movie Release This Year - Kotaku'</li><li>'Richard Lewis Has Parkinson’s Disease, Finished With Stand-Up Comedy Career - Deadline'</li><li>"EXCLUSIVE Dame Mary Quant's plans for 'small funeral' near her home - Daily Mail"</li></ul>       |
| 3     | <ul><li>'GPT-5 not in the works currently: OpenAI CEO Sam Altman - The Economic Times'</li><li>'The 2023 Am Law 100: Ranked by Gross Revenue | The American Lawyer - Law.com'</li><li>"Savings Account or CD: What's Smarter Right Now? - Investopedia"</li></ul>                                                       |
| 5     | <ul><li>"I used all 2023 flagships — here's why the Galaxy S23 Ultra is my favorite phone - Android Central"</li><li>"Google's AI experts on the future of artificial intelligence | 60 Minutes - CBS News"</li><li>'You can snag a first-gen Apple Watch SE for just $149 right now - The Verge'</li></ul>             |
| 0     | <ul><li>'Fernando Tatis Jr. to make Padres return - MLB.com'</li><li>'Knicks-Cavaliers Game 3 live updates: Score, news, more from NBA Playoffs - New York Post '</li><li>'Josh Donaldson Likely To Miss Multiple Weeks With Hamstring Strain - MLB Trade Rumors'</li></ul>                                             |
| 2     | <ul><li>'Are Fermented Foods Actually Good for You? - Lifehacker'</li><li>'ADHD medication | New study says more students self-medicating with ADHD medication - WTVD-TV'</li><li>'Mom With Microscopic Colitis Had Diarrhea up to 40 Times a Day - Insider'</li></ul>                                                  |
| 1     | <ul><li>'Creating Artificial Avians: A Novel Neural Network Generates Realistic Bird Pictures from Text using Common Sense - Neuroscience News'</li><li>'Consciousness begins with feeling, not thinking | Antonio Damasio, Hanna Damasio, - IAI'</li><li>'The Myth of Objective Data - The MIT Press Reader'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.8577   |

## 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-newsapi")
# Run inference
preds = model("GIANT 130-foot asteroid rushing towards Earth TODAY at 42404 kmph, NASA warns - HT Tech")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 4   | 9.1771 | 22  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 16                    |
| 1     | 16                    |
| 2     | 16                    |
| 3     | 16                    |
| 4     | 16                    |
| 5     | 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0021 | 1    | 0.2926        | -               |
| 0.1042 | 50   | 0.0446        | -               |
| 0.2083 | 100  | 0.0023        | -               |
| 0.3125 | 150  | 0.0011        | -               |
| 0.4167 | 200  | 0.001         | -               |
| 0.5208 | 250  | 0.0007        | -               |
| 0.625  | 300  | 0.0007        | -               |
| 0.7292 | 350  | 0.0009        | -               |
| 0.8333 | 400  | 0.0075        | -               |
| 0.9375 | 450  | 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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