---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
- precision
- recall
- f1
widget:
- text: Maintenance to the cambridge.org website is scheduled for 14 March at 12am
    – 8am GMT.
- text: Quarterly Earnings
- text: 'So set sail for Long John Silver''s and discover why wa''re America''s most
    popular sealood vestments antannro fi '
- text: "\n                                                        OPEC oil price\
    \ annually 1960-2024\n                                                    "
- text: 'RUSSELL WILSON OF THE SEATTLE SEAHAWKS — DURING SUPER BOWL XLVIII '
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: 0.8083333333333333
      name: Accuracy
    - type: precision
      value: 0.7894736842105263
      name: Precision
    - type: recall
      value: 0.8035714285714286
      name: Recall
    - type: f1
      value: 0.7964601769911505
      name: F1
---

# 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) -->
<!-- - **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                                                                                                                                                                                                         |
|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| False | <ul><li>'Learn more about this provider'</li><li>'Verletzte und Festnahmen'</li><li>'Bulgaria'</li></ul>                                                                                                         |
| True  | <ul><li>'Free Quotes on Doors '</li><li>'Pakistan Cricket Board, Gaddafi Stadium, Ferozepur Road, Lahore, Pakistan. E-Mail: careers@pcb.com.pk '</li><li>"‘here's a new predator in the urban jungle "</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy | Precision | Recall | F1     |
|:--------|:---------|:----------|:-------|:-------|
| **all** | 0.8083   | 0.7895    | 0.8036 | 0.7965 |

## 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("Quarterly Earnings")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 1   | 8.2229 | 242 |

| Label | Training Sample Count |
|:------|:----------------------|
| False | 236                   |
| True  | 244                   |

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

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0008 | 1    | 0.3892        | -               |
| 0.0417 | 50   | 0.2262        | -               |
| 0.0833 | 100  | 0.2138        | -               |
| 0.125  | 150  | 0.1058        | -               |
| 0.1667 | 200  | 0.1327        | -               |
| 0.2083 | 250  | 0.098         | -               |
| 0.25   | 300  | 0.0719        | -               |
| 0.2917 | 350  | 0.0634        | -               |
| 0.3333 | 400  | 0.0021        | -               |
| 0.375  | 450  | 0.0084        | -               |
| 0.4167 | 500  | 0.0799        | -               |
| 0.4583 | 550  | 0.0822        | -               |
| 0.5    | 600  | 0.0775        | -               |
| 0.5417 | 650  | 0.0114        | -               |
| 0.5833 | 700  | 0.0013        | -               |
| 0.625  | 750  | 0.0121        | -               |
| 0.6667 | 800  | 0.1034        | -               |
| 0.7083 | 850  | 0.0539        | -               |
| 0.75   | 900  | 0.0076        | -               |
| 0.7917 | 950  | 0.0114        | -               |
| 0.8333 | 1000 | 0.0223        | -               |
| 0.875  | 1050 | 0.0208        | -               |
| 0.9167 | 1100 | 0.0246        | -               |
| 0.9583 | 1150 | 0.0098        | -               |
| 1.0    | 1200 | 0.003         | -               |

### Framework Versions
- Python: 3.11.0
- SetFit: 1.0.3
- Sentence Transformers: 2.3.0
- Transformers: 4.37.2
- PyTorch: 2.2.1+cu121
- Datasets: 2.16.1
- Tokenizers: 0.15.1

## 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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