---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
- precision
- recall
- f1
widget:
- text: 'VALID THRU 02/10/2015 '
- text: Les chaines Universal+
- text: "\tminute = second * 60,"
- text: ': Session'
- text: Phil Klay
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: 0.87
      name: Accuracy
    - type: precision
      value: 0.8452380952380952
      name: Precision
    - type: recall
      value: 0.8452380952380952
      name: Recall
    - type: f1
      value: 0.8452380952380952
      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                                                                                                                                             |
|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------|
| True  | <ul><li>'ace} UNIV FEATURING CHAMILLIONAIRE ©) '</li><li>'ee — Pra yon t “pink you are S© Mer '</li><li>'ae PTAA Be hs B corms of Rermee '</li></ul> |
| False | <ul><li>'mmmmmmmmmmlli'</li><li>'Manage Cookie Preferences'</li><li>'Supply Partners'</li></ul>                                                      |

## Evaluation

### Metrics
| Label   | Accuracy | Precision | Recall | F1     |
|:--------|:---------|:----------|:-------|:-------|
| **all** | 0.87     | 0.8452    | 0.8452 | 0.8452 |

## 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(": Session")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 1   | 8.4575 | 265 |

| Label | Training Sample Count |
|:------|:----------------------|
| False | 384                   |
| True  | 416                   |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0005 | 1    | 0.4077        | -               |
| 0.025  | 50   | 0.2689        | -               |
| 0.05   | 100  | 0.2505        | -               |
| 0.075  | 150  | 0.1787        | -               |
| 0.1    | 200  | 0.1602        | -               |
| 0.125  | 250  | 0.1381        | -               |
| 0.15   | 300  | 0.1753        | -               |
| 0.175  | 350  | 0.018         | -               |
| 0.2    | 400  | 0.024         | -               |
| 0.225  | 450  | 0.0863        | -               |
| 0.25   | 500  | 0.0504        | -               |
| 0.275  | 550  | 0.0204        | -               |
| 0.3    | 600  | 0.0124        | -               |
| 0.325  | 650  | 0.066         | -               |
| 0.35   | 700  | 0.1305        | -               |
| 0.375  | 750  | 0.0599        | -               |
| 0.4    | 800  | 0.0323        | -               |
| 0.425  | 850  | 0.0039        | -               |
| 0.45   | 900  | 0.0131        | -               |
| 0.475  | 950  | 0.004         | -               |
| 0.5    | 1000 | 0.0016        | -               |
| 0.525  | 1050 | 0.0139        | -               |
| 0.55   | 1100 | 0.0189        | -               |
| 0.575  | 1150 | 0.0533        | -               |
| 0.6    | 1200 | 0.0645        | -               |
| 0.625  | 1250 | 0.0005        | -               |
| 0.65   | 1300 | 0.0111        | -               |
| 0.675  | 1350 | 0.002         | -               |
| 0.7    | 1400 | 0.0082        | -               |
| 0.725  | 1450 | 0.0009        | -               |
| 0.75   | 1500 | 0.0018        | -               |
| 0.775  | 1550 | 0.0003        | -               |
| 0.8    | 1600 | 0.0108        | -               |
| 0.825  | 1650 | 0.0009        | -               |
| 0.85   | 1700 | 0.0003        | -               |
| 0.875  | 1750 | 0.0009        | -               |
| 0.9    | 1800 | 0.0038        | -               |
| 0.925  | 1850 | 0.0406        | -               |
| 0.95   | 1900 | 0.0012        | -               |
| 0.975  | 1950 | 0.0024        | -               |
| 1.0    | 2000 | 0.0004        | -               |

### 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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