setfit safetensors mpnet sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:sentence-transformers/paraphrase-mpnet-base-v2 base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2 model-index region:us
---
base_model: sentence-transformers/paraphrase-mpnet-base-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Proof Reader
- text: product owner
- text: chief community officer
- text: planner
- text: information technology administrator
inference: true
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: 1.0
      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:** 4 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>'academic head'</li><li>'admin director'</li><li>'admin head'</li></ul>                                |
| 4     | <ul><li>'account director'</li><li>'area vice president'</li><li>'assistant chief executive officer'</li></ul> |
| 2     | <ul><li>'account manager'</li><li>'admin'</li><li>'admin officer'</li></ul>                                    |
| 1     | <ul><li>'accountant'</li><li>'administrator'</li><li>'adviser'</li></ul>                                       |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 1.0      |

## 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("planner")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 1   | 2.1124 | 6   |

| Label | Training Sample Count |
|:------|:----------------------|
| 1     | 380                   |
| 2     | 107                   |
| 3     | 67                    |
| 4     | 193                   |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (3, 3)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0005 | 1    | 0.2621        | -               |
| 0.0268 | 50   | 0.2631        | -               |
| 0.0535 | 100  | 0.2043        | -               |
| 0.0803 | 150  | 0.1561        | -               |
| 0.1071 | 200  | 0.203         | -               |
| 0.1338 | 250  | 0.1823        | -               |
| 0.1606 | 300  | 0.1082        | -               |
| 0.1874 | 350  | 0.0702        | -               |
| 0.2141 | 400  | 0.1159        | -               |
| 0.2409 | 450  | 0.0532        | -               |
| 0.2677 | 500  | 0.0767        | -               |
| 0.2944 | 550  | 0.0965        | -               |
| 0.3212 | 600  | 0.0479        | -               |
| 0.3480 | 650  | 0.0353        | -               |
| 0.3747 | 700  | 0.0235        | -               |
| 0.4015 | 750  | 0.0028        | -               |
| 0.4283 | 800  | 0.004         | -               |
| 0.4550 | 850  | 0.0908        | -               |
| 0.4818 | 900  | 0.0078        | -               |
| 0.5086 | 950  | 0.0149        | -               |
| 0.5353 | 1000 | 0.0841        | -               |
| 0.5621 | 1050 | 0.0141        | -               |
| 0.5889 | 1100 | 0.0328        | -               |
| 0.6156 | 1150 | 0.0031        | -               |
| 0.6424 | 1200 | 0.0027        | -               |
| 0.6692 | 1250 | 0.0205        | -               |
| 0.6959 | 1300 | 0.0584        | -               |
| 0.7227 | 1350 | 0.002         | -               |
| 0.7495 | 1400 | 0.0009        | -               |
| 0.7762 | 1450 | 0.0018        | -               |
| 0.8030 | 1500 | 0.001         | -               |
| 0.8298 | 1550 | 0.0004        | -               |
| 0.8565 | 1600 | 0.0008        | -               |
| 0.8833 | 1650 | 0.0006        | -               |
| 0.9101 | 1700 | 0.0021        | -               |
| 0.9368 | 1750 | 0.009         | -               |
| 0.9636 | 1800 | 0.0031        | -               |
| 0.9904 | 1850 | 0.0024        | -               |
| 1.0171 | 1900 | 0.0327        | -               |
| 1.0439 | 1950 | 0.0257        | -               |
| 1.0707 | 2000 | 0.0006        | -               |
| 1.0974 | 2050 | 0.0009        | -               |
| 1.1242 | 2100 | 0.0006        | -               |
| 1.1510 | 2150 | 0.0004        | -               |
| 1.1777 | 2200 | 0.0011        | -               |
| 1.2045 | 2250 | 0.0004        | -               |
| 1.2313 | 2300 | 0.0012        | -               |
| 1.2580 | 2350 | 0.0005        | -               |
| 1.2848 | 2400 | 0.0013        | -               |
| 1.3116 | 2450 | 0.0007        | -               |
| 1.3383 | 2500 | 0.0002        | -               |
| 1.3651 | 2550 | 0.0005        | -               |
| 1.3919 | 2600 | 0.0006        | -               |
| 1.4186 | 2650 | 0.0006        | -               |
| 1.4454 | 2700 | 0.0004        | -               |
| 1.4722 | 2750 | 0.0004        | -               |
| 1.4989 | 2800 | 0.0008        | -               |
| 1.5257 | 2850 | 0.0003        | -               |
| 1.5525 | 2900 | 0.0012        | -               |
| 1.5792 | 2950 | 0.0006        | -               |
| 1.6060 | 3000 | 0.0003        | -               |
| 1.6328 | 3050 | 0.0002        | -               |
| 1.6595 | 3100 | 0.0026        | -               |
| 1.6863 | 3150 | 0.0003        | -               |
| 1.7131 | 3200 | 0.0003        | -               |
| 1.7398 | 3250 | 0.0003        | -               |
| 1.7666 | 3300 | 0.0003        | -               |
| 1.7934 | 3350 | 0.0003        | -               |
| 1.8201 | 3400 | 0.0004        | -               |
| 1.8469 | 3450 | 0.0003        | -               |
| 1.8737 | 3500 | 0.0005        | -               |
| 1.9004 | 3550 | 0.0003        | -               |
| 1.9272 | 3600 | 0.0003        | -               |
| 1.9540 | 3650 | 0.0002        | -               |
| 1.9807 | 3700 | 0.0003        | -               |
| 2.0075 | 3750 | 0.0003        | -               |
| 2.0343 | 3800 | 0.0003        | -               |
| 2.0610 | 3850 | 0.0002        | -               |
| 2.0878 | 3900 | 0.0004        | -               |
| 2.1146 | 3950 | 0.0003        | -               |
| 2.1413 | 4000 | 0.0003        | -               |
| 2.1681 | 4050 | 0.0002        | -               |
| 2.1949 | 4100 | 0.0541        | -               |
| 2.2216 | 4150 | 0.0002        | -               |
| 2.2484 | 4200 | 0.0003        | -               |
| 2.2752 | 4250 | 0.0582        | -               |
| 2.3019 | 4300 | 0.0003        | -               |
| 2.3287 | 4350 | 0.0002        | -               |
| 2.3555 | 4400 | 0.0003        | -               |
| 2.3822 | 4450 | 0.0005        | -               |
| 2.4090 | 4500 | 0.0004        | -               |
| 2.4358 | 4550 | 0.0003        | -               |
| 2.4625 | 4600 | 0.0003        | -               |
| 2.4893 | 4650 | 0.0002        | -               |
| 2.5161 | 4700 | 0.0002        | -               |
| 2.5428 | 4750 | 0.0003        | -               |
| 2.5696 | 4800 | 0.0008        | -               |
| 2.5964 | 4850 | 0.0002        | -               |
| 2.6231 | 4900 | 0.0002        | -               |
| 2.6499 | 4950 | 0.0005        | -               |
| 2.6767 | 5000 | 0.0003        | -               |
| 2.7034 | 5050 | 0.0002        | -               |
| 2.7302 | 5100 | 0.0004        | -               |
| 2.7570 | 5150 | 0.0002        | -               |
| 2.7837 | 5200 | 0.0005        | -               |
| 2.8105 | 5250 | 0.0004        | -               |
| 2.8373 | 5300 | 0.0394        | -               |
| 2.8640 | 5350 | 0.0002        | -               |
| 2.8908 | 5400 | 0.0399        | -               |
| 2.9176 | 5450 | 0.0002        | -               |
| 2.9443 | 5500 | 0.0002        | -               |
| 2.9711 | 5550 | 0.0002        | -               |
| 2.9979 | 5600 | 0.0002        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.3.1+cu121
- Datasets: 2.20.0
- Tokenizers: 0.15.2

## 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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