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: 'for this reason and this reason only -- the power of its own steadfast ,
    hoity-toity convictions -- chelsea walls deserves a medal . '
- text: 'aside from minor tinkering , this is the same movie you probably loved in
    1994 , except that it looks even better . '
- text: 'cq ''s reflection of artists and the love of cinema-and-self suggests nothing
    less than a new voice that deserves to be considered as a possible successor to
    the best european directors . '
- text: 'i had to look away - this was god awful . '
- text: 'i ''ll bet the video game is a lot more fun than the film . '
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: 0.89
      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:** 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                                                                                                                                    |
|:---------|:--------------------------------------------------------------------------------------------------------------------------------------------|
| positive | <ul><li>'klein , charming in comedies like american pie and dead-on in election , '</li><li>'be fruitful '</li><li>'soulful and '</li></ul> |
| negative | <ul><li>'covered earlier and much better '</li><li>'it too is a bomb . '</li><li>'guilty about it '</li></ul>                               |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.89     |

## 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("DrGwin/setfit-paraphrase-mpnet-base-v2-sst2A")
# Run inference
preds = model("i had to look away - this was god awful . ")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 2   | 9.55   | 46  |

| Label    | Training Sample Count |
|:---------|:----------------------|
| negative | 40                    |
| positive | 60                    |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (4, 4)
- 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0030 | 1    | 0.4181        | -               |
| 0.1506 | 50   | 0.2514        | -               |
| 0.3012 | 100  | 0.0932        | -               |
| 0.4518 | 150  | 0.0029        | -               |
| 0.6024 | 200  | 0.001         | -               |
| 0.7530 | 250  | 0.0006        | -               |
| 0.9036 | 300  | 0.0006        | -               |
| 1.0    | 332  | -             | 0.1722          |
| 1.0542 | 350  | 0.0014        | -               |
| 1.2048 | 400  | 0.0004        | -               |
| 1.3554 | 450  | 0.0004        | -               |
| 1.5060 | 500  | 0.0095        | -               |
| 1.6566 | 550  | 0.0003        | -               |
| 1.8072 | 600  | 0.0003        | -               |
| 1.9578 | 650  | 0.0003        | -               |
| 2.0    | 664  | -             | 0.1820          |
| 2.1084 | 700  | 0.0003        | -               |
| 2.2590 | 750  | 0.0023        | -               |
| 2.4096 | 800  | 0.0003        | -               |
| 2.5602 | 850  | 0.0002        | -               |
| 2.7108 | 900  | 0.0002        | -               |
| 2.8614 | 950  | 0.0002        | -               |
| 3.0    | 996  | -             | 0.1970          |
| 3.0120 | 1000 | 0.0002        | -               |
| 3.1627 | 1050 | 0.0003        | -               |
| 3.3133 | 1100 | 0.0012        | -               |
| 3.4639 | 1150 | 0.0002        | -               |
| 3.6145 | 1200 | 0.0002        | -               |
| 3.7651 | 1250 | 0.0003        | -               |
| 3.9157 | 1300 | 0.001         | -               |
| 4.0    | 1328 | -             | 0.1810          |

### Framework Versions
- Python: 3.11.11
- SetFit: 1.1.1
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Datasets: 3.3.2
- Tokenizers: 0.21.0

## 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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