---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: "most of the results look perfectly healthy, but there are a few that are\
    \ over thresholds, they are: \n\n "
- text: 'so here''s my question: is it possible to have a very slow natural breathing
    rate and be healthy?'
- text: 'never had an issue with reflux before, i eat very healthy....but gave it
    a go.  '
- text: does every other person at their healthy weight range feel like this all the
    time?
- text: penis overall just looks very unhealthy compared to last year and i have no
    idea what it could be and everywhere i’ve looked suggest it is penile cancer.
metrics:
- accuracy
- precision
- recall
- f1
pipeline_tag: text-classification
library_name: setfit
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: 0.9411764705882353
      name: Accuracy
    - type: precision
      value: 0.9411764705882353
      name: Precision
    - type: recall
      value: 0.9411764705882353
      name: Recall
    - type: f1
      value: 0.9411764705882353
      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                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
|:----------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| lifestyle | <ul><li>'i am 21, live a healthy lifestyle, i don’t smoke and only drink socially every once in a while.'</li><li>'i know staying up all night and sleeping during the day isnt good for you, brain wise and hormonaly, i will try my best to eat healthy and have good sleep hygiene, but am i risking my health or anything ?'</li><li>'i have been eating a bit more unhealthy foods like fried foods.\n\n'</li></ul>                                       |
| disease   | <ul><li>'i was told there’s no way to know what caused it &amp; no treatment options or ways to help fix it besides med options to help manage symptoms but my doc doesn’t want to start that yet due to me being “young &amp; healthy”.'</li><li>"i gave the whole history because i've been very ill like this for 6 years now after being healthy."</li><li>'no baseline medical information included, so the following assumes you are healthy.'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy | Precision | Recall | F1     |
|:--------|:---------|:----------|:-------|:-------|
| **all** | 0.9412   | 0.9412    | 0.9412 | 0.9412 |

## 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("never had an issue with reflux before, i eat very healthy....but gave it a go.  ")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 12  | 25.8308 | 60  |

| Label     | Training Sample Count |
|:----------|:----------------------|
| disease   | 30                    |
| lifestyle | 35                    |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (10, 10)
- 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
- l2_weight: 0.01
- seed: 3786
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0061 | 1    | 0.2143        | -               |
| 0.3067 | 50   | 0.2243        | -               |
| 0.6135 | 100  | 0.0812        | -               |
| 0.9202 | 150  | 0.0019        | -               |
| 1.2270 | 200  | 0.0003        | -               |
| 1.5337 | 250  | 0.0002        | -               |
| 1.8405 | 300  | 0.0002        | -               |
| 2.1472 | 350  | 0.0001        | -               |
| 2.4540 | 400  | 0.0001        | -               |
| 2.7607 | 450  | 0.0001        | -               |
| 3.0675 | 500  | 0.0001        | -               |
| 3.3742 | 550  | 0.0001        | -               |
| 3.6810 | 600  | 0.0001        | -               |
| 3.9877 | 650  | 0.0001        | -               |
| 4.2945 | 700  | 0.0001        | -               |
| 4.6012 | 750  | 0.0001        | -               |
| 4.9080 | 800  | 0.0001        | -               |
| 5.2147 | 850  | 0.0001        | -               |
| 5.5215 | 900  | 0.0001        | -               |
| 5.8282 | 950  | 0.0001        | -               |
| 6.1350 | 1000 | 0.0           | -               |
| 6.4417 | 1050 | 0.0           | -               |
| 6.7485 | 1100 | 0.0           | -               |
| 7.0552 | 1150 | 0.0           | -               |
| 7.3620 | 1200 | 0.0           | -               |
| 7.6687 | 1250 | 0.0           | -               |
| 7.9755 | 1300 | 0.0           | -               |
| 8.2822 | 1350 | 0.0           | -               |
| 8.5890 | 1400 | 0.0           | -               |
| 8.8957 | 1450 | 0.0           | -               |
| 9.2025 | 1500 | 0.0           | -               |
| 9.5092 | 1550 | 0.0           | -               |
| 9.8160 | 1600 | 0.0           | -               |

### Framework Versions
- Python: 3.11.7
- SetFit: 1.1.1
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.5.1
- Datasets: 3.2.0
- 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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