---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: sentence-transformers/paraphrase-mpnet-base-v2
metrics:
- accuracy
widget:
- text: Anyone 170 or below that takes Wegovy? Well it is, Ozempic and Wegovy are
    actually the same drug. So if you are taking Wegovy, it is affecting your insulin
    and glucose. I was on Trulicity, insurance made me switch to wegovy.
- text: New Ozempic and Wegovy side effects come to light - After I stopped taking
    it I developed Gallbladder disease and Pancreatitis
- text: 'The beginning of my Semaglutide journey! ???? #semaglutide #ozempic #wegovy
    #weightloss #health #prediabetes #semaglutideweightloss #change'
- text: I am on victoza. It works well for me. Ozempic made me sick so my doctor placed
    me back on victoza since it was working. I do want to mention that the side effects
    of victoza re the same as Ozempic. That includes thyroid issues.
- text: What's the cheapest way possible to get semaglutide? I'm currently taking
    2000mg of Metformin with compounded semaglutide with no issues. I have PCOS and
    not Type 2, so I sadly don't qualify for Ozempic through insurance.
pipeline_tag: text-classification
inference: true
---

# 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
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### 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1     | <ul><li>'I skipped this era, did she ever give a reason why she quit Ozempic? It’s fucking bananas to me she quit after finding something that works for the first time in a decade.'</li><li>"I was on Ozempic and I lost 40lbs but it stopped there. It cost me $225 Canadian per month. I'm not taking anymore."</li><li>"Maybe you can try Manjaro or a similar drug. Also, I'm sure they have Ozempic replacements in the works that will be released and you could try them."</li></ul>                                                                                                                                                                                                                                                       |
| 0     | <ul><li>'I was taking 4 metformin a day with morning blood sugar numbers around 200. I am currently on week 11 of taking Ozempic and only 1 metformin. My sugars are around 100 every morning. I finally feel good.'</li><li>"Yup, I'm on CRF as well and have probably gained about 50 lbs over time. It sucks. I'm currently taking a smaller dose of mirtazapine and am also on ozempic for weight loss."</li><li>"July 19 started new lifestyle. Down 30.6 lbs already - Interesting! I started off with Metaformin for a month, but wasn't happy with it, so my doc put me on Ozempic. I just started 0.25. Just for my own understanding, did you doc recommend taking both? If so, did he give any warnings about mixing the two?"</li></ul> |

## 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("bhaskars113/ozempic-taking-medications-classifier-1.1")
# Run inference
preds = model("New Ozempic and Wegovy side effects come to light - After I stopped taking it I developed Gallbladder disease and Pancreatitis")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 14  | 36.4333 | 94  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 15                    |
| 1     | 15                    |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0133 | 1    | 0.223         | -               |
| 0.6667 | 50   | 0.0031        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.0
- PyTorch: 2.2.1+cu121
- Datasets: 2.19.0
- Tokenizers: 0.19.1

## 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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