README.md

---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
datasets:
- konsman/setfit-messages-optimized
metrics:
- f1
- accuracy
widget:
- text: Tomato sauce is acidic and causes problems with my reflux.  That, in turn,
    irritates the vagus nerve and may bring on arrthymia.  I can't eat tomato soup
    anymore.  It could be a trigger for that reason.
- text: pednisone is synthetic cortisol hormone naturally produced by our adrenal
    glands , a powerful anti inflamatory , it works by reducing swelling . I recommend
    reading the book "adrenal fatigue - the 21st century health syndrome" , the doc
    says all allergies , frequent respiratory tract infections and asthma have an
    underlying  cause that is adrenal fatigue..
- text: 'You may want to read about Nigella sativa.  It is helpful for many conditions,
    and studies have been done showing it to be beneficial at reducing inflammation
    of ulcerative colitis.  It is also generally good for preventing many diseases,
    including cancer.  Also hemorrhoids.   '
- text: Sorry forgot to say that unfortunately after this problem that made me let
    sports and with the anxiety meds . I am now 83 kg
- text: 6 months pregnant had an abnormal pap, doctor did a biopsy and came back as
    cis what is this how serious and what's the cause? I have to have a leep after
    my son comes, what does this entail? Doc not good at explaining anything
pipeline_tag: text-classification
inference: false
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: konsman/setfit-messages-optimized
      type: konsman/setfit-messages-optimized
      split: test
    metrics:
    - type: f1
      value: 0.6896901980700864
      name: F1
    - type: accuracy
      value: 0.3403755868544601
      name: Accuracy
---

# SetFit with sentence-transformers/paraphrase-mpnet-base-v2

This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [konsman/setfit-messages-optimized](https://huggingface.co/datasets/konsman/setfit-messages-optimized) dataset 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 MultiOutputClassifier 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 MultiOutputClassifier instance
- **Maximum Sequence Length:** 512 tokens
<!-- - **Number of Classes:** Unknown -->
- **Training Dataset:** [konsman/setfit-messages-optimized](https://huggingface.co/datasets/konsman/setfit-messages-optimized)
<!-- - **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)

## Evaluation

### Metrics
| Label   | F1     | Accuracy |
|:--------|:-------|:---------|
| **all** | 0.6897 | 0.3404   |

## 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("konsman/setfit-messages-multilabel-example")
# Run inference
preds = model("Sorry forgot to say that unfortunately after this problem that made me let sports and with the anxiety meds . I am now 83 kg")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median   | Max |
|:-------------|:----|:---------|:----|
| Word count   | 5   | 110.2344 | 469 |

### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (2, 2)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 5
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0031 | 1    | 0.3209        | -               |
| 0.1562 | 50   | 0.1823        | -               |
| 0.3125 | 100  | 0.1003        | -               |
| 0.4688 | 150  | 0.1774        | -               |
| 0.625  | 200  | 0.0832        | -               |
| 0.7812 | 250  | 0.0828        | -               |
| 0.9375 | 300  | 0.0721        | -               |
| 1.0938 | 350  | 0.1331        | -               |
| 1.25   | 400  | 0.1215        | -               |
| 1.4062 | 450  | 0.1494        | -               |
| 1.5625 | 500  | 0.0444        | -               |
| 1.7188 | 550  | 0.0688        | -               |
| 1.875  | 600  | 0.1033        | -               |
| 0.0125 | 1    | 0.0508        | -               |
| 0.625  | 50   | 0.0793        | -               |
| 1.25   | 100  | 0.081         | -               |
| 1.875  | 150  | 0.1367        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.2
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.16.1
- Tokenizers: 0.15.0

## 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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