README.md

---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: She is Female, her heart rate is 63, she walks 4000 steps daily and is Underweight.
    She slept at 2 hrs. Yesterday, she slept from 1 hrs to 7 hrs, with a duration
    of 360 minutes and 5 interruptions. The day before yesterday, she slept from 23
    hrs to 7 hrs, with a duration of 420 minutes and 3 interruptions.
- text: She is Female, her heart rate is 70, she walks 8000 steps daily and is Normal.
    She slept at 22 hrs. Yesterday, she slept from 23 hrs to 7 hrs, with a duration
    of 400 minutes and 2 interruptions. The day before yesterday, she slept from 22
    hrs to 6 hrs, with a duration of 430 minutes and 2 interruptions.
- text: He is Male, his heart rate is 70, he walks 2400 steps daily, and is Underweight.
    He slept at 0 hrs. Yesterday, he slept from 2hrs to 7 hrs, with a duration of
    280 minutes and 4 interruptions. The day before yesterday, he slept from 2 hrs
    to 8 hrs, with a duration of 340 minutes and 4 interruptions.
- text: She is Female, her heart rate is 68, she walks 11,000 steps daily and is Normal.
    She slept at 1 hrs. Yesterday, she slept from 1 hrs to 9 hrs, with a duration
    of 495 minutes and 0 interruptions. The day before yesterday, she slept from 1
    hrs to 10 hrs, with a duration of 540 minutes and 1 interruptions.
- text: He is Male, his heart rate is 67, he walks 12000 steps daily, and is Normal.
    He slept at 3 hrs. Yesterday, he slept from 4hrs to 11 hrs, with a duration of
    420 minutes and 3 interruptions. The day before yesterday, he slept from 3 hrs
    to 5 hrs, with a duration of 150 minutes and 0 interruptions.
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: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.0
      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 OneVsRestClassifier 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 OneVsRestClassifier instance
- **Maximum Sequence Length:** 512 tokens
<!-- - **Number of Classes:** Unknown -->
<!-- - **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)

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.0      |

## 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("naushin/setfit-ethos-multilabel-example")
# Run inference
preds = model("He is Male, his heart rate is 67, he walks 12000 steps daily, and is Normal. He slept at 3 hrs. Yesterday, he slept from 4hrs to 11 hrs, with a duration of 420 minutes and 3 interruptions. The day before yesterday, he slept from 3 hrs to 5 hrs, with a duration of 150 minutes and 0 interruptions.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 59  | 59.5   | 60  |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0667 | 1    | 0.421         | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.6.1
- Transformers: 4.38.2
- PyTorch: 2.2.1+cu121
- Datasets: 2.18.0
- 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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