---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: sentence-transformers/all-mpnet-base-v2
metrics:
- accuracy
- precision
- recall
- f1
widget:
- text: I appreciate the smooth operation of our public transit system every day.
- text: Precision shines through in your flawless execution of the project.
- text: Your assistance with the move was invaluable; I couldn’t have managed without
    you.
- text: Well done on orchestrating such a seamless event!
- text: The presentation Anne gave was remarkably insightful and comprehensive.
pipeline_tag: text-classification
inference: false
model-index:
- name: SetFit with sentence-transformers/all-mpnet-base-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.5
      name: Accuracy
    - type: precision
      value: 0.8
      name: Precision
    - type: recall
      value: 0.8888888888888888
      name: Recall
    - type: f1
      value: 0.8421052631578947
      name: F1
---

# SetFit with sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
- **Classification head:** a MultiOutputClassifier instance
- **Maximum Sequence Length:** 384 tokens
- **Number of Classes:** 3 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)

## Evaluation

### Metrics
| Label   | Accuracy | Precision | Recall | F1     |
|:--------|:---------|:----------|:-------|:-------|
| **all** | 0.5      | 0.8       | 0.8889 | 0.8421 |

## 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("Well done on orchestrating such a seamless event!")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 6   | 10.75  | 16  |

### Training Hyperparameters
- batch_size: (32, 2)
- num_epochs: (10, 10)
- 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True

### Training Results
| Epoch   | Step   | Training Loss | Validation Loss |
|:-------:|:------:|:-------------:|:---------------:|
| 0.0769  | 1      | 0.3115        | -               |
| 1.0     | 13     | -             | 0.1928          |
| 2.0     | 26     | -             | 0.1831          |
| 3.0     | 39     | -             | 0.1724          |
| 3.8462  | 50     | 0.08          | -               |
| **4.0** | **52** | **-**         | **0.1614**      |
| 5.0     | 65     | -             | 0.1695          |
| 6.0     | 78     | -             | 0.1837          |
| 7.0     | 91     | -             | 0.1904          |
| 7.6923  | 100    | 0.0364        | -               |
| 8.0     | 104    | -             | 0.1997          |
| 9.0     | 117    | -             | 0.1994          |
| 10.0    | 130    | -             | 0.1967          |

* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.12.1
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.37.2
- PyTorch: 2.2.0
- Datasets: 2.19.1
- Tokenizers: 0.15.1

## 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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