---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Could you step back a bit?
- text: Move to the right
- text: Go ahead in this direction a little
- text: Adjust your position slightly to the right
- text: Proceed forward along this path
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/paraphrase-mpnet-base-v2
---

# 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:** 4 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                                                                                                                                   |
|:---------|:-------------------------------------------------------------------------------------------------------------------------------------------|
| forward  | <ul><li>'Proceed along this path ahead'</li><li>'Proceed carefully in that direction'</li><li>'Proceed forward a little'</li></ul>         |
| right    | <ul><li>'Move toward the right'</li><li>'Adjust your position to the right'</li><li>'Adjust your position slightly to the right'</li></ul> |
| left     | <ul><li>'Head towards the left side'</li><li>'Move towards your left'</li><li>'Shift your way to the left'</li></ul>                       |
| backward | <ul><li>'Could you step back slightly?'</li><li>'Move backward, please'</li><li>'Could you go back the other way?'</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("cahlen/setfit-navigation-instructions")
# Run inference
preds = model("Move to the right")
```

<!--
### Downstream Use

*List how someone could finetune this model on their own dataset.*
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 2   | 5.0    | 12  |

| Label    | Training Sample Count |
|:---------|:----------------------|
| right    | 22                    |
| left     | 21                    |
| forward  | 11                    |
| backward | 13                    |

### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (4, 4)
- 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0024 | 1    | 0.1239        | -               |
| 0.1220 | 50   | 0.1257        | -               |
| 0.2439 | 100  | 0.0215        | -               |
| 0.3659 | 150  | 0.0047        | -               |
| 0.4878 | 200  | 0.0025        | -               |
| 0.6098 | 250  | 0.0017        | -               |
| 0.7317 | 300  | 0.0014        | -               |
| 0.8537 | 350  | 0.0011        | -               |
| 0.9756 | 400  | 0.0013        | -               |
| 1.0    | 410  | -             | 0.0182          |
| 1.0976 | 450  | 0.0009        | -               |
| 1.2195 | 500  | 0.0008        | -               |
| 1.3415 | 550  | 0.0007        | -               |
| 1.4634 | 600  | 0.0007        | -               |
| 1.5854 | 650  | 0.0006        | -               |
| 1.7073 | 700  | 0.0007        | -               |
| 1.8293 | 750  | 0.0006        | -               |
| 1.9512 | 800  | 0.0006        | -               |
| 2.0    | 820  | -             | 0.0227          |
| 2.0732 | 850  | 0.0005        | -               |
| 2.1951 | 900  | 0.0005        | -               |
| 2.3171 | 950  | 0.0006        | -               |
| 2.4390 | 1000 | 0.0005        | -               |
| 2.5610 | 1050 | 0.0006        | -               |
| 2.6829 | 1100 | 0.0005        | -               |
| 2.8049 | 1150 | 0.0005        | -               |
| 2.9268 | 1200 | 0.0004        | -               |
| 3.0    | 1230 | -             | 0.0236          |
| 3.0488 | 1250 | 0.0004        | -               |
| 3.1707 | 1300 | 0.0004        | -               |
| 3.2927 | 1350 | 0.0004        | -               |
| 3.4146 | 1400 | 0.0005        | -               |
| 3.5366 | 1450 | 0.0004        | -               |
| 3.6585 | 1500 | 0.0004        | -               |
| 3.7805 | 1550 | 0.0004        | -               |
| 3.9024 | 1600 | 0.0004        | -               |
| 4.0    | 1640 | -             | 0.0240          |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.2.1
- Transformers: 4.44.2
- PyTorch: 2.8.0.dev20250331+cu128
- Datasets: 3.5.0
- 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->