---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: You believe this old fossil?
- text: I was 100% fossil.
- text: Spandex blended with other fabrics like cotton or polyester can be ideal for
    exercise, because spandex is not only flexible but also durable.
- text: The flexible schedule and departure every other day ensures that passengers
    will be able to find the ideal time for their Bahamas vacation.
- text: Buying a new, greener refrigerator to replace an older model can be a great
    way to positively impact the environment.
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/all-mpnet-base-v2
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: 0.9242424242424242
      name: Accuracy
---

# 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 [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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 384 tokens
- **Number of Classes:** 2 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                                                                                                                                                                                                                                                                                                                                                                                                             |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0     | <ul><li>'A simple black spider is a fast face painting design that can make a big impact come Halloween.'</li><li>'A recently discovered fossil group, the Pteridospermae have characters intermediate between the Ptendophyta and the more primitive seedplants.'</li><li>'The Moral Balance model proposes that most humans operate out of a limited or flexible morality.'</li></ul>                              |
| 1     | <ul><li>'That fossil down the street?'</li><li>'Likewise, stores such as TJ Maxx, Ross and other discount clothing outlets often have Ralph Lauren clothing on sale, although you may have to be a bit more flexible about the color.'</li><li>'Giving some guidelines for the style, such as asking each attendant to wear matching hair pins, is fine, but being flexible will keep attendants smiling.'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9242   |

## 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("d31fs0/context-aware-language-classifier")
# Run inference
preds = model("I was 100% fossil.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 4   | 17.8011 | 46  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 124                   |
| 1     | 62                    |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0008 | 1    | 0.7262        | -               |
| 0.0412 | 50   | 0.3557        | -               |
| 0.0824 | 100  | 0.1985        | -               |
| 0.1237 | 150  | 0.0489        | -               |
| 0.1649 | 200  | 0.0019        | -               |
| 0.2061 | 250  | 0.0006        | -               |
| 0.2473 | 300  | 0.0004        | -               |
| 0.2885 | 350  | 0.0003        | -               |
| 0.3298 | 400  | 0.0002        | -               |
| 0.3710 | 450  | 0.0002        | -               |
| 0.4122 | 500  | 0.0002        | -               |
| 0.4534 | 550  | 0.0001        | -               |
| 0.4946 | 600  | 0.0001        | -               |
| 0.5359 | 650  | 0.0001        | -               |
| 0.5771 | 700  | 0.0001        | -               |
| 0.6183 | 750  | 0.0001        | -               |
| 0.6595 | 800  | 0.0001        | -               |
| 0.7007 | 850  | 0.0001        | -               |
| 0.7420 | 900  | 0.0001        | -               |
| 0.7832 | 950  | 0.0001        | -               |
| 0.8244 | 1000 | 0.0001        | -               |
| 0.8656 | 1050 | 0.0001        | -               |
| 0.9068 | 1100 | 0.0001        | -               |
| 0.9481 | 1150 | 0.0001        | -               |
| 0.9893 | 1200 | 0.0001        | -               |
| 1.0    | 1213 | -             | 0.1145          |

### Framework Versions
- Python: 3.12.12
- SetFit: 1.1.3
- Sentence Transformers: 5.2.0
- Transformers: 4.57.6
- PyTorch: 2.9.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.22.2

## 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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