setfit safetensors mpnet sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:sentence-transformers/paraphrase-mpnet-base-v2 base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2 model-index region:us
---
base_model: sentence-transformers/paraphrase-mpnet-base-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: What competitive edge does IKEA have over other furniture stores nearby?
- text: Analyze the sites and provide recommendations.
- text: How does the proximity to schools and parks affect the Whole Foods market?
- text: How does this store compare to its competitors?
- text: What are the key competitive advantages of the local Best Buy compared to
    other electronics stores?
inference: true
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: 1.0
      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 [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:** 5 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                                                                                                                                                                                                                                                                                                                                                                 |
|:-----------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| store neighbourhood analysis | <ul><li>'How does the area compare in an analysis for this store?'</li><li>'How do the local beauty trends influence product demand at Sephora?'</li><li>'What is the socio-economic status of the neighborhood for this store?'</li></ul>                                                                                                                               |
| exploratory                  | <ul><li>'Which are the stores with the largest parking lots?'</li><li>'Which stores are located in commercial zones?'</li><li>'Which are the largest stores by square footage?'</li></ul>                                                                                                                                                                                |
| site recommendations         | <ul><li>'How would you analyze potential sites for recommendations?'</li><li>'Which neighborhoods offer potential for opening new Michaels craft stores to meet the demand for art supplies and creative outlets?'</li><li>'Can you provide an analysis to recommend sites?'</li></ul>                                                                                   |
| baseline compare             | <ul><li>"What is the variance in revenue generation between this Best Buy outlet and the brand's expected benchmarks?"</li><li>"What is the disparity in sales performance between this Home Depot store and the brand's standard expectations?"</li><li>'What are the variations in customer loyalty scores between this Target store and the brand average?'</li></ul> |
| store competition            | <ul><li>'How does the service quality at Safeway compare to other grocery stores in the neighborhood?'</li><li>"How does Target's product selection compare to that of neighboring retail stores?"</li><li>'What competitive strategies has Staples employed to stand out among other office supply retailers?'</li></ul>                                                |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 1.0      |

## 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("a-n-a-n-y-a-123/setfit-model-intent")
# Run inference
preds = model("Analyze the sites and provide recommendations.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 6   | 11.7467 | 20  |

| Label                        | Training Sample Count |
|:-----------------------------|:----------------------|
| baseline compare             | 15                    |
| exploratory                  | 15                    |
| site recommendations         | 15                    |
| store competition            | 15                    |
| store neighbourhood analysis | 15                    |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0053 | 1    | 0.2398        | -               |
| 0.2660 | 50   | 0.0757        | -               |
| 0.5319 | 100  | 0.0021        | -               |
| 0.7979 | 150  | 0.0013        | -               |
| 1.0638 | 200  | 0.0005        | -               |
| 1.3298 | 250  | 0.0006        | -               |
| 1.5957 | 300  | 0.0006        | -               |
| 1.8617 | 350  | 0.0004        | -               |
| 2.1277 | 400  | 0.0006        | -               |
| 2.3936 | 450  | 0.0004        | -               |
| 2.6596 | 500  | 0.0003        | -               |
| 2.9255 | 550  | 0.0003        | -               |
| 0.0053 | 1    | 0.0002        | -               |
| 0.2660 | 50   | 0.0003        | -               |
| 0.5319 | 100  | 0.0004        | -               |
| 0.7979 | 150  | 0.0001        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.0
- Transformers: 4.39.0
- PyTorch: 2.3.0+cu121
- Datasets: 2.19.2
- 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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