---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: sentence-transformers/paraphrase-mpnet-base-v2
metrics:
- accuracy
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?
pipeline_tag: text-classification
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
### 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 |
- 'How does the area compare in an analysis for this store?'
- 'How do the local beauty trends influence product demand at Sephora?'
- 'What is the socio-economic status of the neighborhood for this store?'
|
| exploratory | - 'Which are the stores with the largest parking lots?'
- 'Which stores are located in commercial zones?'
- 'Which are the largest stores by square footage?'
|
| site recommendations | - 'How would you analyze potential sites for recommendations?'
- 'Which neighborhoods offer potential for opening new Michaels craft stores to meet the demand for art supplies and creative outlets?'
- 'Can you provide an analysis to recommend sites?'
|
| baseline compare | - "What is the variance in revenue generation between this Best Buy outlet and the brand's expected benchmarks?"
- "What is the disparity in sales performance between this Home Depot store and the brand's standard expectations?"
- 'What are the variations in customer loyalty scores between this Target store and the brand average?'
|
| store competition | - 'How does the service quality at Safeway compare to other grocery stores in the neighborhood?'
- "How does Target's product selection compare to that of neighboring retail stores?"
- 'What competitive strategies has Staples employed to stand out among other office supply retailers?'
|
## 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.")
```
## 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}
}
```