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---
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 makeup products do you have for eyes?
- text: How can I prevent acne if I have oily skin?
- text: What is the estimated delivery time for orders within the same country?
- text: Can you recommend a good moisturizer for winter skin care?
- text: Is the Beachy-Floral-Citrus Mini Eau De Parfum Gift Set suitable for all skin
types?
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: 0.9583333333333334
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) -->
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### 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 |
|:------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| product discoverability | <ul><li>'Can you show me all the products for oily skin?'</li><li>'Do you have any makeup remover?'</li><li>'Can you show me all the products for dark spots?'</li></ul> |
| order tracking | <ul><li>'What is the estimated delivery time for orders within the same state?'</li><li>'I need to know the status of my recent order. Can you check if it has been dispatched?'</li><li>'I ordered the Cake Decorating Kit 4 days ago, can you provide the tracking information?'</li></ul> |
| product faq | <ul><li>'What are the different shades available in the Color Affair Nail Polish Pixie Dust Collection?'</li><li>'Is the Touch-N-Go Lip & Cheek Tint a vegan and cruelty-free product?'</li><li>'Is this product suitable for oily skin?'</li></ul> |
| general faq | <ul><li>'How often should I use exfoliants to reduce open pores?'</li><li>'What are the most effective ingredients for treating acne?'</li><li>'Are home remedies effective for severe acne?'</li></ul> |
| product policy | <ul><li>'Are your products suitable for sensitive skin?'</li><li>'How can I track my order on the Plum Goodness app?'</li><li>'What is the contact number for customer support?'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9583 |
## 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("What makeup products do you have for eyes?")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 4 | 11.0 | 24 |
| Label | Training Sample Count |
|:------------------------|:----------------------|
| general faq | 20 |
| order tracking | 24 |
| product discoverability | 16 |
| product faq | 24 |
| product policy | 12 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0022 | 1 | 0.0832 | - |
| 0.1101 | 50 | 0.0046 | - |
| 0.2203 | 100 | 0.0002 | - |
| 0.3304 | 150 | 0.0029 | - |
| 0.4405 | 200 | 0.0001 | - |
| 0.5507 | 250 | 0.0005 | - |
| 0.6608 | 300 | 0.0001 | - |
| 0.7709 | 350 | 0.0001 | - |
| 0.8811 | 400 | 0.0001 | - |
| 0.9912 | 450 | 0.0001 | - |
| 1.1013 | 500 | 0.0001 | - |
| 1.2115 | 550 | 0.0001 | - |
| 1.3216 | 600 | 0.0001 | - |
| 1.4317 | 650 | 0.0001 | - |
| 1.5419 | 700 | 0.0002 | - |
| 1.6520 | 750 | 0.0001 | - |
| 1.7621 | 800 | 0.0001 | - |
| 1.8722 | 850 | 0.0001 | - |
| 1.9824 | 900 | 0.0001 | - |
### Framework Versions
- Python: 3.9.19
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.2
- PyTorch: 2.2.2
- Datasets: 2.19.1
- 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}
}
```
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