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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: cookie boxes for gifting under $20
- text: Are there any restrictions on returning candle supplies?
- text: special features for bakery boxes
- text: I need to confirm the shipping date for my recent purchase. Can you help me
with that?
- text: different types of bakery boxes available
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: 0.8380952380952381
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:** 4 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** 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>'Do you have Adidas Superstar shoes?'</li><li>'Do you have any running shoes in pink color?'</li><li>'Do you have black Yeezy sneakers in size 9?'</li></ul> |
| order tracking | <ul><li>"I'm concerned about the delay in the delivery of my order. Can you please provide me with the status?"</li><li>'What is the estimated delivery time for orders within the same city?'</li><li>"I placed an order last week and it still hasn't arrived. Can you check the status for me?"</li></ul> |
| product policy | <ul><li>'Are there any exceptions to the return policy for items that were purchased with a student discount?'</li><li>'Do you offer a try-and-buy option for sneakers?'</li><li>'Do you offer a price adjustment for sneakers if the price drops after purchase?'</li></ul> |
| product faq | <ul><li>'Do you have any limited edition sneakers available?'</li><li>'Are the Adidas Yeezy Foam Runner available in size 7?'</li><li>"Are the Nike Air Force 1 sneakers available in women's sizes?"</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.8381 |
## 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("special features for bakery boxes")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 3 | 11.6415 | 24 |
| Label | Training Sample Count |
|:------------------------|:----------------------|
| order tracking | 30 |
| product discoverability | 30 |
| product faq | 16 |
| product policy | 30 |
### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0019 | 1 | 0.1782 | - |
| 0.0965 | 50 | 0.0628 | - |
| 0.1931 | 100 | 0.0036 | - |
| 0.2896 | 150 | 0.0013 | - |
| 0.3861 | 200 | 0.0012 | - |
| 0.4826 | 250 | 0.0003 | - |
| 0.5792 | 300 | 0.0002 | - |
| 0.6757 | 350 | 0.0003 | - |
| 0.7722 | 400 | 0.0002 | - |
| 0.8687 | 450 | 0.0005 | - |
| 0.9653 | 500 | 0.0003 | - |
| 1.0618 | 550 | 0.0001 | - |
| 1.1583 | 600 | 0.0002 | - |
| 1.2548 | 650 | 0.0002 | - |
| 1.3514 | 700 | 0.0002 | - |
| 1.4479 | 750 | 0.0001 | - |
| 1.5444 | 800 | 0.0001 | - |
| 1.6409 | 850 | 0.0001 | - |
| 1.7375 | 900 | 0.0002 | - |
| 1.8340 | 950 | 0.0001 | - |
| 1.9305 | 1000 | 0.0001 | - |
### Framework Versions
- Python: 3.9.16
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.2
- PyTorch: 2.3.0
- 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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