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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: I recently ordered the Bella Silver Pendant, but I haven't received any update
about the shipment. Can you provide me with the current status of my order?
- text: What is the metal purity of the Eternal Swirl Rose Gold Hoop Earring, and
does it come with a certificate of authenticity?
- text: Can you suggest some minimalist necklaces from your 'Best Sellers - Minimalist'
range?
- text: I recently ordered the Pearly Round Earring but haven't received any shipping
updates. Can you please provide me with the tracking information?
- text: what are the colors available in air jordan 4
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: 0.8762886597938144
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:** 6 classes
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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 policy | <ul><li>'Are there any exceptions to the return policy for items that were purchased with a special offer promotion?'</li><li>'What is your policy on returning sneakers with added paint or dye?'</li><li>'Do you offer exchanges for items that were purchased with a special event celebration?'</li></ul> |
| order tracking | <ul><li>"I recently placed an order for the Regalia Gold Ring but I haven't received any confirmation or tracking details. Could you please update me on the status of my order?"</li><li>'What is the process for rerouting a shipment to a different address?'</li><li>"I recently ordered a Three Crystal Proposal Ring but haven't received any shipping updates yet. Could you please provide me with the current status of my order?"</li></ul> |
| complaints | <ul><li>"I recently bought the Golden Love Affair Pendant, but it seems to have tarnished very quickly. I'm not satisfied with the quality. What can you do about this?"</li><li>"I recently purchased the Three Crystal Proposal Ring, but I'm disappointed to find that one of the crystals is loose. Can you assist me with this issue?"</li><li>'I received my Kali- Handcrafted Earring today, but I found that one earring is slightly different from the other in design. Can you help me with this issue?'</li></ul> |
| product faq | <ul><li>'What is the material used for making the All the Stars Pendant Set, and does it come with matching earrings?'</li><li>'What is the Bold and Beautiful Link Ring made of, and could you provide information on sizing and care instructions?'</li><li>'What is the material used for making the Sheer Heart Ring, and is it available in different sizes?'</li></ul> |
| product discoveribility | <ul><li>"I'm interested in necklaces that have an adjustable length. What options do you have?"</li><li>'Do you have any charm bracelets available at your store?'</li><li>'Could you suggest some pendants that would go well with traditional attire?'</li></ul> |
| product discoverability | <ul><li>'Types of bakery boxes available'</li><li>'adidas sneakers under 25k'</li><li>'show me 100 cookie boxes under $50'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.8763 |
## 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 are the colors available in air jordan 4")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 4 | 16.2235 | 36 |
| Label | Training Sample Count |
|:------------------------|:----------------------|
| complaints | 30 |
| order tracking | 30 |
| product discoverability | 30 |
| product discoveribility | 30 |
| product faq | 20 |
| product policy | 30 |
### 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0007 | 1 | 0.1501 | - |
| 0.0333 | 50 | 0.1076 | - |
| 0.0667 | 100 | 0.01 | - |
| 0.1 | 150 | 0.0023 | - |
| 0.1333 | 200 | 0.0008 | - |
| 0.1667 | 250 | 0.0007 | - |
| 0.2 | 300 | 0.0005 | - |
| 0.2333 | 350 | 0.0005 | - |
| 0.2667 | 400 | 0.0003 | - |
| 0.3 | 450 | 0.0005 | - |
| 0.3333 | 500 | 0.0003 | - |
| 0.3667 | 550 | 0.0003 | - |
| 0.4 | 600 | 0.0002 | - |
| 0.4333 | 650 | 0.0002 | - |
| 0.4667 | 700 | 0.0003 | - |
| 0.5 | 750 | 0.0002 | - |
| 0.5333 | 800 | 0.0002 | - |
| 0.5667 | 850 | 0.0002 | - |
| 0.6 | 900 | 0.0002 | - |
| 0.6333 | 950 | 0.0002 | - |
| 0.6667 | 1000 | 0.0001 | - |
| 0.7 | 1050 | 0.0001 | - |
| 0.7333 | 1100 | 0.0002 | - |
| 0.7667 | 1150 | 0.0001 | - |
| 0.8 | 1200 | 0.0001 | - |
| 0.8333 | 1250 | 0.0001 | - |
| 0.8667 | 1300 | 0.0002 | - |
| 0.9 | 1350 | 0.0001 | - |
| 0.9333 | 1400 | 0.0002 | - |
| 0.9667 | 1450 | 0.0001 | - |
| 1.0 | 1500 | 0.0002 | - |
### 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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