---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: google-t5/t5-small
metrics:
- accuracy
widget:
- text: Do you have any special deals or discounts on bulk items?
- text: I'd like to exchange a product I bought in-store. Do I need to bring the original
    receipt?
- text: I have a question about freight shipping rates for a bulk order I'm considering
    placing
- text: I need to find some dairy-free milk alternatives. What options do you carry?
- text: I purchased a product that was supposed to be on sale but I didn't get the
    discounted price. Can I get a credit for the difference?
pipeline_tag: text-classification
inference: true
---

# SetFit with google-t5/t5-small

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) 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:** [google-t5/t5-small](https://huggingface.co/google-t5/t5-small)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** None 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                                                                                                                                                                                                                                                                                                                                                             |
|:-------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Tech Support | <ul><li>"My loyalty card isn't working at the checkout. What should I do?"</li><li>'How can I reset my password for the online account?'</li><li>'How can I reset my password for the online account?'</li></ul>                                                                                                                                                     |
| HR           | <ul><li>"I'm interested in applying for a job at your company. Can you provide information on current openings?"</li><li>'I have a question about my paycheck. Who should I contact?'</li><li>"I'm having an issue with my timesheet submission. Who should I contact?"</li></ul>                                                                                    |
| Product      | <ul><li>'What brand of nut butters do you carry that are peanut-free?'</li><li>'Do you offer any delivery or pickup options for online grocery orders?'</li><li>'I have a dietary restriction - how can I easily identify suitable products?'</li></ul>                                                                                                              |
| Returns      | <ul><li>'My grocery delivery contained items that were spoiled or past their expiration date. How do I get replacements?'</li><li>"I purchased a product that was supposed to be on sale but I didn't get the discounted price. Can I get a credit for the difference?"</li><li>"I bought an item that doesn't fit. What's the process for exchanging it?"</li></ul> |
| Logistics    | <ul><li>'My delivery was marked as "undeliverable" - what are the next steps I should take?'</li><li>'I need to change the delivery address for my upcoming order. How can I do that?'</li><li>'Is there a way to get real-time updates on the status of my order during the shipping process?'</li></ul>                                                            |

## 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("Do you have any special deals or discounts on bulk items?")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 10  | 14.25  | 26  |

| Label        | Training Sample Count |
|:-------------|:----------------------|
| Returns      | 8                     |
| Tech Support | 8                     |
| Logistics    | 8                     |
| HR           | 8                     |
| Product      | 8                     |

### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (100, 100)
- 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: False

### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-----:|:----:|:-------------:|:---------------:|
| 0.025 | 1    | 0.2674        | -               |
| 1.25  | 50   | 0.2345        | -               |
| 2.5   | 100  | 0.2558        | -               |
| 3.75  | 150  | 0.2126        | -               |
| 5.0   | 200  | 0.1904        | -               |
| 6.25  | 250  | 0.1965        | -               |
| 7.5   | 300  | 0.2013        | -               |
| 8.75  | 350  | 0.1221        | -               |
| 10.0  | 400  | 0.1254        | -               |
| 11.25 | 450  | 0.0791        | -               |
| 12.5  | 500  | 0.0917        | -               |
| 13.75 | 550  | 0.0757        | -               |
| 15.0  | 600  | 0.0446        | -               |
| 16.25 | 650  | 0.0407        | -               |
| 17.5  | 700  | 0.0276        | -               |
| 18.75 | 750  | 0.0297        | -               |
| 20.0  | 800  | 0.017         | -               |
| 21.25 | 850  | 0.0193        | -               |
| 22.5  | 900  | 0.0105        | -               |
| 23.75 | 950  | 0.0143        | -               |
| 25.0  | 1000 | 0.0133        | -               |
| 26.25 | 1050 | 0.0127        | -               |
| 27.5  | 1100 | 0.0064        | -               |
| 28.75 | 1150 | 0.0076        | -               |
| 30.0  | 1200 | 0.0099        | -               |
| 31.25 | 1250 | 0.0077        | -               |
| 32.5  | 1300 | 0.0059        | -               |
| 33.75 | 1350 | 0.0047        | -               |
| 35.0  | 1400 | 0.0059        | -               |
| 36.25 | 1450 | 0.005         | -               |
| 37.5  | 1500 | 0.005         | -               |
| 38.75 | 1550 | 0.005         | -               |
| 40.0  | 1600 | 0.0043        | -               |
| 41.25 | 1650 | 0.0056        | -               |
| 42.5  | 1700 | 0.0036        | -               |
| 43.75 | 1750 | 0.0029        | -               |
| 45.0  | 1800 | 0.0031        | -               |
| 46.25 | 1850 | 0.0033        | -               |
| 47.5  | 1900 | 0.0028        | -               |
| 48.75 | 1950 | 0.0042        | -               |
| 50.0  | 2000 | 0.0038        | -               |
| 51.25 | 2050 | 0.0032        | -               |
| 52.5  | 2100 | 0.0033        | -               |
| 53.75 | 2150 | 0.0031        | -               |
| 55.0  | 2200 | 0.0023        | -               |
| 56.25 | 2250 | 0.002         | -               |
| 57.5  | 2300 | 0.003         | -               |
| 58.75 | 2350 | 0.0039        | -               |
| 60.0  | 2400 | 0.003         | -               |
| 61.25 | 2450 | 0.0035        | -               |
| 62.5  | 2500 | 0.0022        | -               |
| 63.75 | 2550 | 0.0029        | -               |
| 65.0  | 2600 | 0.0029        | -               |
| 66.25 | 2650 | 0.0019        | -               |
| 67.5  | 2700 | 0.002         | -               |
| 68.75 | 2750 | 0.0041        | -               |
| 70.0  | 2800 | 0.0022        | -               |
| 71.25 | 2850 | 0.0027        | -               |
| 72.5  | 2900 | 0.0016        | -               |
| 73.75 | 2950 | 0.002         | -               |
| 75.0  | 3000 | 0.0029        | -               |
| 76.25 | 3050 | 0.0024        | -               |
| 77.5  | 3100 | 0.0017        | -               |
| 78.75 | 3150 | 0.0017        | -               |
| 80.0  | 3200 | 0.0025        | -               |
| 81.25 | 3250 | 0.0023        | -               |
| 82.5  | 3300 | 0.0018        | -               |
| 83.75 | 3350 | 0.0021        | -               |
| 85.0  | 3400 | 0.0016        | -               |
| 86.25 | 3450 | 0.0021        | -               |
| 87.5  | 3500 | 0.0018        | -               |
| 88.75 | 3550 | 0.0014        | -               |
| 90.0  | 3600 | 0.0014        | -               |
| 91.25 | 3650 | 0.0026        | -               |
| 92.5  | 3700 | 0.0012        | -               |
| 93.75 | 3750 | 0.0031        | -               |
| 95.0  | 3800 | 0.0025        | -               |
| 96.25 | 3850 | 0.0014        | -               |
| 97.5  | 3900 | 0.0012        | -               |
| 98.75 | 3950 | 0.0025        | -               |
| 100.0 | 4000 | 0.002         | -               |

### Framework Versions
- Python: 3.11.8
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.0
- PyTorch: 2.2.2
- Datasets: 2.19.0
- 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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