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t5-small-finetuned

Text Classification
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 ---
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
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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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