---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Is a residential portion of a building that sells alcohol considered "licensed
    premises" in indiana
- text: In Michigan, what criteria do courts consider in granting grandparent visitation
    rights?
- text: Ohio aggravated arson cases
- text: In Texas, what protections exist for whistleblowers?
- text: What did you have for breakfast?
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: nomic-ai/nomic-embed-text-v1.5
---

# SetFit with nomic-ai/nomic-embed-text-v1.5

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) 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:** [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 8192 tokens
- **Number of Classes:** 7 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                                                                                                                                                                                                                                                                                                                                                                                                                                   |
|:------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Term of Art Interpretations & Application | <ul><li>'How do courts in Illinois define "constructive eviction"?'</li><li>'How do Pennsylvania courts define "reasonable suspicion" in DUI cases?'</li><li>'definition of ex parte'</li></ul>                                                                                                                                                                                                                                            |
| Out of Scope                              | <ul><li>'Has Capt. Ashley Heiberger ever testified as an expert witness?'</li><li>'Have you recently attended any weddings or special celebrations?'</li><li>'Have you seen any good movies lately?'</li></ul>                                                                                                                                                                                                                             |
| SDR                                       | <ul><li>'Gonzalez et al. v. Mexico'</li><li>'2021 U.S. Dist. LEXIS 14890'</li><li>'Elizabeth Holmes Theranos ORDER DENYING MOTION FOR RELEASE PENDING APPEAL'</li></ul>                                                                                                                                                                                                                                                                    |
| Identify Current Law                      | <ul><li>'Does Michigan have a statute of repose?'</li><li>'Mississippi law concerning challenges to changes made in updated HOA regulations'</li><li>'cases on nurse liability for making medication dosage mistake in kentucky'</li></ul>                                                                                                                                                                                                 |
| Agent decision                            | <ul><li>'Search for  USPTO Patent Decisions: BPAI and PTAB discussing  the integration of a judicial exception into practical applications'</li><li>'Are there any EPA Environmental Appeals Board Decisions regarding the guidelines for establishing a "critical habitat" for wildlife?'</li><li>'Find Merit Systems Protection Board decisions regarding when the plain language of a statute must be treated as controlling'</li></ul> |
| Q&A - Complex                             | <ul><li>'Are bloodhounds considered reliable for establishing probable cause in Idaho?'</li><li>'What are the requirements to file a class action lawsuit in Florida?'</li><li>'Can a corporation be held liable for damages caused by an employee driving under the influence of alcohol in New York?'</li></ul>                                                                                                                          |
| Practical Guidance                        | <ul><li>'What does an "Election of Remedy" clause involve in an indemnity agreement? T'</li><li>'Where is Private Company Corporate Governance Board Resolutions Resource Kit T'</li><li>'If I start a law firm in Michigan, what types of employee leave do I need to provide compared to my current firm in Ohio? T'</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("tonyshaw/setfit_pg_70h_nomic-v1.5")
# Run inference
preds = model("Ohio aggravated arson cases")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 1   | 11.2193 | 98  |

| Label                                     | Training Sample Count |
|:------------------------------------------|:----------------------|
| Agent decision                            | 130                   |
| Identify Current Law                      | 500                   |
| Out of Scope                              | 100                   |
| Practical Guidance                        | 41                    |
| Q&A - Complex                             | 500                   |
| SDR                                       | 500                   |
| Term of Art Interpretations & Application | 500                   |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 10
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0004 | 1    | 0.2703        | -               |
| 0.0176 | 50   | 0.2289        | -               |
| 0.0352 | 100  | 0.2032        | -               |
| 0.0528 | 150  | 0.0951        | -               |
| 0.0704 | 200  | 0.0434        | -               |
| 0.0881 | 250  | 0.026         | -               |
| 0.1057 | 300  | 0.0299        | -               |
| 0.1233 | 350  | 0.02          | -               |
| 0.1409 | 400  | 0.0136        | -               |
| 0.1585 | 450  | 0.013         | -               |
| 0.1761 | 500  | 0.0147        | -               |
| 0.1937 | 550  | 0.0144        | -               |
| 0.2113 | 600  | 0.0052        | -               |
| 0.2290 | 650  | 0.0067        | -               |
| 0.2466 | 700  | 0.0021        | -               |
| 0.2642 | 750  | 0.0038        | -               |
| 0.2818 | 800  | 0.006         | -               |
| 0.2994 | 850  | 0.0039        | -               |
| 0.3170 | 900  | 0.0007        | -               |
| 0.3346 | 950  | 0.0003        | -               |
| 0.3522 | 1000 | 0.0002        | -               |
| 0.3698 | 1050 | 0.0026        | -               |
| 0.3875 | 1100 | 0.0027        | -               |
| 0.4051 | 1150 | 0.0003        | -               |
| 0.4227 | 1200 | 0.0012        | -               |
| 0.4403 | 1250 | 0.0022        | -               |
| 0.4579 | 1300 | 0.0027        | -               |
| 0.4755 | 1350 | 0.0014        | -               |
| 0.4931 | 1400 | 0.0008        | -               |
| 0.5107 | 1450 | 0.0001        | -               |
| 0.5284 | 1500 | 0.0013        | -               |
| 0.5460 | 1550 | 0.0001        | -               |
| 0.5636 | 1600 | 0.0011        | -               |
| 0.5812 | 1650 | 0.0           | -               |
| 0.5988 | 1700 | 0.001         | -               |
| 0.6164 | 1750 | 0.0001        | -               |
| 0.6340 | 1800 | 0.0002        | -               |
| 0.6516 | 1850 | 0.0           | -               |
| 0.6692 | 1900 | 0.0           | -               |
| 0.6869 | 1950 | 0.0           | -               |
| 0.7045 | 2000 | 0.0           | -               |
| 0.7221 | 2050 | 0.0           | -               |
| 0.7397 | 2100 | 0.0           | -               |
| 0.7573 | 2150 | 0.0           | -               |
| 0.7749 | 2200 | 0.0           | -               |
| 0.7925 | 2250 | 0.001         | -               |
| 0.8101 | 2300 | 0.0           | -               |
| 0.8278 | 2350 | 0.0           | -               |
| 0.8454 | 2400 | 0.0013        | -               |
| 0.8630 | 2450 | 0.0           | -               |
| 0.8806 | 2500 | 0.0001        | -               |
| 0.8982 | 2550 | 0.0004        | -               |
| 0.9158 | 2600 | 0.0           | -               |
| 0.9334 | 2650 | 0.0001        | -               |
| 0.9510 | 2700 | 0.0           | -               |
| 0.9687 | 2750 | 0.0           | -               |
| 0.9863 | 2800 | 0.0           | -               |

### Framework Versions
- Python: 3.11.11
- SetFit: 1.1.1
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.6.0+cu124
- Datasets: 3.4.1
- Tokenizers: 0.21.1

## 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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