README.md

---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
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metrics:
- f1_micro
- f1_macro
- f1_weighted
- precision
- accuracy
- recall
pipeline_tag: text-classification
library_name: setfit
inference: false
model-index:
- name: SetFit
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: f1_micro
      value: 0.9322709163346613
      name: F1_Micro
    - type: f1_macro
      value: 0.37012987012987014
      name: F1_Macro
    - type: f1_weighted
      value: 0.8821255080797066
      name: F1_Weighted
    - type: precision
      value: 0.9750000238418579
      name: Precision
    - type: accuracy
      value: 0.9468749761581421
      name: Accuracy
    - type: recall
      value: 0.8931297659873962
      name: Recall
---

# SetFit

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A OneVsRestClassifier 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:** [Unknown](https://huggingface.co/unknown) -->
- **Classification head:** a OneVsRestClassifier instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 8 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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)

## Evaluation

### Metrics
| Label   | F1_Micro | F1_Macro | F1_Weighted | Precision | Accuracy | Recall |
|:--------|:---------|:---------|:------------|:----------|:---------|:-------|
| **all** | 0.9323   | 0.3701   | 0.8821      | 0.9750    | 0.9469   | 0.8931 |

## 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("hasAdditionalInformation: AO, hasCreatedDate: 2024-10-08, hasCustomerHomeCountry: United States, hasCustomerID: 30642, hasCustomerName: Station Casinos LLC(Station Casinos), hasCutting: Trim to size, hasElementID: 3555960, hasElementTitle: 211696 - 381\" X 363\" SS FRONTLIT EXTERIOR VINYL SIGN , hasFinishedSizeHeight: 363, hasFinishedSizeWidth: 381, hasFscPaperBeenSpecified: No, hasInternalID: 04b8890a-dc33-4778-ad73-c1f68f68231c, hasMaterialCategory: Plastic, hasMaterialDescription: 13OZ VINYL, hasMaterialRecycledPercentage: 0%, hasMaterialThicknessOrWeight: 13, hasMaterialType: PVC, hasMaterialUnitOfMeasure: Ounces (oz), hasNumberOfVersions: 1, hasPrice: 676.0, hasPrintedSides: Not printed, hasProductCategory: Banners (synthetic), hasProofType: PDF digital proof, hasQuantity: 1, hasRecycledContentBeenOffered: N/A, hasSupplierName: WestRock Company(Westrock - 14360 - HHGSP), hasTotalColours: 4, hasUnitOfMeasure: Inches (in), ")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median   | Max |
|:-------------|:----|:---------|:----|
| Word count   | 67  | 110.9875 | 238 |

### Framework Versions
- Python: 3.10.16
- SetFit: 1.1.2
- Sentence Transformers: 3.4.1
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Datasets: 3.2.0
- 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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