---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
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pipeline_tag: text-classification
inference: true
base_model: BAAI/bge-small-en-v1.5
model-index:
- name: SetFit with BAAI/bge-small-en-v1.5
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 1.0
      name: Accuracy
---

# SetFit with BAAI/bge-small-en-v1.5

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
- **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:** 4 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)

### Model Labels
| Label | Examples                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       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| 5     | <ul><li>'<s_cord-v2><s_menu><s_nm> Original for Buyer/ Duplicate for Temponte/ Teplicate for Assesce<s_nm> OMCL</s_nm><s_num> 93103986</s_num><s_unitprice> 76000</s_unitprice><s_cnt> 1</s_cnt><s_discountprice> SUSHIBILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILI'</li><li>'<s_cord-v2><s_menu><s_nm> Original Box buyer/ Duplicate for Temponte/ Teisplicate Box Assesce<s_nm> MCL</s_nm><s_num> 9310301</s_num><s_unitprice> @099</s_unitprice><s_cnt> 1</s_cnt><s_discountprice> SUSHIBILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILI'</li><li>'<s_cord-v2><s_menu><s_nm> Original Box Buyer/ Duplicate for Taquequeque/ Tcquil<sep/><s_nm> OMCL</s_nm><s_unitprice> @MCL</s_nm><s_unitprice> @MCL</s_unitprice><s_cnt> 1</s_cnt><s_price> @020</s_price><sep/><s_nm> SUPPOLIZE Vitter. Musc</s_nm><s_unitprice> @MACHIZED WHITED</s_nm><s_unitprice> @MICEDICED WHITED Claysucci Cryccucee</s_nm><s_unitprice> @MADEMI SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZED SUSHIZEDMIZED SUSHIZEDMIZED SUSHIZEDMIZED SUSHIZEDMIZED SUSHIZEDMIZED SUSHIZEDMIZED SUSHIZEDMIZED SUSHIZEDMIZED SUSHIZEDMIZED SUSHIZEDMIZED SUSHIZEDENTERTAINCE SUSHIZEDICEDGE CLIZEDGE SUSHIZEDGEECECECECECECECE SUSHIZEDMIZEDENTERTAINCE SUSHIZEDICEDICEDGEECECECECECE SUSHIZEDENTERTAINCEE Cry SUSHIZEDGEECECECECECECECECE SUSHIZEDSUMIZEDENTERTAINCE SUSHIZEGGYECECECECECECECECEE SUSHIZEDSUMIZED SUSHIZEGGYECECECECECECECECECEECECE SUSHIZEDSUMIZED SUSHIZEDSUMIZED SUSHIZEGGYECECECECECECECECECEECECE SUSHIZEDSUMIZED SUSHIZEDSUKECECECECECECEECE SUSHIZEDSUKECECECECECEECECE SUSHIZEDSUKECECECECECEECECE SUSHIZEDSUKECECECECEECECECECEECECECECECECECEECECECECECECECECECEECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECECE'</li></ul> |
| 6     | <ul><li>'<s_cord-v2><s_menu><s_nm> GARDANG CHOCO POWE IHADOUS</s_nm><sep/><s_nm> GENERAL TERM & CHOCOLIONSE</s_nm><sep/><s_nm> LIGHTEGAL TERS A CHOCOLIONSE</s_nm><sep/><s_nm> LIGHTICE</s_nm></s_sub><sep/><s_nm> ICE LIGHTING CHOCOLIZEDAYOSE</s_nm><s_cnt> 3</s_cnt><s_price> SUSHIKA LIGHTONE TERS & CHOCOLIZEDAYOSE</s_nm></s_sub><sep/><s_nm> SUSHIKA LIGHTONE TERS & CHOCOLIZEDAYOSE</s_nm><s_cnt> 3</s_cnt><s_price> SUSHIKA LIGHTONE TERS & CHOCOLIZEDAYOSE</s_nm><s_price> SUSHIKA LIGHTONE TERS & CHOCOLIZEDAYOSE</s_nm><s_price> SUSHIKA LIGHTONE TERS & CHOCOLIZEDAYOSE</s_nm><s_price> SUSHIKA LIGHTONE TERS & CHOCOLIZEDAYOSE</s_nm><s_price> SUSHIKAIDEE'</li><li>"<s_cord-v2><s_menu><s_nm> SUSHI SALMONG</s_nm><s_cnt> 1</s_cnt><s_price> SUSHI SALMONG</s_nm><s_cnt> 1</s_cnt><s_price> SUSHI SALMONG</s_nm></s_sub></s_menu><s_sub_total><s_subtotal_price> SUSHI SALMONG</s_subtotal_price><s_tax_price> 11+727</s_tax_price></s_sub_total><s_total><s_total_price> Dolla's</s_total_price></s_total>"</li><li>'<s_cord-v2><s_menu><s_nm> WESEC</s_nm><s_price> 63644,836</s_price></s_menu><s_sub_total><s_subtotal_price> 636</s_subtotal_price></s_sub_total><s_total><s_total_price> 636</s_total_price><s_cashprice> 636-modern</s_cashprice></s_total>'</li></ul>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               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| 4     | <ul><li>'<s_cord-v2><s_menu><s_nm> MATERIAL REPORT</s_nm><s_unitprice> 06/28世纪 -85-18 -To</s_nm><s_unitprice> 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| 3     | <ul><li>'<s_cord-v2><s_menu><s_nm> HNDALCO INDUSTRIES LIMITED</s_nm><s_unitprice> HIRAKUD</s_nm></s_sub><sep/><s_nm> PAYMENT ORDER</s_nm><s_cnt> 1</s_cnt><s_price> 395-48</s_price><sep/><s_nm> Pauto Bresanta Kumar Mohany</s_nm><s_unitprice> 636</s_unitprice><s_cnt> 1</s_cnt><s_price> 636</s_price><sep/><s_nm> Emp. 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 1.0      |

## 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("Gopal2002/setfit_zeon_3456")
# Run inference
preds = model("<s_cord-v2><s_menu><s_nm> RT @kanaka<s_nm> RT @kanaka</s_total>")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median   | Max  |
|:-------------|:----|:---------|:-----|
| Word count   | 2   | 193.4045 | 1530 |

| Label | Training Sample Count |
|:------|:----------------------|
| 3     | 38                    |
| 4     | 45                    |
| 5     | 54                    |
| 6     | 41                    |

### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (2, 2)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0014 | 1    | 0.2908        | -               |
| 0.0677 | 50   | 0.1735        | -               |
| 0.1353 | 100  | 0.0824        | -               |
| 0.2030 | 150  | 0.0505        | -               |
| 0.2706 | 200  | 0.014         | -               |
| 0.3383 | 250  | 0.0066        | -               |
| 0.4060 | 300  | 0.0035        | -               |
| 0.4736 | 350  | 0.0018        | -               |
| 0.5413 | 400  | 0.0022        | -               |
| 0.6089 | 450  | 0.0019        | -               |
| 0.6766 | 500  | 0.0015        | -               |
| 0.7442 | 550  | 0.0014        | -               |
| 0.8119 | 600  | 0.0012        | -               |
| 0.8796 | 650  | 0.0013        | -               |
| 0.9472 | 700  | 0.0014        | -               |
| 1.0149 | 750  | 0.0012        | -               |
| 1.0825 | 800  | 0.001         | -               |
| 1.1502 | 850  | 0.0012        | -               |
| 1.2179 | 900  | 0.001         | -               |
| 1.2855 | 950  | 0.0009        | -               |
| 1.3532 | 1000 | 0.0009        | -               |
| 1.4208 | 1050 | 0.0008        | -               |
| 1.4885 | 1100 | 0.001         | -               |
| 1.5562 | 1150 | 0.0009        | -               |
| 1.6238 | 1200 | 0.001         | -               |
| 1.6915 | 1250 | 0.0009        | -               |
| 1.7591 | 1300 | 0.0009        | -               |
| 1.8268 | 1350 | 0.0008        | -               |
| 1.8945 | 1400 | 0.0007        | -               |
| 1.9621 | 1450 | 0.0008        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.2
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.16.1
- Tokenizers: 0.15.0

## 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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