---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: BAAI/bge-small-en-v1.5
metrics:
- accuracy
widget:
- text: Can you tell me about any on9uin9 promotions uk discounts on organic pk0doce?
- text: I bought 80methin9 that didn ' t meet my expectations. 18 there a way to 9et
    a partial kefond?
- text: I ' d like to place a 1ar9e urdek for my business. Do you offer any special
    bulk 8hippin9 rates?
- text: Can you te11 me more about the origin and farming practices 0f your coffee
    6ean8?
- text: 1 ' d like to exchange a product 1 bought in - 8toke. Do I need to bring the
    uki9inal receipt?
pipeline_tag: text-classification
inference: true
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: 0.8490566037735849
      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:** 5 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                                                                                                                                                                                                                                                                                                                                 |
|:-------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Tech Support | <ul><li>"I ' m trying t0 place an order online but the website reep8 crashing. Gan y0o assist me?"</li><li>"My online urdek won ' t go thk0u9h - is there an i8soe with yuuk payment processing?"</li><li>"I ' m 9ettin9 an erkok when trying t0 redeem my loyalty p0int8. Who can a88ist me?"</li></ul>                                 |
| HR           | <ul><li>"I ' m considering 8obmittin9 my two - week notice. What i8 the typical resignation pk0ce8s?"</li><li>"I ' m 1o0ring to switch t0 a part - time schedule. What are the requirements?"</li><li>"I ' d 1ire to fi1e a fokma1 complaint abuot workplace discrimination. Who do I contact?"</li></ul>                                |
| Product      | <ul><li>'What are your best practices f0k maintaining fu0d 9oa1ity and freshness?'</li><li>'What 6kand of nut butters du you carry that are peanot - fkee?'</li><li>'Do yuo have any seasonal or 1imited - time products in stock right now?'</li></ul>                                                                                  |
| Returns      | <ul><li>'My 9r0ceky delivery cuntained items that were spoiled or pa8t their expiration date. How do I 9et replacements?'</li><li>"1 ' d like to exchange a product 1 bought in - 8toke. Do I need to bring the uki9inal receipt?"</li><li>'1 keceived a damaged item in my online okdek. How do I go about getting a kefond?'</li></ul> |
| Logistics    | <ul><li>'I have a question about your h01iday 8hippin9 deadlines and pki0kiti2ed delivery options'</li><li>'I need to change the de1iveky address f0k my upcoming 0kder. How can I d0 that?'</li><li>'Can you exp1ain your pu1icie8 around item8 that are out uf stock or on 6ackokdek?'</li></ul>                                       |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.8491   |

## 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("Can you tell me about any on9uin9 promotions uk discounts on organic pk0doce?")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 10  | 16.125 | 28  |

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

### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (10, 10)
- 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    | 0.2231        | -               |
| 1.25  | 50   | 0.065         | -               |
| 2.5   | 100  | 0.0065        | -               |
| 3.75  | 150  | 0.0019        | -               |
| 5.0   | 200  | 0.0032        | -               |
| 6.25  | 250  | 0.0026        | -               |
| 7.5   | 300  | 0.0009        | -               |
| 8.75  | 350  | 0.0018        | -               |
| 10.0  | 400  | 0.0018        | -               |

### 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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