---
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 I about eny ongoing promoistion onr discounts onteh organic produce?
- text: A bought somenting that didn ' th meet my expectations. It there ein way go
    get and partial refund?
- text: I ' d like to palac a ladge ordet for my business. Do you offer ang specialy
    bulk shopping rates?
- text: Ken you telle mo more about the origin atch farming practices of your cofffee
    beans?
- text: I ' d llike to exchange a product I bought in - store. Du hi needs yo bring
    tie oringal 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: 0.9056603773584906
      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 ' am trying to place an orden online bt Then website keeps crashing. Can you assit my?"</li><li>"Mi online order won ' t go throw - is there an isuue with years pament prossesing?"</li><li>"I ' m goning an error when tryied tou redeem my loyality points. Who cen assist we?"</li></ul>                                       |
| HR           | <ul><li>"I ' m considere submitting my ow - weeck notice. Waht It's tehe typical resignation process?"</li><li>"I ' m looking e swich to a part - time sehdule. Whate re rhe requirements?"</li><li>"In ' d loke to fill a formal complain about worksplace discrimination. Who did I contact?"</li></ul>                                     |
| Product      | <ul><li>'Whots are your best practices ofr mantain foord quality and freshness?'</li><li>'Whots newbrand ow nut butters dou you carry tahat are peanut - free?'</li><li>'Do you hafe any seasonal nor limited - tíme produts in stock rignt now?'</li></ul>                                                                                   |
| Returns      | <ul><li>'My grocery delivary contained items tath where spoiled or pas their expiration date. How dos me get replacements?'</li><li>"I ' d llike to exchange a product I bought in - store. Du hi needs yo bring tie oringal receipt?"</li><li>'I eceibed de demaged item in my online oder. Hou do I’m go about getting a refund?'</li></ul> |
| Logistics    | <ul><li>'I have a question about youtr Holiday shiping deathlines and prioritized delivery options'</li><li>'I nedd to change the delivery addrss foy mh upcoming older. How can I go that?'</li><li>'Can jou explain York polices around iterms that approxmatlly out of stock or on backorder?'</li></ul>                                   |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9057   |

## 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 I about eny ongoing promoistion onr discounts onteh organic produce?")
```

<!--
### Downstream Use

*List how someone could finetune this model on their own dataset.*
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## 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    | 0.2185        | -               |
| 1.25  | 50   | 0.0888        | -               |
| 2.5   | 100  | 0.0157        | -               |
| 3.75  | 150  | 0.0053        | -               |
| 5.0   | 200  | 0.0033        | -               |
| 6.25  | 250  | 0.004         | -               |
| 7.5   | 300  | 0.0024        | -               |
| 8.75  | 350  | 0.0027        | -               |
| 10.0  | 400  | 0.0025        | -               |

### Framework Versions
- Python: 3.11.8
- SetFit: 1.0.3
- Sentence Transformers: 2.6.1
- Transformers: 4.39.3
- PyTorch: 2.4.0.dev20240413
- Datasets: 2.18.0
- Tokenizers: 0.15.2

## 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->