---
base_model: BAAI/bge-small-en-v1.5
language: en
library_name: setfit
license: apache-2.0
metrics:
- '0'
- '1'
- accuracy
- macro avg
- weighted avg
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Of what discipline is affective computing a branch?
- text: why is my mitsubishi aircon light blinking
- text: Obesity can cause resistance to which hormone?
- text: farm beer garden - ohio
- text: Where did Reagan and Gorbachev have their Star Wars summit in October 19865?
inference: true
model-index:
- name: SetFit with BAAI/bge-small-en-v1.5 on Health Information Needs
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Health Information Needs
      type: unknown
      split: test
    metrics:
    - type: '0'
      value:
        precision: 0.5862573099415205
        recall: 0.9796416938110749
        f1-score: 0.7335365853658536
        support: 1228.0
      name: '0'
    - type: '1'
      value:
        precision: 0.9926318891836133
        recall: 0.7986720417358312
        f1-score: 0.8851511169513797
        support: 4217.0
      name: '1'
    - type: accuracy
      value: 0.8394857667584941
      name: Accuracy
    - type: macro avg
      value:
        precision: 0.789444599562567
        recall: 0.889156867773453
        f1-score: 0.8093438511586166
        support: 5445.0
      name: Macro Avg
    - type: weighted avg
      value:
        precision: 0.9009830400909982
        recall: 0.8394857667584941
        f1-score: 0.8509577937581702
        support: 5445.0
      name: Weighted Avg
---

# SetFit with BAAI/bge-small-en-v1.5 on Health Information Needs

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:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
- **Language:** en
- **License:** apache-2.0

### 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                                                                                                                                                    |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1     | <ul><li>'why is my mitsubishi aircon light blinking'</li><li>'what was legalism'</li><li>'farm beer garden - ohio'</li></ul>                                |
| 0     | <ul><li>'what makes an adult vulnerable'</li><li>'Of what discipline is affective computing a branch?'</li><li>'What does a ribosome consist of?'</li></ul> |

## Evaluation

### Metrics
| Label   | 0                                                                                                                  | 1                                                                                                                  | Accuracy | Macro Avg                                                                                                        | Weighted Avg                                                                                                       |
|:--------|:-------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------|:---------|:-----------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------|
| **all** | {'precision': 0.5862573099415205, 'recall': 0.9796416938110749, 'f1-score': 0.7335365853658536, 'support': 1228.0} | {'precision': 0.9926318891836133, 'recall': 0.7986720417358312, 'f1-score': 0.8851511169513797, 'support': 4217.0} | 0.8395   | {'precision': 0.789444599562567, 'recall': 0.889156867773453, 'f1-score': 0.8093438511586166, 'support': 5445.0} | {'precision': 0.9009830400909982, 'recall': 0.8394857667584941, 'f1-score': 0.8509577937581702, 'support': 5445.0} |

## 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("setfit_model_id")
# Run inference
preds = model("farm beer garden - ohio")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 1   | 7.2    | 15  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 10                    |
| 1     | 10                    |

### 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.1429 | 1    | 0.1987        | -               |
| 7.1429 | 50   | 0.1561        | -               |

### Framework Versions
- Python: 3.12.2
- SetFit: 1.1.0
- Sentence Transformers: 3.0.1
- Transformers: 4.45.2
- PyTorch: 2.2.2
- Datasets: 3.1.0
- Tokenizers: 0.20.3

## 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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