---
language: en
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: the nerves,blood vessels, and glands are located in which layer of the skin
- text: Where would you put refuse if you do not want it to exist any more?
- text: Obesity can cause resistance to which hormone?
- text: Referees
- text: where does the water at niagra falls come from
metrics:
- '0'
- '1'
- accuracy
- macro avg
- weighted avg
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: BAAI/bge-small-en-v1.5
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: 0.37465309898242366
        recall: 0.989413680781759
        f1-score: 0.5435025721315142
        support: 1228.0
      name: '0'
    - type: '1'
      value:
        precision: 0.9940962761126249
        recall: 0.5190894000474271
        f1-score: 0.6820377005764138
        support: 4217.0
      name: '1'
    - type: accuracy
      value: 0.6251606978879706
      name: Accuracy
    - type: macro avg
      value:
        precision: 0.6843746875475243
        recall: 0.7542515404145931
        f1-score: 0.6127701363539639
        support: 5445.0
      name: Macro Avg
    - type: weighted avg
      value:
        precision: 0.8543944907102582
        recall: 0.6251606978879706
        f1-score: 0.6507941491107871
        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>'Where would you put refuse if you do not want it to exist any more?'</li><li>'Which is an example of the absolutism under peter the great?'</li><li>'where does the water at niagra falls come from'</li></ul> |
| 0     | <ul><li>'the nerves,blood vessels, and glands are located in which layer of the skin'</li><li>'Of what discipline is affective computing a branch?'</li><li>'Obesity can cause resistance to which hormone?'</li></ul>  |

## Evaluation

### Metrics
| Label   | 0                                                                                                                  | 1                                                                                                                  | Accuracy | Macro Avg                                                                                                          | Weighted Avg                                                                                                       |
|:--------|:-------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------|:---------|:-------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------|
| **all** | {'precision': 0.37465309898242366, 'recall': 0.989413680781759, 'f1-score': 0.5435025721315142, 'support': 1228.0} | {'precision': 0.9940962761126249, 'recall': 0.5190894000474271, 'f1-score': 0.6820377005764138, 'support': 4217.0} | 0.6252   | {'precision': 0.6843746875475243, 'recall': 0.7542515404145931, 'f1-score': 0.6127701363539639, 'support': 5445.0} | {'precision': 0.8543944907102582, 'recall': 0.6251606978879706, 'f1-score': 0.6507941491107871, '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("fuhakiem/hin-v001-trainer")
# Run inference
preds = model("Referees")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 1   | 7.3    | 15  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 5                     |
| 1     | 5                     |

### 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 |
|:-----:|:----:|:-------------:|:---------------:|
| 0.5   | 1    | 0.1957        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.3.1
- Transformers: 4.42.2
- PyTorch: 2.5.1+cu124
- Datasets: 3.2.0
- 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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