---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Although traditional database search methods can effectively identify peptide
    matches, this approach correlates tandem mass spectral data with amino acid sequences
    in a protein database 'however' providing additional confirmation and improving
    identification accuracy.
- text: The study involved 30 smallholder farmers from three different regions in
    Africa, each with an average farm size of 1.5 hectares and an annual income from
    farming of approximately $1,500.
- text: This study aimed to evaluate the efficacy and safety of interferon α2b plus
    ribavirin for 48 weeks or 24 weeks compared to interferon α2b plus placebo for
    48 weeks in the treatment of chronic hepatitis C virus infection.
- text: The study reported that 73% of the psychotherapists endorsed the use of cognitive
    techniques in their treatment of eating disorders, while 61% reported using behavioral
    techniques.
- text: Previous research on the psychoanalytic concept of the working alliance has
    established its significance in therapeutic change and identified key components
    such as the bond between therapist and client and the agreement on therapeutic
    goals.
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/all-MiniLM-L6-v2
model-index:
- name: SetFit with sentence-transformers/all-MiniLM-L6-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.9498398588143016
      name: Accuracy
---

# SetFit with sentence-transformers/all-MiniLM-L6-v2

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-v2) 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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 256 tokens
- **Number of Classes:** 2 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
|:------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Misc        | <ul><li>'Pravastatin therapy in patients with average cholesterol levels following myocardial infarction has been shown to reduce the risk of coronary events, highlighting the importance of lipid-lowering therapy in internal medicine for cardiovascular disease prevention.'</li><li>'However, the efficacy of pravastatin in patients with average cholesterol levels is less clear.'</li><li>'This study investigates the impact of Pravastatin on reducing coronary events in internal medicine patients with average cholesterol levels after a myocardial infarction.'</li></ul>                                                                                                                                                                                                                                              |
| Uncertainty | <ul><li>'Despite the widespread use of pravastatin in post-myocardial infarction patients with average cholesterol levels, the evidence regarding its impact on coronary events remains inconclusive and sometimes contradictory.'</li><li>'Despite the findings of this study showing a reduction in coronary events with Pravastatin use in patients with average cholesterol levels, contrasting evidence exists suggesting no significant benefit in similar patient populations (Miller et al., 2018).'</li><li>'Despite the proven benefits of dual antiplatelet therapy with aspirin and clopidogrel in the secondary prevention of cardiovascular events, particularly in coronary artery disease, there is a paucity of data specifically addressing its use in stroke or transient ischemic attack (TIA) patients.'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9498   |

## 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("Corran/SciGenSetfit24Binary")
# Run inference
preds = model("The study reported that 73% of the psychotherapists endorsed the use of cognitive techniques in their treatment of eating disorders, while 61% reported using behavioral techniques.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 8   | 29.6038 | 60  |

| Label       | Training Sample Count |
|:------------|:----------------------|
| Misc        | 2500                  |
| Uncertainty | 2500                  |

### Training Hyperparameters
- batch_size: (300, 300)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 5
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0060 | 1    | 0.4529        | -               |
| 0.2994 | 50   | 0.3104        | -               |
| 0.5988 | 100  | 0.2514        | -               |
| 0.8982 | 150  | 0.25          | -               |
| 1.0    | 167  | -             | 0.2479          |
| 0.0060 | 1    | 0.2406        | -               |
| 0.2994 | 50   | 0.1576        | -               |
| 0.5988 | 100  | 0.0912        | -               |
| 0.8982 | 150  | 0.0656        | -               |
| 1.0    | 167  | -             | 0.0683          |
| 0.0060 | 1    | 0.0827        | -               |
| 0.2994 | 50   | 0.0581        | -               |
| 0.5988 | 100  | 0.0393        | -               |
| 0.8982 | 150  | 0.0339        | -               |
| 1.0    | 167  | -             | 0.0516          |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.2.0.dev0
- Sentence Transformers: 3.1.1
- Transformers: 4.42.2
- PyTorch: 2.5.1+cu121
- 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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