---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: it does not make sense because sally believe its makes sense and at the same
    time does not make  sense to help the homeless.
- text: it contradicts itself- how can something be right and you then think it's
    not right?
- text: it made sense because it is tom's opinion that cyberbullying is not wrong.
- text: a person can think it is raining even when it is. there is nothing wrong with
    thinking that way. the thought makes sense even though the fact is incorrect.
- text: they contradict their own opinions on the morals. although i can understand
    how they came to that conclusion. perhaps they mean, helping the homeless is morally
    right, however it's not right for my situation. context and clarification is key
    here.
metrics:
- accuracy
- precision
- recall
- f1
pipeline_tag: text-classification
library_name: setfit
inference: true
model-index:
- name: SetFit
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.9210526315789473
      name: Accuracy
    - type: precision
      value: 0.9198717948717949
      name: Precision
    - type: recall
      value: 0.9030769230769231
      name: Recall
    - type: f1
      value: 0.9105882352941177
      name: F1
---

# SetFit

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. 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:** [Unknown](https://huggingface.co/unknown) -->
- **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:** 3 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                                                                                                                                                                                                                                                                                                         |
|:-----------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Enrichment / reinterpretation                  | <ul><li>'the statement recognised the objective compassion but the opinion contradicted it'</li><li>"the person's individual belief doesn't tally with the accepted belief; this is perfectly reasonable."</li><li>'cyberbully may seem cruel to everyone, but to tom, he does not feel cruel to him.'</li></ul> |
| Linguistic (in)felicity                        | <ul><li>'because if its wrong how can you then make a statement saying it is not wrong'</li><li>'it is contradictory.'</li><li>'because the writer just stated that it s raining so how could she then not know if it is raining?'</li></ul>                                                                     |
| Lack of understanding / clear misunderstanding | <ul><li>'it sounds very contradictory'</li><li>'it reads well and makes sense'</li><li>'it make not sense on one hand help the homeless people is right, on the hand hand it is not unethical.'</li></ul>                                                                                                        |

## Evaluation

### Metrics
| Label   | Accuracy | Precision | Recall | F1     |
|:--------|:---------|:----------|:-------|:-------|
| **all** | 0.9211   | 0.9199    | 0.9031 | 0.9106 |

## 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("it made sense because it is tom's opinion that cyberbullying is not wrong.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 2   | 16.375 | 92  |

| Label                                          | Training Sample Count |
|:-----------------------------------------------|:----------------------|
| Enrichment / reinterpretation                  | 29                    |
| Lack of understanding / clear misunderstanding | 11                    |
| Linguistic (in)felicity                        | 112                   |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (10, 10)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- 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: 376
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0026 | 1    | 0.2512        | -               |
| 0.1316 | 50   | 0.2213        | -               |
| 0.2632 | 100  | 0.1707        | -               |
| 0.3947 | 150  | 0.0839        | -               |
| 0.5263 | 200  | 0.0335        | -               |
| 0.6579 | 250  | 0.0141        | -               |
| 0.7895 | 300  | 0.0072        | -               |
| 0.9211 | 350  | 0.0026        | -               |
| 1.0526 | 400  | 0.0008        | -               |
| 1.1842 | 450  | 0.0006        | -               |
| 1.3158 | 500  | 0.0004        | -               |
| 1.4474 | 550  | 0.0002        | -               |
| 1.5789 | 600  | 0.0002        | -               |
| 1.7105 | 650  | 0.0002        | -               |
| 1.8421 | 700  | 0.0002        | -               |
| 1.9737 | 750  | 0.0002        | -               |
| 2.1053 | 800  | 0.0002        | -               |
| 2.2368 | 850  | 0.0002        | -               |
| 2.3684 | 900  | 0.0001        | -               |
| 2.5    | 950  | 0.0001        | -               |
| 2.6316 | 1000 | 0.0001        | -               |
| 2.7632 | 1050 | 0.0001        | -               |
| 2.8947 | 1100 | 0.0001        | -               |
| 3.0263 | 1150 | 0.0001        | -               |
| 3.1579 | 1200 | 0.0001        | -               |
| 3.2895 | 1250 | 0.0001        | -               |
| 3.4211 | 1300 | 0.0001        | -               |
| 3.5526 | 1350 | 0.0001        | -               |
| 3.6842 | 1400 | 0.0001        | -               |
| 3.8158 | 1450 | 0.0001        | -               |
| 3.9474 | 1500 | 0.0001        | -               |
| 4.0789 | 1550 | 0.0002        | -               |
| 4.2105 | 1600 | 0.0001        | -               |
| 4.3421 | 1650 | 0.0033        | -               |
| 4.4737 | 1700 | 0.0001        | -               |
| 4.6053 | 1750 | 0.0004        | -               |
| 4.7368 | 1800 | 0.0035        | -               |
| 4.8684 | 1850 | 0.0002        | -               |
| 5.0    | 1900 | 0.0003        | -               |
| 5.1316 | 1950 | 0.0001        | -               |
| 5.2632 | 2000 | 0.0001        | -               |
| 5.3947 | 2050 | 0.0001        | -               |
| 5.5263 | 2100 | 0.0001        | -               |
| 5.6579 | 2150 | 0.0001        | -               |
| 5.7895 | 2200 | 0.0001        | -               |
| 5.9211 | 2250 | 0.0001        | -               |
| 6.0526 | 2300 | 0.0001        | -               |
| 6.1842 | 2350 | 0.0001        | -               |
| 6.3158 | 2400 | 0.0001        | -               |
| 6.4474 | 2450 | 0.0001        | -               |
| 6.5789 | 2500 | 0.0001        | -               |
| 6.7105 | 2550 | 0.0001        | -               |
| 6.8421 | 2600 | 0.0001        | -               |
| 6.9737 | 2650 | 0.0001        | -               |
| 7.1053 | 2700 | 0.0001        | -               |
| 7.2368 | 2750 | 0.0001        | -               |
| 7.3684 | 2800 | 0.0001        | -               |
| 7.5    | 2850 | 0.0           | -               |
| 7.6316 | 2900 | 0.0001        | -               |
| 7.7632 | 2950 | 0.0001        | -               |
| 7.8947 | 3000 | 0.0001        | -               |
| 8.0263 | 3050 | 0.0001        | -               |
| 8.1579 | 3100 | 0.0001        | -               |
| 8.2895 | 3150 | 0.0001        | -               |
| 8.4211 | 3200 | 0.0001        | -               |
| 8.5526 | 3250 | 0.0001        | -               |
| 8.6842 | 3300 | 0.0001        | -               |
| 8.8158 | 3350 | 0.0001        | -               |
| 8.9474 | 3400 | 0.0001        | -               |
| 9.0789 | 3450 | 0.0001        | -               |
| 9.2105 | 3500 | 0.0001        | -               |
| 9.3421 | 3550 | 0.0           | -               |
| 9.4737 | 3600 | 0.0           | -               |
| 9.6053 | 3650 | 0.0001        | -               |
| 9.7368 | 3700 | 0.0001        | -               |
| 9.8684 | 3750 | 0.0           | -               |
| 10.0   | 3800 | 0.0           | -               |

### Framework Versions
- Python: 3.11.9
- SetFit: 1.1.2
- Sentence Transformers: 4.1.0
- Transformers: 4.52.4
- PyTorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1

## 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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