---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Your mic is pure ear rape you deaf bastard.
- text: your heart grows the best kind of magic
- text: chimichanga
- text: cooked him
- text: your mom aborted you too late
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: jhu-clsp/mmBERT-base
model-index:
- name: SetFit with jhu-clsp/mmBERT-base
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.9969719909159728
      name: Accuracy
---

# SetFit with jhu-clsp/mmBERT-base

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [jhu-clsp/mmBERT-base](https://huggingface.co/jhu-clsp/mmBERT-base) 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:** [jhu-clsp/mmBERT-base](https://huggingface.co/jhu-clsp/mmBERT-base)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 8192 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                                                                                                       |
|:----------|:---------------------------------------------------------------------------------------------------------------|
| toxic     | <ul><li>'slanteye'</li><li>'lets meet in real life cutie ;)'</li><li>'youre dead to me and soon irl'</li></ul> |
| not toxic | <ul><li>'wanna make a snowman?'</li><li>'word'</li><li>'boost'</li></ul>                                       |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9970   |

## 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("johnpaulbin/toxicity-setfit-5-large")
# Run inference
preds = model("cooked him")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 1   | 4.6067 | 81  |

| Label     | Training Sample Count |
|:----------|:----------------------|
| not toxic | 8689                  |
| toxic     | 4512                  |

### Training Hyperparameters
- batch_size: (128, 128)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: num_iterations
- num_iterations: 12
- 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: True
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0004 | 1    | 0.3044        | -               |
| 0.0202 | 50   | 0.2803        | -               |
| 0.0404 | 100  | 0.2355        | -               |
| 0.0606 | 150  | 0.1607        | -               |
| 0.0808 | 200  | 0.0911        | -               |
| 0.1010 | 250  | 0.0694        | -               |
| 0.1212 | 300  | 0.0532        | -               |
| 0.1414 | 350  | 0.0391        | -               |
| 0.1616 | 400  | 0.0343        | -               |
| 0.1817 | 450  | 0.0296        | -               |
| 0.2019 | 500  | 0.0264        | -               |
| 0.2221 | 550  | 0.022         | -               |
| 0.2423 | 600  | 0.0201        | -               |
| 0.2625 | 650  | 0.0127        | -               |
| 0.2827 | 700  | 0.0165        | -               |
| 0.3029 | 750  | 0.0097        | -               |
| 0.3231 | 800  | 0.0093        | -               |
| 0.3433 | 850  | 0.0098        | -               |
| 0.3635 | 900  | 0.009         | -               |
| 0.3837 | 950  | 0.0076        | -               |
| 0.4039 | 1000 | 0.0073        | -               |
| 0.4241 | 1050 | 0.0076        | -               |
| 0.4443 | 1100 | 0.0058        | -               |
| 0.4645 | 1150 | 0.0049        | -               |
| 0.4847 | 1200 | 0.0066        | -               |
| 0.5048 | 1250 | 0.0044        | -               |
| 0.5250 | 1300 | 0.0052        | -               |
| 0.5452 | 1350 | 0.0041        | -               |
| 0.5654 | 1400 | 0.0061        | -               |
| 0.5856 | 1450 | 0.0033        | -               |
| 0.6058 | 1500 | 0.0051        | -               |
| 0.6260 | 1550 | 0.0029        | -               |
| 0.6462 | 1600 | 0.0039        | -               |
| 0.6664 | 1650 | 0.0046        | -               |
| 0.6866 | 1700 | 0.0028        | -               |
| 0.7068 | 1750 | 0.0037        | -               |
| 0.7270 | 1800 | 0.0022        | -               |
| 0.7472 | 1850 | 0.0032        | -               |
| 0.7674 | 1900 | 0.0025        | -               |
| 0.7876 | 1950 | 0.0027        | -               |
| 0.8078 | 2000 | 0.0024        | -               |
| 0.8279 | 2050 | 0.0031        | -               |
| 0.8481 | 2100 | 0.0024        | -               |
| 0.8683 | 2150 | 0.0033        | -               |
| 0.8885 | 2200 | 0.0039        | -               |
| 0.9087 | 2250 | 0.0028        | -               |
| 0.9289 | 2300 | 0.0029        | -               |
| 0.9491 | 2350 | 0.0023        | -               |
| 0.9693 | 2400 | 0.0022        | -               |
| 0.9895 | 2450 | 0.0027        | -               |
| 1.0    | 2476 | -             | 0.0046          |

### Framework Versions
- Python: 3.12.12
- SetFit: 1.2.0.dev0
- Sentence Transformers: 5.2.0
- Transformers: 4.57.3
- PyTorch: 2.9.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.22.2

## 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}
}
```

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