setfit safetensors mpnet sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:sentence-transformers/paraphrase-mpnet-base-v2 base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2 region:us
---
base_model: sentence-transformers/paraphrase-mpnet-base-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: is completely right on this. carnildo’s comment is just a waste of space.
    176.12.107.140
- text: '"   please do not vandalize pages, as you did with this edit to bella swan.
    if you continue to do so, you will be blocked from editing.   (talk) "'
- text: ipv6   mirc doesn't natively supports ipv6 protocols. it could be enabled
    by adding a external dll plugin who will enable a special protocol for dns and
    connecting to ipv6 servers.
- text: '"   link   thanks for fixing that disambiguation link on usher''s album )
    flash; "'
- text: '|b-class-1= yes  |b-class-2= yes  |b-class-3= yes  |b-class-4= yes  |b-class-5=
    yes'
inference: true
---

# SetFit with sentence-transformers/paraphrase-mpnet-base-v2

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **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:** 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0     | <ul><li>', metal unless it was 70s oldskool'</li><li>"do whichever you think would be best then: if you rename the account, just let me know, and i'll go over there to recreate it; but if you'd prefer to rename the account, recreate it, and send me the password which i can then change, that's fine with me."</li><li>'" no, it was a far-too-much-of-an-in-joke on the fact that principle→principal is usually one of the first things the fa regulars jump on (along with the dreaded spaced em dash)\xa0–\xa0scent "'</li></ul> |
| 1     | <ul><li>'heey fuckface you are a real bitch ass pussies whipped muthafucking faggot shitfaced dick head'</li><li>'you cocksucker  fuck you ok? i hate you! stop reverting my edits. i will kill you. fuck off and go fuck urself, cockmuncher!'</li><li>'fuck you   youfuckingidiot'</li></ul>                                                                                                                                                                                                                                             |

## 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("waterabbit114/my-setfit-classifier_severe_toxic")
# Run inference
preds = model("\"   link   thanks for fixing that disambiguation link on usher's album ) flash; \"")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 72.05  | 426 |

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

### Training Hyperparameters
- batch_size: (1, 1)
- 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0013 | 1    | 0.1472        | -               |
| 0.0625 | 50   | 0.0173        | -               |
| 0.125  | 100  | 0.0288        | -               |
| 0.1875 | 150  | 0.0004        | -               |
| 0.25   | 200  | 0.0002        | -               |
| 0.3125 | 250  | 0.0012        | -               |
| 0.375  | 300  | 0.0011        | -               |
| 0.4375 | 350  | 0.0012        | -               |
| 0.5    | 400  | 0.0018        | -               |
| 0.5625 | 450  | 0.0009        | -               |
| 0.625  | 500  | 0.0001        | -               |
| 0.6875 | 550  | 0.0           | -               |
| 0.75   | 600  | 0.0005        | -               |
| 0.8125 | 650  | 0.0           | -               |
| 0.875  | 700  | 0.0           | -               |
| 0.9375 | 750  | 0.0           | -               |
| 1.0    | 800  | 0.0           | -               |
| 1.0625 | 850  | 0.0002        | -               |
| 1.125  | 900  | 0.0           | -               |
| 1.1875 | 950  | 0.0003        | -               |
| 1.25   | 1000 | 0.0003        | -               |
| 1.3125 | 1050 | 0.0           | -               |
| 1.375  | 1100 | 0.0           | -               |
| 1.4375 | 1150 | 0.0001        | -               |
| 1.5    | 1200 | 0.0           | -               |
| 1.5625 | 1250 | 0.0001        | -               |
| 1.625  | 1300 | 0.0001        | -               |
| 1.6875 | 1350 | 0.0           | -               |
| 1.75   | 1400 | 0.0           | -               |
| 1.8125 | 1450 | 0.0           | -               |
| 1.875  | 1500 | 0.0004        | -               |
| 1.9375 | 1550 | 0.0           | -               |
| 2.0    | 1600 | 0.0003        | -               |
| 2.0625 | 1650 | 0.0001        | -               |
| 2.125  | 1700 | 0.0001        | -               |
| 2.1875 | 1750 | 0.0001        | -               |
| 2.25   | 1800 | 0.0           | -               |
| 2.3125 | 1850 | 0.0           | -               |
| 2.375  | 1900 | 0.0001        | -               |
| 2.4375 | 1950 | 0.0001        | -               |
| 2.5    | 2000 | 0.0           | -               |
| 2.5625 | 2050 | 0.0           | -               |
| 2.625  | 2100 | 0.0           | -               |
| 2.6875 | 2150 | 0.0           | -               |
| 2.75   | 2200 | 0.0002        | -               |
| 2.8125 | 2250 | 0.0001        | -               |
| 2.875  | 2300 | 0.0           | -               |
| 2.9375 | 2350 | 0.0001        | -               |
| 3.0    | 2400 | 0.0001        | -               |
| 3.0625 | 2450 | 0.0           | -               |
| 3.125  | 2500 | 0.0           | -               |
| 3.1875 | 2550 | 0.0001        | -               |
| 3.25   | 2600 | 0.0           | -               |
| 3.3125 | 2650 | 0.0           | -               |
| 3.375  | 2700 | 0.0           | -               |
| 3.4375 | 2750 | 0.0           | -               |
| 3.5    | 2800 | 0.0           | -               |
| 3.5625 | 2850 | 0.0           | -               |
| 3.625  | 2900 | 0.0           | -               |
| 3.6875 | 2950 | 0.0001        | -               |
| 3.75   | 3000 | 0.0           | -               |
| 3.8125 | 3050 | 0.0           | -               |
| 3.875  | 3100 | 0.0           | -               |
| 3.9375 | 3150 | 0.0           | -               |
| 4.0    | 3200 | 0.0           | -               |
| 4.0625 | 3250 | 0.0           | -               |
| 4.125  | 3300 | 0.0           | -               |
| 4.1875 | 3350 | 0.0           | -               |
| 4.25   | 3400 | 0.0           | -               |
| 4.3125 | 3450 | 0.0           | -               |
| 4.375  | 3500 | 0.0           | -               |
| 4.4375 | 3550 | 0.0001        | -               |
| 4.5    | 3600 | 0.0           | -               |
| 4.5625 | 3650 | 0.0           | -               |
| 4.625  | 3700 | 0.0           | -               |
| 4.6875 | 3750 | 0.0           | -               |
| 4.75   | 3800 | 0.0           | -               |
| 4.8125 | 3850 | 0.0           | -               |
| 4.875  | 3900 | 0.0           | -               |
| 4.9375 | 3950 | 0.0           | -               |
| 5.0    | 4000 | 0.0           | -               |
| 5.0625 | 4050 | 0.0001        | -               |
| 5.125  | 4100 | 0.0           | -               |
| 5.1875 | 4150 | 0.0           | -               |
| 5.25   | 4200 | 0.0           | -               |
| 5.3125 | 4250 | 0.0           | -               |
| 5.375  | 4300 | 0.0           | -               |
| 5.4375 | 4350 | 0.0           | -               |
| 5.5    | 4400 | 0.0           | -               |
| 5.5625 | 4450 | 0.0           | -               |
| 5.625  | 4500 | 0.0           | -               |
| 5.6875 | 4550 | 0.0           | -               |
| 5.75   | 4600 | 0.0           | -               |
| 5.8125 | 4650 | 0.0           | -               |
| 5.875  | 4700 | 0.0           | -               |
| 5.9375 | 4750 | 0.0           | -               |
| 6.0    | 4800 | 0.0           | -               |
| 6.0625 | 4850 | 0.0           | -               |
| 6.125  | 4900 | 0.0001        | -               |
| 6.1875 | 4950 | 0.0001        | -               |
| 6.25   | 5000 | 0.0           | -               |
| 6.3125 | 5050 | 0.0           | -               |
| 6.375  | 5100 | 0.0           | -               |
| 6.4375 | 5150 | 0.0           | -               |
| 6.5    | 5200 | 0.0           | -               |
| 6.5625 | 5250 | 0.0           | -               |
| 6.625  | 5300 | 0.0           | -               |
| 6.6875 | 5350 | 0.0           | -               |
| 6.75   | 5400 | 0.0           | -               |
| 6.8125 | 5450 | 0.0           | -               |
| 6.875  | 5500 | 0.0           | -               |
| 6.9375 | 5550 | 0.0           | -               |
| 7.0    | 5600 | 0.0           | -               |
| 7.0625 | 5650 | 0.0           | -               |
| 7.125  | 5700 | 0.0           | -               |
| 7.1875 | 5750 | 0.0           | -               |
| 7.25   | 5800 | 0.0           | -               |
| 7.3125 | 5850 | 0.0           | -               |
| 7.375  | 5900 | 0.0           | -               |
| 7.4375 | 5950 | 0.0           | -               |
| 7.5    | 6000 | 0.0           | -               |
| 7.5625 | 6050 | 0.0           | -               |
| 7.625  | 6100 | 0.0           | -               |
| 7.6875 | 6150 | 0.0           | -               |
| 7.75   | 6200 | 0.0           | -               |
| 7.8125 | 6250 | 0.0           | -               |
| 7.875  | 6300 | 0.0           | -               |
| 7.9375 | 6350 | 0.0           | -               |
| 8.0    | 6400 | 0.0           | -               |
| 8.0625 | 6450 | 0.0           | -               |
| 8.125  | 6500 | 0.0           | -               |
| 8.1875 | 6550 | 0.0           | -               |
| 8.25   | 6600 | 0.0           | -               |
| 8.3125 | 6650 | 0.0           | -               |
| 8.375  | 6700 | 0.0           | -               |
| 8.4375 | 6750 | 0.0           | -               |
| 8.5    | 6800 | 0.0           | -               |
| 8.5625 | 6850 | 0.0           | -               |
| 8.625  | 6900 | 0.0           | -               |
| 8.6875 | 6950 | 0.0001        | -               |
| 8.75   | 7000 | 0.0           | -               |
| 8.8125 | 7050 | 0.0           | -               |
| 8.875  | 7100 | 0.0           | -               |
| 8.9375 | 7150 | 0.0           | -               |
| 9.0    | 7200 | 0.0           | -               |
| 9.0625 | 7250 | 0.0           | -               |
| 9.125  | 7300 | 0.0           | -               |
| 9.1875 | 7350 | 0.0           | -               |
| 9.25   | 7400 | 0.0           | -               |
| 9.3125 | 7450 | 0.0           | -               |
| 9.375  | 7500 | 0.0           | -               |
| 9.4375 | 7550 | 0.0           | -               |
| 9.5    | 7600 | 0.0           | -               |
| 9.5625 | 7650 | 0.0           | -               |
| 9.625  | 7700 | 0.0           | -               |
| 9.6875 | 7750 | 0.0           | -               |
| 9.75   | 7800 | 0.0           | -               |
| 9.8125 | 7850 | 0.0           | -               |
| 9.875  | 7900 | 0.0           | -               |
| 9.9375 | 7950 | 0.0           | -               |
| 10.0   | 8000 | 0.0           | -               |

### Framework Versions
- Python: 3.11.7
- SetFit: 1.0.3
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.1+cu121
- Datasets: 2.14.5
- Tokenizers: 0.15.1

## 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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