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 -->
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### 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>"i'm going to kill you   zink dawg your a scrap and a fag and you need to die. i'm going to kill you if someone else doesn't. you better keep protecting your user space because i will keep vandalizing it."</li><li>"hope your head gets cut off and someone wipes there ass with it and then stabs you in your heart!!! no one can keep me from here you dumb piece of shit, not yamla's ass ass or you!!! now hurry up and sit in some traffic so i can see your head roll down the street"</li><li>'unblock me now you piece of shit! or i will find you and kill you, slowly and painfully!'</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_threat")
# 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   | 50.65  | 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    | 0.192         | -               |
| 0.0625 | 50   | 0.0173        | -               |
| 0.125  | 100  | 0.0013        | -               |
| 0.1875 | 150  | 0.0024        | -               |
| 0.25   | 200  | 0.0002        | -               |
| 0.3125 | 250  | 0.0           | -               |
| 0.375  | 300  | 0.0           | -               |
| 0.4375 | 350  | 0.0006        | -               |
| 0.5    | 400  | 0.0003        | -               |
| 0.5625 | 450  | 0.0001        | -               |
| 0.625  | 500  | 0.0001        | -               |
| 0.6875 | 550  | 0.0002        | -               |
| 0.75   | 600  | 0.0008        | -               |
| 0.8125 | 650  | 0.0002        | -               |
| 0.875  | 700  | 0.0001        | -               |
| 0.9375 | 750  | 0.0009        | -               |
| 1.0    | 800  | 0.0001        | -               |
| 1.0625 | 850  | 0.0001        | -               |
| 1.125  | 900  | 0.0001        | -               |
| 1.1875 | 950  | 0.0           | -               |
| 1.25   | 1000 | 0.0           | -               |
| 1.3125 | 1050 | 0.0           | -               |
| 1.375  | 1100 | 0.0001        | -               |
| 1.4375 | 1150 | 0.0           | -               |
| 1.5    | 1200 | 0.0           | -               |
| 1.5625 | 1250 | 0.0           | -               |
| 1.625  | 1300 | 0.0           | -               |
| 1.6875 | 1350 | 0.0           | -               |
| 1.75   | 1400 | 0.0003        | -               |
| 1.8125 | 1450 | 0.0001        | -               |
| 1.875  | 1500 | 0.0           | -               |
| 1.9375 | 1550 | 0.0001        | -               |
| 2.0    | 1600 | 0.0           | -               |
| 2.0625 | 1650 | 0.0           | -               |
| 2.125  | 1700 | 0.0001        | -               |
| 2.1875 | 1750 | 0.0           | -               |
| 2.25   | 1800 | 0.0           | -               |
| 2.3125 | 1850 | 0.0           | -               |
| 2.375  | 1900 | 0.0           | -               |
| 2.4375 | 1950 | 0.0           | -               |
| 2.5    | 2000 | 0.0           | -               |
| 2.5625 | 2050 | 0.0           | -               |
| 2.625  | 2100 | 0.0001        | -               |
| 2.6875 | 2150 | 0.0           | -               |
| 2.75   | 2200 | 0.0           | -               |
| 2.8125 | 2250 | 0.0002        | -               |
| 2.875  | 2300 | 0.0           | -               |
| 2.9375 | 2350 | 0.0           | -               |
| 3.0    | 2400 | 0.0002        | -               |
| 3.0625 | 2450 | 0.0           | -               |
| 3.125  | 2500 | 0.0001        | -               |
| 3.1875 | 2550 | 0.0001        | -               |
| 3.25   | 2600 | 0.0001        | -               |
| 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 | 0.0           | -               |
| 3.75   | 3000 | 0.0           | -               |
| 3.8125 | 3050 | 0.0           | -               |
| 3.875  | 3100 | 0.0002        | -               |
| 3.9375 | 3150 | 0.0           | -               |
| 4.0    | 3200 | 0.0           | -               |
| 4.0625 | 3250 | 0.0001        | -               |
| 4.125  | 3300 | 0.0001        | -               |
| 4.1875 | 3350 | 0.0           | -               |
| 4.25   | 3400 | 0.0004        | -               |
| 4.3125 | 3450 | 0.0001        | -               |
| 4.375  | 3500 | 0.0001        | -               |
| 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 | 0.0001        | -               |
| 4.9375 | 3950 | 0.0           | -               |
| 5.0    | 4000 | 0.0           | -               |
| 5.0625 | 4050 | 0.0           | -               |
| 5.125  | 4100 | 0.0           | -               |
| 5.1875 | 4150 | 0.0           | -               |
| 5.25   | 4200 | 0.0           | -               |
| 5.3125 | 4250 | 0.0002        | -               |
| 5.375  | 4300 | 0.0           | -               |
| 5.4375 | 4350 | 0.0           | -               |
| 5.5    | 4400 | 0.0           | -               |
| 5.5625 | 4450 | 0.0001        | -               |
| 5.625  | 4500 | 0.0           | -               |
| 5.6875 | 4550 | 0.0           | -               |
| 5.75   | 4600 | 0.0002        | -               |
| 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 | 0.0           | -               |
| 6.1875 | 4950 | 0.0           | -               |
| 6.25   | 5000 | 0.0           | -               |
| 6.3125 | 5050 | 0.0           | -               |
| 6.375  | 5100 | 0.0001        | -               |
| 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 | 0.0001        | -               |
| 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 | 0.0           | -               |
| 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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