---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
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'
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-mpnet-base-v2
---
# 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
### 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 | - ', metal unless it was 70s oldskool'
- "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."
- '" 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 "'
|
| 1 | - 'heey fuckface you are a real bitch ass pussies whipped muthafucking faggot shitfaced dick head'
- 'you cocksucker fuck you ok? i hate you! stop reverting my edits. i will kill you. fuck off and go fuck urself, cockmuncher!'
- 'fuck you youfuckingidiot'
|
## 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; \"")
```
## 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}
}
```