---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Cheers, sololander!
- text: At least it’s not anything worse, and that you are still close to that person
    :)
- text: It's crazy how far Photoshop has come. Underwater bridges?!! NEVER!!!
- text: My favourite food is anything I didn't have to cook myself.
- text: You are going to do the dishes now
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/paraphrase-mpnet-base-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.3
      name: Accuracy
---

# 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:** 23 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                                                                                                                                                                                                                                                                                                          |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 27    | <ul><li>'that is what retardation looks like'</li><li>"Don't kiss your doorbell! Or anyone else's for that matter..."</li><li>'Hello everyone. Im from Toronto as well. Can call and visit in personal if needed.'</li></ul>                                                                                      |
| 2     | <ul><li>"Troll, bro. They know they're saying stupid shit. The motherfucker does nothing but stink up libertarian subs talking shit"</li><li>'Ok, then what the actual fuck is your plan?'</li><li>'Stupidly stubborn / stubbornly stupid'</li></ul>                                                              |
| 4     | <ul><li>'Pay you for what, just standing there? Done.'</li><li>"Sometimes life actually hands you lemons. We're just lucky that we have a proverbial phrase that gives us an idea of what we can do with them."</li><li>"true I am a troll, but fortunately for me I'm not emotionally invested in it."</li></ul> |
| 3     | <ul><li>'Dirty Southern Wankers'</li><li>'Shit, I guess I accidentally bought a Pay-Per-View boxing match'</li><li>'The republicans are the military. You are an idiot.'</li></ul>                                                                                                                                |
| 0     | <ul><li>'Awesome! I’m a cradle [RELIGION], so really interesting to hear your experience. Thanks for sharing.'</li><li>'What a wonderful world'</li><li>'Twilight... STILL a better love story than The Last Jedi!'</li></ul>                                                                                     |
| 6     | <ul><li>"All sounds possible except the key, I can't see how it was missed in the first search. "</li><li>'What does FPTP have to do with the referendum?'</li><li>'Maybe that’s what happened to the great white at Houston zoo'</li></ul>                                                                       |
| 10    | <ul><li>"This isn't really wholesome"</li></ul>                                                                                                                                                                                                                                                                   |
| 16    | <ul><li>'I read on a different post that he died shortly after of internal injuries.'</li><li>'I miss them being alive'</li></ul>                                                                                                                                                                                 |
| 7     | <ul><li>'I think the 90 day rule applies to increases over 5%?'</li><li>'So this means the people who have debt can see those that don’t. Am I sensing an easier target for muggings and such?'</li></ul>                                                                                                         |
| 1     | <ul><li>"Aww... she'll probably come around eventually, I'm sure she was just jealous of [NAME]... I mean, what woman wouldn't be! lol "</li><li>'And then they say, “HAHAHAHHA IT WAS RIGHT THERE WOW!”'</li><li>'just noticed, lol. damn pervert foreigners.'</li></ul>                                         |
| 25    | <ul><li>'my brain hurts...'</li><li>'Pretty sure I’ve seen this. He swings away with the harness he is wearing. Still looks painful but I think he lives'</li><li>'sorry [NAME]! 😘😘😘'</li></ul>                                                                                                                   |
| 15    | <ul><li>'Super, thanks'</li><li>'Thank you friend'</li><li>'Yes I heard abt the f bombs! That has to be why. Thanks for your reply:) until then hubby and I will anxiously wait 😝'</li></ul>                                                                                                                      |
| 18    | <ul><li>'I love Rocket Love and Blasted. I just wonder who the songs were written for because these are all reference tracks except Acura Intergul'</li></ul>                                                                                                                                                     |
| 26    | <ul><li>"OmG pEyToN iSn'T gOoD eNoUgH tO hElP uS iN tHe PlAyOfFs! Dumbass Broncos fans circa December 2015."</li></ul>                                                                                                                                                                                            |
| 17    | <ul><li>'Happy to be able to help.'</li></ul>                                                                                                                                                                                                                                                                     |
| 8     | <ul><li>'We need more boards and to create a bit more space for [NAME]. Then we’ll be good.'</li></ul>                                                                                                                                                                                                            |
| 5     | <ul><li>"R/sleeptrain Might be time for some sleep training. Take a look and try to feel out what's right for your family."</li></ul>                                                                                                                                                                             |
| 14    | <ul><li>'To make her feel threatened'</li></ul>                                                                                                                                                                                                                                                                   |
| 13    | <ul><li>'Very interesting. Thx'</li><li>'This...has 9k upvotes. Wow.'</li></ul>                                                                                                                                                                                                                                   |
| 20    | <ul><li>"It's true though. He either gets no shirt and freezes to death or wears a stupid looking butchers cape. I hope he gets something better next season"</li></ul>                                                                                                                                           |
| 12    | <ul><li>"I just shit my pants. Then walk away. Embarrassing enough he won't press or follow you."</li><li>'i got a bump and a bald spot. i feel dumb <3'</li></ul>                                                                                                                                                |
| 9     | <ul><li>'He was off by 5 minutes, not impressed. '</li></ul>                                                                                                                                                                                                                                                      |
| 24    | <ul><li>'Apologies, I take it all back as I’ve just seen his latest effort'</li></ul>                                                                                                                                                                                                                             |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.3      |

## 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("Cheers, sololander!")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 2   | 10.6   | 27  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 10                    |
| 1     | 3                     |
| 2     | 5                     |
| 3     | 4                     |
| 4     | 5                     |
| 5     | 1                     |
| 6     | 4                     |
| 7     | 2                     |
| 8     | 1                     |
| 9     | 1                     |
| 10    | 1                     |
| 12    | 2                     |
| 13    | 2                     |
| 14    | 1                     |
| 15    | 5                     |
| 16    | 2                     |
| 17    | 1                     |
| 18    | 1                     |
| 20    | 1                     |
| 24    | 1                     |
| 25    | 3                     |
| 26    | 1                     |
| 27    | 33                    |

### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- 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: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0047 | 1    | 0.2551        | -               |
| 0.2358 | 50   | 0.2056        | -               |
| 0.4717 | 100  | 0.0522        | -               |
| 0.7075 | 150  | 0.0206        | -               |
| 0.9434 | 200  | 0.0154        | -               |

### Framework Versions
- Python: 3.12.12
- SetFit: 1.1.3
- Sentence Transformers: 5.1.2
- Transformers: 4.57.1
- PyTorch: 2.8.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.22.1

## 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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