README.md

---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 'Trump stated he wanted to stockpile 1% of all BTC. '
- text: about what nigga
- text: hehe panicked yield chaser exiting
- text: '"larpas" is liquidating all his millions of YES Trump and NO Harris. He is
    still 1.2 million USD to go.'
- text: Lol prove it
metrics:
- name: Macro F1
  type: f1_macro
  value: 0.8245
- name: Accuracy
  type: accuracy
  value: 0.8376
- name: F1 NOISE
  type: f1_noise
  value: 0.8725
- name: Precision NOISE
  type: precision_noise
  value: 0.9028
- name: Recall NOISE
  type: recall_noise
  value: 0.8442
- name: F1 CONTRIBUTING
  type: f1_contributing
  value: 0.7765
- name: Precision CONTRIBUTING
  type: precision_contributing
  value: 0.7333
- name: Recall CONTRIBUTING
  type: recall_contributing
  value: 0.825
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/all-mpnet-base-v2
---

# SetFit with sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 384 tokens
- **Number of Classes:** 2 classes
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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>'maybe coffeezilla was right that Polymarket is just a tool for insiders to make money....'</li><li>'Coping yes kids in chat '</li><li>'The most common with ABOP'</li></ul>                                                                                                                                         |
| 1     | <ul><li>"Just dispute the market even though rules always mislead sometimes others can't understand English."</li><li>'I sold some of this'</li><li>"Yes you're missing something, if the GOP wins all swing states they win by between 65-104. They have to also win virginia or minnesota to get more than that"</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("gehaustein/polymarket-comments-binary")
# Run inference
preds = model("Lol prove it")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 1   | 16.6287 | 117 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 307                   |
| 1     | 307                   |

### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- 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: True

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0003 | 1    | 0.4909        | -               |
| 0.0163 | 50   | 0.3797        | 0.3548          |
| 0.0326 | 100  | 0.2726        | 0.2727          |
| 0.0489 | 150  | 0.2527        | 0.2652          |
| 0.0651 | 200  | 0.2342        | 0.2476          |
| 0.0814 | 250  | 0.1839        | 0.2088          |
| 0.0977 | 300  | 0.0915        | 0.2271          |
| 0.1140 | 350  | 0.0417        | 0.2716          |
| 0.1303 | 400  | 0.0197        | 0.3131          |

### Framework Versions
- Python: 3.12.13
- SetFit: 1.1.3
- Sentence Transformers: 5.3.0
- Transformers: 4.49.0
- PyTorch: 2.10.0+cu128
- Datasets: 4.0.0
- Tokenizers: 0.21.4

## 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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