
# 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>'“They know this is one of the great scandals in the history of our country because basically what they did is, they used [former Trump campaign aide] Carter Page, who nobody even knew, who I feel very badly for, I think he’s been treated very badly.\n'</li><li>'The Guardian did not make a mistake in vilifying Assange without a shred of evidence.\n'</li><li>'He himself said: “No one defends Islam like Arab Christians.” It is to defend Islam that Western clerics do not raise their voice against such acts of brutality.\n'</li></ul>                                                                                                                                                         |
| 1     | <ul><li>'As the political scientist Richard Neustadt said, political elites are constantly evaluating and re-evaluating the president.\n'</li><li>'“I can tell you 100% this is not that kind of guy,” said Rick, adding that he would see Paddock every other day and that the two would go to a local bar and play slot machines.\n'</li><li>'Now, new information released by investigative reporter Laura Loomer proves that authorities have directly lied to the American people about the case at least once by claiming that supposed shooter Stephen Paddock checked into the Mandalay Bay Hotel on September 28th when valet records (with photos) prove he actually arrived three days earlier.\n'</li></ul> |

## Evaluation

### Metrics
| Label   | F1     |
|:--------|:-------|
| **all** | 0.2237 |

## 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("anismahmahi/appeal-to-authority-setfit-model")
# Run inference
preds = model("Ganesh makes wild leaps and inferences.
")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 2   | 28.8867 | 111 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 452                   |
| 1     | 113                   |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True

### Training Results
| Epoch   | Step     | Training Loss | Validation Loss |
|:-------:|:--------:|:-------------:|:---------------:|
| 0.0007  | 1        | 0.3148        | -               |
| 0.0354  | 50       | 0.2792        | -               |
| 0.0708  | 100      | 0.1707        | -               |
| 0.1062  | 150      | 0.1197        | -               |
| 0.1415  | 200      | 0.0768        | -               |
| 0.1769  | 250      | 0.0406        | -               |
| 0.2123  | 300      | 0.0053        | -               |
| 0.2477  | 350      | 0.0571        | -               |
| 0.2831  | 400      | 0.0324        | -               |
| 0.3185  | 450      | 0.001         | -               |
| 0.3539  | 500      | 0.077         | -               |
| 0.3892  | 550      | 0.0002        | -               |
| 0.4246  | 600      | 0.0011        | -               |
| 0.4600  | 650      | 0.003         | -               |
| 0.4954  | 700      | 0.0004        | -               |
| 0.5308  | 750      | 0.0004        | -               |
| 0.5662  | 800      | 0.0006        | -               |
| 0.6016  | 850      | 0.0002        | -               |
| 0.6369  | 900      | 0.0002        | -               |
| 0.6723  | 950      | 0.0003        | -               |
| 0.7077  | 1000     | 0.0116        | -               |
| 0.7431  | 1050     | 0.0059        | -               |
| 0.7785  | 1100     | 0.0002        | -               |
| 0.8139  | 1150     | 0.0001        | -               |
| 0.8493  | 1200     | 0.0001        | -               |
| 0.8846  | 1250     | 0.0003        | -               |
| 0.9200  | 1300     | 0.0001        | -               |
| 0.9554  | 1350     | 0.0           | -               |
| 0.9908  | 1400     | 0.0125        | -               |
| **1.0** | **1413** | **-**         | **0.2868**      |
| 1.0262  | 1450     | 0.0003        | -               |
| 1.0616  | 1500     | 0.0002        | -               |
| 1.0970  | 1550     | 0.0001        | -               |
| 1.1323  | 1600     | 0.0002        | -               |
| 1.1677  | 1650     | 0.0001        | -               |
| 1.2031  | 1700     | 0.0001        | -               |
| 1.2385  | 1750     | 0.0038        | -               |
| 1.2739  | 1800     | 0.0001        | -               |
| 1.3093  | 1850     | 0.0065        | -               |
| 1.3447  | 1900     | 0.0002        | -               |
| 1.3800  | 1950     | 0.0002        | -               |
| 1.4154  | 2000     | 0.0197        | -               |
| 1.4508  | 2050     | 0.0061        | -               |
| 1.4862  | 2100     | 0.0001        | -               |
| 1.5216  | 2150     | 0.0           | -               |
| 1.5570  | 2200     | 0.0321        | -               |
| 1.5924  | 2250     | 0.0002        | -               |
| 1.6277  | 2300     | 0.0331        | -               |
| 1.6631  | 2350     | 0.0069        | -               |
| 1.6985  | 2400     | 0.0001        | -               |
| 1.7339  | 2450     | 0.0           | -               |
| 1.7693  | 2500     | 0.0           | -               |
| 1.8047  | 2550     | 0.0337        | -               |
| 1.8401  | 2600     | 0.0347        | -               |
| 1.8754  | 2650     | 0.0612        | -               |
| 1.9108  | 2700     | 0.0398        | -               |
| 1.9462  | 2750     | 0.0001        | -               |
| 1.9816  | 2800     | 0.0001        | -               |
| 2.0     | 2826     | -             | 0.2926          |

* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.16.1
- Tokenizers: 0.15.0

## 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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{"library_name": "setfit", "tags": ["setfit", "sentence-transformers", "text-classification", "generated_from_setfit_trainer"], "metrics": ["f1"], "widget": [{"text": "Pointing out the glaring nature of the smear campaign was the fact that there has been absolutely zero information released about the warrants conducted on officer Amber Guyger, the killer cop who lived just below Jean.\n"}, {"text": "Ganesh makes wild leaps and inferences.\n"}, {"text": "But during his 2004 campaign for the Senate, Obama and his corrupt party in Chicago somehow managed to unseal the divorce records of his opponent Jack Ryan, who was leading by a large margin.\n"}, {"text": "Trump has only the \u201cdeplorables,\u201d and they are unorganized and will experience retribution once Trump is removed.\n"}, {"text": "\u201cAl Franken must be held accountable if our party wants to live up to our commitment to women & girls.\u201d\n"}], "pipeline_tag": "text-classification", "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": "f1", "value": 0.2236842105263158, "name": "F1"}]}]}]}
setfit safetensors mpnet sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:sentence-transformers/paraphrase-mpnet-base-v2 model-index region:us