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 model-index 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: 'It was a jihad training camp.

    '
- text: 'Batten echoed that sentiment saying, “Tommy Robinson is a political prisoner."

    '
- text: 'Failing to answer, Ellison tried to move from person to person, allowing
    his minions to try and provide cover for him, similar to that of Maxine Waters,
    but there was no "member''s only" elevator to flee into.

    '
- text: 'More details about the horrid compound could be revealed Wednesday when the
    five adults arrested from the site make their first court appearances.

    '
- text: 'Black Death Warning: The Plague Is Impossible To Eradicate

    '
inference: false
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: 0.5849056603773585
      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 OneVsRestClassifier 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 OneVsRestClassifier instance
- **Maximum Sequence Length:** 512 tokens
<!-- - **Number of Classes:** Unknown -->
<!-- - **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)

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.5849   |

## 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/G2-multilabel-setfit-model")
# Run inference
preds = model("It was a jihad training camp.
")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 1   | 26.6518 | 129 |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 10
- 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 |
|:-------:|:--------:|:-------------:|:---------------:|
| 0.0006  | 1        | 0.3905        | -               |
| 0.0275  | 50       | 0.2239        | -               |
| 0.0550  | 100      | 0.2359        | -               |
| 0.0826  | 150      | 0.2443        | -               |
| 0.1101  | 200      | 0.2495        | -               |
| 0.1376  | 250      | 0.2498        | -               |
| 0.1651  | 300      | 0.116         | -               |
| 0.1926  | 350      | 0.1672        | -               |
| 0.2201  | 400      | 0.1281        | -               |
| 0.2477  | 450      | 0.139         | -               |
| 0.2752  | 500      | 0.0615        | -               |
| 0.3027  | 550      | 0.0972        | -               |
| 0.3302  | 600      | 0.0851        | -               |
| 0.3577  | 650      | 0.1769        | -               |
| 0.3853  | 700      | 0.1673        | -               |
| 0.4128  | 750      | 0.0615        | -               |
| 0.4403  | 800      | 0.1232        | -               |
| 0.4678  | 850      | 0.0094        | -               |
| 0.4953  | 900      | 0.0135        | -               |
| 0.5228  | 950      | 0.0107        | -               |
| 0.5504  | 1000     | 0.1137        | -               |
| 0.5779  | 1050     | 0.0173        | -               |
| 0.6054  | 1100     | 0.0573        | -               |
| 0.6329  | 1150     | 0.0115        | -               |
| 0.6604  | 1200     | 0.0374        | -               |
| 0.6879  | 1250     | 0.0231        | -               |
| 0.7155  | 1300     | 0.0392        | -               |
| 0.7430  | 1350     | 0.0754        | -               |
| 0.7705  | 1400     | 0.007         | -               |
| 0.7980  | 1450     | 0.0138        | -               |
| 0.8255  | 1500     | 0.0569        | -               |
| 0.8531  | 1550     | 0.0971        | -               |
| 0.8806  | 1600     | 0.1052        | -               |
| 0.9081  | 1650     | 0.0084        | -               |
| 0.9356  | 1700     | 0.0859        | -               |
| 0.9631  | 1750     | 0.0081        | -               |
| 0.9906  | 1800     | 0.0362        | -               |
| 1.0     | 1817     | -             | 0.2354          |
| 1.0182  | 1850     | 0.0429        | -               |
| 1.0457  | 1900     | 0.056         | -               |
| 1.0732  | 1950     | 0.0098        | -               |
| 1.1007  | 2000     | 0.002         | -               |
| 1.1282  | 2050     | 0.0892        | -               |
| 1.1558  | 2100     | 0.0557        | -               |
| 1.1833  | 2150     | 0.001         | -               |
| 1.2108  | 2200     | 0.0125        | -               |
| 1.2383  | 2250     | 0.0152        | -               |
| 1.2658  | 2300     | 0.0202        | -               |
| 1.2933  | 2350     | 0.0593        | -               |
| 1.3209  | 2400     | 0.007         | -               |
| 1.3484  | 2450     | 0.014         | -               |
| 1.3759  | 2500     | 0.003         | -               |
| 1.4034  | 2550     | 0.0012        | -               |
| 1.4309  | 2600     | 0.0139        | -               |
| 1.4584  | 2650     | 0.0149        | -               |
| 1.4860  | 2700     | 0.002         | -               |
| 1.5135  | 2750     | 0.009         | -               |
| 1.5410  | 2800     | 0.0066        | -               |
| 1.5685  | 2850     | 0.0173        | -               |
| 1.5960  | 2900     | 0.0052        | -               |
| 1.6236  | 2950     | 0.0039        | -               |
| 1.6511  | 3000     | 0.0042        | -               |
| 1.6786  | 3050     | 0.0339        | -               |
| 1.7061  | 3100     | 0.001         | -               |
| 1.7336  | 3150     | 0.0005        | -               |
| 1.7611  | 3200     | 0.0049        | -               |
| 1.7887  | 3250     | 0.01          | -               |
| 1.8162  | 3300     | 0.0815        | -               |
| 1.8437  | 3350     | 0.0227        | -               |
| 1.8712  | 3400     | 0.005         | -               |
| 1.8987  | 3450     | 0.0053        | -               |
| 1.9263  | 3500     | 0.0152        | -               |
| 1.9538  | 3550     | 0.0155        | -               |
| 1.9813  | 3600     | 0.0182        | -               |
| **2.0** | **3634** | **-**         | **0.2266**      |

* 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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