README.md

---
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: false
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: 0.8041298489691044
      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** | 0.8041   |

## 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")
# Run inference
preds = model("\"   link   thanks for fixing that disambiguation link on usher's album ) flash; \"")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 3   | 69.1481 | 898 |

### Training Hyperparameters
- batch_size: (1, 1)
- num_epochs: (1, 1)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0005 | 1    | 0.2094        | -               |
| 0.0231 | 50   | 0.033         | -               |
| 0.0463 | 100  | 0.0439        | -               |
| 0.0694 | 150  | 0.001         | -               |
| 0.0926 | 200  | 0.0245        | -               |
| 0.1157 | 250  | 0.0008        | -               |
| 0.1389 | 300  | 0.0001        | -               |
| 0.1620 | 350  | 0.0           | -               |
| 0.1852 | 400  | 0.0012        | -               |
| 0.2083 | 450  | 0.0           | -               |
| 0.2315 | 500  | 0.0002        | -               |
| 0.2546 | 550  | 0.0006        | -               |
| 0.2778 | 600  | 0.002         | -               |
| 0.3009 | 650  | 0.0044        | -               |
| 0.3241 | 700  | 0.0015        | -               |
| 0.3472 | 750  | 0.0007        | -               |
| 0.3704 | 800  | 0.0001        | -               |
| 0.3935 | 850  | 0.0001        | -               |
| 0.4167 | 900  | 0.0001        | -               |
| 0.4398 | 950  | 0.0004        | -               |
| 0.4630 | 1000 | 0.0001        | -               |
| 0.4861 | 1050 | 0.0001        | -               |
| 0.5093 | 1100 | 0.0           | -               |
| 0.5324 | 1150 | 0.0052        | -               |
| 0.5556 | 1200 | 0.0002        | -               |
| 0.5787 | 1250 | 0.0           | -               |
| 0.6019 | 1300 | 0.0003        | -               |
| 0.625  | 1350 | 0.0           | -               |
| 0.6481 | 1400 | 0.0001        | -               |
| 0.6713 | 1450 | 0.0           | -               |
| 0.6944 | 1500 | 0.0           | -               |
| 0.7176 | 1550 | 0.0           | -               |
| 0.7407 | 1600 | 0.0002        | -               |
| 0.7639 | 1650 | 0.0001        | -               |
| 0.7870 | 1700 | 0.0011        | -               |
| 0.8102 | 1750 | 0.0001        | -               |
| 0.8333 | 1800 | 0.0           | -               |
| 0.8565 | 1850 | 0.0001        | -               |
| 0.8796 | 1900 | 0.0006        | -               |
| 0.9028 | 1950 | 0.0002        | -               |
| 0.9259 | 2000 | 0.0002        | -               |
| 0.9491 | 2050 | 0.0           | -               |
| 0.9722 | 2100 | 0.0001        | -               |
| 0.9954 | 2150 | 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}
}
```

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