---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: Buses are more simple - you just buy a ticket .
- text: As citizens of village , we totally care about environment of our village
    .
- text: So , finally I suggest that it would be a great idea to combine the different
    types of activities , both popular and the newest .
- text: Had 12 years old .
- text: On the other hand , I have the theoretical knowledge to use new the technologies
    this great project requires .
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: accuracy
      value: 0.13152173913043477
      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 [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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 [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 8 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 7     | <ul><li>"When I 've had a very bad and stressful day I can relax doing karate , because It 's the kind of sport that it is n't very hard ."</li><li>"Also , you 'll meet friendly people who usually ask to you something to be friends and change your telephone number ."</li><li>'When I have spare time , I often gather my friends to watch basketball match on television .'</li></ul>                                                                                                                                                                                            |
| 4     | <ul><li>"stop shouting . do n't shout ."</li><li>'Yours Sincerely .'</li><li>'Something that they don know was that the whole thing was a movie !'</li></ul>                                                                                                                                                                                                                                                                                                                                                                                                                            |
| 1     | <ul><li>'She stay sleeping in the bed and doing nothing all day .'</li><li>'People collects trash of their house and await the trash truck that carried the trash to a landfill located outside the village .'</li><li>"Travelling by car is n't so much more convenient unless it is so much more comfortable , but actually we do n't think about the contamination in our planet ."</li></ul>                                                                                                                                                                                        |
| 6     | <ul><li>'When the concert finished , we went to cloakroom to get signatures from musicians .'</li><li>'We have solar panels and a place to make compost at the last garden , with worms who eat and degrade all the organic waste of the school .'</li><li>'The aim of this report is to give you my personal point of view of the course I did in your branch in Madrid last month .'</li></ul>                                                                                                                                                                                        |
| 5     | <ul><li>'You can also bought a lot of gifts like key chains , statue , or what else memories to be made before returning to Malaysia .'</li><li>'I always said that I passed that test and I was sure of that .'</li><li>'In addition , to decrease the risk of negative comments or posts , Facebook and Twitter would improve their futures to solve the less personal privacy problem .'</li></ul>                                                                                                                                                                                   |
| 2     | <ul><li>'They were not only really clever people but also excellent co - workers .'</li><li>'On balance , learning foreign languages is very positive on different aspect , so if you have the positivity of learning a new language do it , because it will bring you many benefits .'</li><li>'In many years of work I have honed my skills in managing non - standard situations , analyzing the problem , finding and implementing practical and easy solutions .'</li></ul>                                                                                                        |
| 0     | <ul><li>'It is very funny .'</li><li>'In China , English is took to be a foreign language which many students choose to learn .'</li><li>'We also value that they have specialised studies in Cloud technology , and hosting management .'</li></ul>                                                                                                                                                                                                                                                                                                                                    |
| 3     | <ul><li>"Usually there are generation problems , sons do n't understand parents and vicecersa , but dialoging and listening emotions and facts , everyone can have another point of view ."</li><li>'the two boys heard that he was planing to steal some money and kill people so the boys start their adventure on stoping Injuin Joe ...'</li><li>'As an example , if you are able to get alone with your travel companion could enjoy each moment of the trip , exchange some pictures , eat together , and visit places with common interest such as museums or malls .'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.1315   |

## 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("HelgeKn/BEA2019-multi-class-4")
# Run inference
preds = model("Had 12 years old .")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 3   | 19.1562 | 42  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 4                     |
| 1     | 4                     |
| 2     | 4                     |
| 3     | 4                     |
| 4     | 4                     |
| 5     | 4                     |
| 6     | 4                     |
| 7     | 4                     |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0125 | 1    | 0.1886        | -               |
| 0.625  | 50   | 0.0778        | -               |
| 1.25   | 100  | 0.0194        | -               |
| 1.875  | 150  | 0.0069        | -               |

### Framework Versions
- Python: 3.9.13
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.36.0
- PyTorch: 2.1.1+cpu
- Datasets: 2.15.0
- 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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