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: 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 .
inference: true
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: 0.10108695652173913
      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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 6     | <ul><li>'In addition , she has no blithe memory in her childhood .'</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>'When the concert finished , we went to cloakroom to get signatures from musicians .'</li></ul>                                                                                                                                                                                                                                                        |
| 5     | <ul><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><li>'I asked myself many times what is the aim of our life ?'</li><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></ul>                                                                                                                                                                                          |
| 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>'I been twelve years practice volleyball and because of it I knew lot of people who help me to grow up in the sport and life .'</li><li>'When I have spare time , I often gather my friends to watch basketball match on television .'</li></ul>                                                                                                                                                                                    |
| 0     | <ul><li>"That 's why I order all of you to go there and feel the pleasure and have a try their own food ."</li><li>'No one can deny that the pollution issue is one of the utmost important thing which should be prevented .'</li><li>'It is very funny .'</li></ul>                                                                                                                                                                                                                                                                                                                   |
| 4     | <ul><li>'Something that they don know was that the whole thing was a movie !'</li><li>'On september 12th,2014 I went to New York city .'</li><li>"If we think about it the car is better because we do n't need to wait for them has we are waiting for the bus or underground but in another way car cust more money than the public transport ."</li></ul>                                                                                                                                                                                                                            |
| 3     | <ul><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><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>"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></ul> |
| 2     | <ul><li>'It is a job with a lot of interesting aspects ,'</li><li>'when you have played enough in this city , and you want to find a job here .'</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>                                                                                                                                                                                                                                               |
| 1     | <ul><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>'She stay sleeping in the bed and doing nothing all day .'</li><li>'He decided to give swimming up and started to taking care of Mel .'</li></ul>                                                                                                                                                                                                                                                                                   |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.1011   |

## 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-6")
# 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   | 20.5208 | 42  |

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

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0083 | 1    | 0.2774        | -               |
| 0.4167 | 50   | 0.1729        | -               |
| 0.8333 | 100  | 0.0282        | -               |
| 1.25   | 150  | 0.0173        | -               |
| 1.6667 | 200  | 0.0049        | -               |

### 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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