---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: sentence-transformers/paraphrase-mpnet-base-v2
metrics:
- accuracy
widget:
- text: Anmeldung über ILIAS. https://ilias3.uni-stuttgart.de/goto_Uni_Stuttgart_crs_1439207.html
    Anmeldung zu den Übungsgruppen wird am Mittwoch um 18:00 freigeschaltet.
- text: 60-18-2110-pf Machine Learning in Information and Communication Technology
    (ICT) (18-kp-2110)
- text: 'Programmierpraktikum: Datenmanagement und Web-basierte Anwendungssysteme
    (Praktikum) [Presence]'
- text: 'Proseminar: Mathematischer Programmierkurs'
- text: How to... Neuer Master Informatik und Softwaretechnik
pipeline_tag: text-classification
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: 0.8828125
      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 [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:** 6 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 3     | <ul><li>'see module description IN0006'</li><li>'Anleitung zu Masterarbeiten: Lehrstuhl für Softwaretechnik'</li><li>'Übung: 2.01.005g Softwaretechnik I (Ü) - ONLINE'</li></ul>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
| 6     | <ul><li>'Technological trends such as social media, cloud computing or big data have changed the way of how enterprises operate.\nAlthough companies put a lot of effort integrating these technological trends into their daily business, one challenge\nremains to show that the integration is worth. The idea of this seminar is to develop paper prototypes, which demonstrate \ninnovative solutions or products that result from an integration of technological trends with digital products \n(such as e.g., Google glass, Google watch). \nParticularly, recommender systems should be used as a means of demonstrating the customers the technological benefits. \nThe paper prototypes will be develop in teams.'</li><li>'Softwareentwicklung im Team (Programmierpraktikum II)'</li><li>'Softwaretechnik und Programmierparadigmen (SWTPP) (Klausur) [Zweittermin, 03.04.2023]'</li></ul>                                                  |
| 1     | <ul><li>'Die Veranstaltung findet im PC-Pool "Am Windkanal" statt.\xa0Beschreibung  Das Seminar soll eine Einführung in zwei prominente Modelle der kognitiv orientierten komputationalen Neurowissenschaften geben. Der Kurs hat das Ziel theoretische Grundlagen zu vermitteln und praktische Fertigkeiten zu üben. Zu den beiden Themenkomplexen wird es jeweils Aufgaben geben, in denen die konkrete Umsetzung der Modelle realisiert werden soll. Programmierkentnisse (MATLAB) sind von Vorteil, allerdings nicht zwingend notwendig. Das Seminar richtet sich an Studierende der Psychologie und verwandter Disziplinen, die ein tiefergehendes Interesse an Informationsverarbeitung und –aufbewahrung im Gehirn haben.'</li><li>'03-03-2104-ue Computerbasierte Datenanalyse'</li><li>'1. Statistics of data sets\n2. Graphical representation of data sets\n3. Statistics of pairs of data sets\n4. Simulation of random variables'</li></ul> |
| 2     | <ul><li>'Entwicklung/Demonstration eines autonomen unbemannten Fluggerätes'</li><li>'Lernziele: <br /> ?<br /> <br /> Stoffplan: <br /> ?<br /> <br /> Diploma Supplement: <br /> development of functions for autonomous robotic systems, experimental evaluation, team work, presentation<br /> <br />'</li><li>'Modellierung, Analyse und Entwurf neuer Roboterkinematiken II'</li></ul>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
| 5     | <ul><li>'Quantum Electronics II - Spintronics and Quantum Computation (QE II) - Lecture'</li><li>'Spintronics and Quantum Computation - Lecture'</li><li>'Spintronics and Quantum Computation - Exercises'</li></ul>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
| 4     | <ul><li>'...was macht User Interfaces \x84gut\x93 und \x84bedienbar\x93? ......wie können wir sie besser und bedienbarer machen? ......warum ist Touch intuitiver als Tastatur und Maus? ......was macht das iPhone besser als seine Vorgänger? ...... wie interagieren wir mit Computern in der Zukunft? ...... ??? Computer Interfaces verändern sich zur Zeit schneller als je zuvor. Human-Computer Interaction (HCI) beschäftigt sich mit benutzergerechten Gestaltung von Nutzerschnittstellen. Anhand von zahlreichen Beispielen moderner Nutzerschnittstellen wird in dieser Vorlesung eine umfassende Einführung in das Thema HCI gegeben. Die Vorlesung stellt unter anderem vor, welche verschiedenen Typen von Nutzerschnittstellen es gibt, wohin es in der Zukunft geht, wie man "gute" Interfaces erstellt, was "gut" hier bedeutet und wie es gemessen werden kann. Inhalte:'</li></ul>                                                  |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.8828   |

## 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("Chernoffface/fs-setfit-model")
# Run inference
preds = model("Proseminar: Mathematischer Programmierkurs")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 1   | 32.4872 | 368 |

| Label | Training Sample Count |
|:------|:----------------------|
| 1     | 69                    |
| 2     | 7                     |
| 3     | 200                   |
| 4     | 1                     |
| 5     | 9                     |
| 6     | 223                   |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 10
- 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0016 | 1    | 0.2519        | -               |
| 0.0785 | 50   | 0.2065        | -               |
| 0.1570 | 100  | 0.1206        | -               |
| 0.2355 | 150  | 0.062         | -               |
| 0.3140 | 200  | 0.0311        | -               |
| 0.3925 | 250  | 0.0124        | -               |
| 0.4710 | 300  | 0.0085        | -               |
| 0.5495 | 350  | 0.0022        | -               |
| 0.6279 | 400  | 0.0043        | -               |
| 0.7064 | 450  | 0.001         | -               |
| 0.7849 | 500  | 0.0012        | -               |
| 0.8634 | 550  | 0.0009        | -               |
| 0.9419 | 600  | 0.0012        | -               |

### Framework Versions
- Python: 3.12.3
- SetFit: 1.1.0
- Sentence Transformers: 3.0.0
- Transformers: 4.43.1
- PyTorch: 2.3.1+cu121
- Datasets: 2.20.0
- Tokenizers: 0.19.1

## 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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