README.md

---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: für Integration
- text: Zugang zu Integrationsmaßnahmen sicherstellen;
- text: Wir sehen in der natürlichen Zwei- oder Mehrsprachigkeit ein wichtiges Potenzial,
    das durch eine gezielte sprachliche Förderung realisiert werden kann.
- text: Deutschland braucht ein umfassendes Integrationskonzept auf allen Ebenen -
    der Kommune, des Landes und des Bundes.
- text: Eine offene Gesellschaft bietet im Rahmen der Grundrechte allen Religionen
    den Freiraum zur Entfaltung ihres Glaubens.
metrics:
- f1
- precision
- recall
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: deutsche-telekom/gbert-large-paraphrase-cosine
model-index:
- name: SetFit with deutsche-telekom/gbert-large-paraphrase-cosine
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: f1
      value: 0.8563995837669095
      name: F1
    - type: precision
      value: 0.858476507713885
      name: Precision
    - type: recall
      value: 0.8548387096774194
      name: Recall
---

# SetFit with deutsche-telekom/gbert-large-paraphrase-cosine

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [deutsche-telekom/gbert-large-paraphrase-cosine](https://huggingface.co/deutsche-telekom/gbert-large-paraphrase-cosine) 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:** [deutsche-telekom/gbert-large-paraphrase-cosine](https://huggingface.co/deutsche-telekom/gbert-large-paraphrase-cosine)
- **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:** 2 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                                                                                                                                                                                                                                                                                                                                            |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1     | <ul><li>'Deutschland ist ein gastfreundliches und weltoffenes Land.'</li><li>'Aber auch in der Polizei und Justiz muss sich einiges ändern.'</li><li>'Die FDP sucht das Gespräch mit der evangelischen und katholischen Kirche ebenso wie mit dem Judentum, dem Islam und allen anderen Religionsgemeinschaften.'</li></ul>                         |
| 0     | <ul><li>'Wir wollen eine Integrationsoffensive.'</li><li>'Kenntnisse der deutschen Sprache sind eine unverzichtbare Voraussetzung zur Beseitigung sozialer Benachteiligungen und zum Erreichen schulischer, beruflicher und gesellschaftlicher Erfolge.'</li><li>'Wir erwarten von Zuwandernden, dass sie die deutsche Sprache erlernen.'</li></ul> |

## Evaluation

### Metrics
| Label   | F1     | Precision | Recall |
|:--------|:-------|:----------|:-------|
| **all** | 0.8564 | 0.8585    | 0.8548 |

## 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("gehaustein/gbert-large-stance-multiculturalism")
# Run inference
preds = model("für Integration")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 1   | 14.6336 | 42  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 128                   |
| 1     | 366                   |

### Training Hyperparameters
- batch_size: (128, 128)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (1e-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 |
|:----------:|:-------:|:-------------:|:---------------:|
| 0.0008     | 1       | 0.3283        | -               |
| 0.0424     | 50      | 0.2401        | 0.234           |
| 0.0848     | 100     | 0.0852        | 0.202           |
| 0.1272     | 150     | 0.0054        | 0.2493          |
| 0.1696     | 200     | 0.001         | 0.2502          |
| 0.2120     | 250     | 0.0002        | 0.2513          |
| 0.2545     | 300     | 0.0012        | 0.2496          |
| 0.2969     | 350     | 0.0046        | 0.2485          |
| 0.3393     | 400     | 0.0056        | 0.2538          |
| 0.3817     | 450     | 0.0001        | 0.2543          |
| **0.4241** | **500** | **0.0001**    | **0.2443**      |
| 0.4665     | 550     | 0.0001        | 0.2472          |
| 0.5089     | 600     | 0.0051        | 0.2655          |
| 0.5513     | 650     | 0.0002        | 0.2646          |

* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.11.11
- SetFit: 1.1.0.dev0
- Sentence Transformers: 3.3.1
- Transformers: 4.48.1
- PyTorch: 2.5.1+cu121
- Datasets: 2.14.4
- Tokenizers: 0.21.0

## 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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