---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: when will be the matches of Liverpool
- text: quand liverpool jouera avec man city
- text: quand auront lieu les matchs de Aston Villa
- text: why do I need you
- text: who won in Norwich City vs West Ham United match
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 1.0
      name: Accuracy
---

# SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-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-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-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-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 128 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                                                                                                                                                           |
|:------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| matches-match_time      | <ul><li>'Norwich City vs Newcastle United'</li><li>'will Manchester United play with chelsea'</li><li>'est-ce que Manchester United jouera avec chelsea'</li></ul> |
| matches-match_result    | <ul><li>'Liverpool and West Ham result'</li><li>'what is the score of Wolverhampton match'</li><li>'who won in Liverpool vs Newcastle United match'</li></ul>      |
| greet-who_are_you       | <ul><li>'how can you help me'</li><li>"pourquoi j'ai besoin de toi"</li><li>'je ne te comprends pas'</li></ul>                                                     |
| matches-team_next_match | <ul><li>'Real Madrid fixtures'</li><li>'quels sont les prochains matchs de Borussia Dortmund'</li><li>'próximos partidos de Atletico Madrid'</li></ul>             |
| greet-good_bye          | <ul><li>'See you later'</li><li>'A plus tard'</li><li>'stop'</li></ul>                                                                                             |
| greet-hi                | <ul><li>'Hello buddy'</li><li>'Salut'</li><li>'Hey'</li></ul>                                                                                                      |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 1.0      |

## 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("Ah7med/setfit-football_bootpress_paraph-multi-v2")
# Run inference
preds = model("why do I need you")
```

<!--
### Downstream Use

*List how someone could finetune this model on their own dataset.*
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 1   | 5.2    | 10  |

| Label                   | Training Sample Count |
|:------------------------|:----------------------|
| greet-hi                | 5                     |
| greet-who_are_you       | 7                     |
| greet-good_bye          | 5                     |
| matches-team_next_match | 21                    |
| matches-match_time      | 12                    |
| matches-match_result    | 15                    |

### Training Hyperparameters
- batch_size: (4, 4)
- num_epochs: (4, 4)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0012 | 1    | 0.1308        | -               |
| 0.0603 | 50   | 0.1596        | -               |
| 0.1206 | 100  | 0.1399        | -               |
| 0.1809 | 150  | 0.089         | -               |
| 0.2413 | 200  | 0.0461        | -               |
| 0.3016 | 250  | 0.026         | -               |
| 0.3619 | 300  | 0.0081        | -               |
| 0.4222 | 350  | 0.0048        | -               |
| 0.4825 | 400  | 0.0039        | -               |
| 0.5428 | 450  | 0.0018        | -               |
| 0.6031 | 500  | 0.002         | -               |
| 0.6634 | 550  | 0.0015        | -               |
| 0.7238 | 600  | 0.0011        | -               |
| 0.7841 | 650  | 0.0009        | -               |
| 0.8444 | 700  | 0.0008        | -               |
| 0.9047 | 750  | 0.0005        | -               |
| 0.9650 | 800  | 0.0007        | -               |
| 1.0    | 829  | -             | 0.0211          |
| 1.0253 | 850  | 0.0006        | -               |
| 1.0856 | 900  | 0.0005        | -               |
| 1.1460 | 950  | 0.0005        | -               |
| 1.2063 | 1000 | 0.0003        | -               |
| 1.2666 | 1050 | 0.0003        | -               |
| 1.3269 | 1100 | 0.0004        | -               |
| 1.3872 | 1150 | 0.0003        | -               |
| 1.4475 | 1200 | 0.0004        | -               |
| 1.5078 | 1250 | 0.0002        | -               |
| 1.5682 | 1300 | 0.0003        | -               |
| 1.6285 | 1350 | 0.0003        | -               |
| 1.6888 | 1400 | 0.0003        | -               |
| 1.7491 | 1450 | 0.0003        | -               |
| 1.8094 | 1500 | 0.0003        | -               |
| 1.8697 | 1550 | 0.0003        | -               |
| 1.9300 | 1600 | 0.0002        | -               |
| 1.9903 | 1650 | 0.0002        | -               |
| 2.0    | 1658 | -             | 0.0190          |
| 2.0507 | 1700 | 0.0003        | -               |
| 2.1110 | 1750 | 0.0002        | -               |
| 2.1713 | 1800 | 0.0002        | -               |
| 2.2316 | 1850 | 0.0002        | -               |
| 2.2919 | 1900 | 0.0002        | -               |
| 2.3522 | 1950 | 0.0002        | -               |
| 2.4125 | 2000 | 0.0002        | -               |
| 2.4729 | 2050 | 0.0002        | -               |
| 2.5332 | 2100 | 0.0002        | -               |
| 2.5935 | 2150 | 0.0002        | -               |
| 2.6538 | 2200 | 0.0001        | -               |
| 2.7141 | 2250 | 0.0002        | -               |
| 2.7744 | 2300 | 0.0001        | -               |
| 2.8347 | 2350 | 0.0002        | -               |
| 2.8951 | 2400 | 0.0001        | -               |
| 2.9554 | 2450 | 0.0002        | -               |
| 3.0    | 2487 | -             | 0.0181          |
| 3.0157 | 2500 | 0.0002        | -               |
| 3.0760 | 2550 | 0.0001        | -               |
| 3.1363 | 2600 | 0.0001        | -               |
| 3.1966 | 2650 | 0.0001        | -               |
| 3.2569 | 2700 | 0.0001        | -               |
| 3.3172 | 2750 | 0.0001        | -               |
| 3.3776 | 2800 | 0.0001        | -               |
| 3.4379 | 2850 | 0.0001        | -               |
| 3.4982 | 2900 | 0.0001        | -               |
| 3.5585 | 2950 | 0.0001        | -               |
| 3.6188 | 3000 | 0.0001        | -               |
| 3.6791 | 3050 | 0.0001        | -               |
| 3.7394 | 3100 | 0.0001        | -               |
| 3.7998 | 3150 | 0.0001        | -               |
| 3.8601 | 3200 | 0.0001        | -               |
| 3.9204 | 3250 | 0.0001        | -               |
| 3.9807 | 3300 | 0.0001        | -               |
| 4.0    | 3316 | -             | 0.0176          |

### Framework Versions
- Python: 3.11.12
- SetFit: 1.1.2
- Sentence Transformers: 3.4.1
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Datasets: 3.5.1
- Tokenizers: 0.21.1

## 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->