README.md

---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: intfloat/multilingual-e5-small
metrics:
- accuracy
widget:
- text: 'query: Sí, la próxima vez que vayas, cuenta conmigo. He querido salir y hacer
    más actividades en la naturaleza.'
- text: 'query: I''m man, I''m leaving now.'
- text: 'query: Ja, forse possiamo fare un giro in bicicletta insieme.'
- text: 'query: Mak saya suruh balik, jumpa lagi.'
- text: 'query: İnanılmaz, bu harika! Bir ayı gördüğüne inanamıyorum!'
pipeline_tag: text-classification
inference: true
---

# SetFit with intfloat/multilingual-e5-small

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) 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:** [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small)
- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
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### 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>'query: Tja, måste dra nu, ses senare.'</li><li>'query: Ispričavam se, moram sada otići.'</li><li>'query: Przepraszam, muszę już iść.'</li></ul>                                                               |
| 0     | <ul><li>'query: Sveiki, kā jums klājas?'</li><li>'query: அதிர்ச்சிகரமானது, அது மிகவும் அருமையாக இருக்கிறது! நீ கரடியை பார்த்தது எனக்கு நம்பிக்கையே வரவில்லை!'</li><li>'query: Ég hef það fínt, takk. Og þú?'</li></ul> |

## 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("setfit_model_id")
# Run inference
preds = model("query: I'm man, I'm leaving now.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 2   | 7.6965 | 31  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 902                   |
| 1     | 910                   |

### Training Hyperparameters
- batch_size: (16, 2)
- num_epochs: (1, 16)
- max_steps: -1
- sampling_strategy: undersampling
- body_learning_rate: (1e-05, 1e-05)
- head_learning_rate: 0.001
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.1
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- run_name: multilingual-e5-small
- eval_max_steps: -1
- load_best_model_at_end: True

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0000 | 1    | 0.3613        | -               |
| 0.0005 | 50   | 0.3577        | -               |
| 0.0010 | 100  | 0.3511        | 0.3413          |
| 0.0015 | 150  | 0.3372        | -               |
| 0.0019 | 200  | 0.3447        | 0.3347          |
| 0.0024 | 250  | 0.3349        | -               |
| 0.0029 | 300  | 0.3326        | 0.3224          |
| 0.0034 | 350  | 0.3372        | -               |
| 0.0039 | 400  | 0.3185        | 0.3039          |
| 0.0044 | 450  | 0.2828        | -               |
| 0.0049 | 500  | 0.3055        | 0.2774          |
| 0.0054 | 550  | 0.2594        | -               |
| 0.0058 | 600  | 0.2779        | 0.2489          |
| 0.0063 | 650  | 0.2486        | -               |
| 0.0068 | 700  | 0.2321        | 0.22            |
| 0.0073 | 750  | 0.1838        | -               |
| 0.0078 | 800  | 0.1845        | 0.2075          |
| 0.0083 | 850  | 0.1899        | -               |
| 0.0088 | 900  | 0.2147        | 0.2025          |
| 0.0093 | 950  | 0.1644        | -               |
| 0.0097 | 1000 | 0.2019        | 0.1821          |
| 0.0102 | 1050 | 0.2309        | -               |
| 0.0107 | 1100 | 0.2084        | 0.1784          |
| 0.0112 | 1150 | 0.1508        | -               |
| 0.0117 | 1200 | 0.1064        | 0.1453          |
| 0.0122 | 1250 | 0.1376        | -               |
| 0.0127 | 1300 | 0.0828        | 0.121           |
| 0.0132 | 1350 | 0.1628        | -               |
| 0.0136 | 1400 | 0.1308        | 0.1018          |
| 0.0141 | 1450 | 0.0566        | -               |
| 0.0146 | 1500 | 0.0953        | 0.0767          |
| 0.0151 | 1550 | 0.1607        | -               |
| 0.0156 | 1600 | 0.1322        | 0.0625          |
| 0.0161 | 1650 | 0.0861        | -               |
| 0.0166 | 1700 | 0.0926        | 0.0423          |
| 0.0171 | 1750 | 0.0338        | -               |
| 0.0175 | 1800 | 0.1029        | 0.0344          |
| 0.0180 | 1850 | 0.0442        | -               |
| 0.0185 | 1900 | 0.019         | 0.0256          |
| 0.0190 | 1950 | 0.0489        | -               |
| 0.0195 | 2000 | 0.0675        | 0.0187          |

### Framework Versions
- Python: 3.10.11
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.39.0
- PyTorch: 2.4.0
- Datasets: 2.20.0
- Tokenizers: 0.15.2

## 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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