---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: intfloat/multilingual-e5-small
metrics:
- accuracy
widget:
- text: 'query: Baiklah, kita cakap lagi nanti, Mark. Selamat hari!'
- text: 'query: Tôi xin lỗi nhưng tôi phải đi'
- text: 'query: 次回行くときは、私を連れて行ってください。もっと自然の中で活動したいと思っています。'
- text: 'query: Entschuldigung, ich muss jetzt gehen.'
- text: 'query: Buenos días, ¿cómo están ustedes?'
pipeline_tag: text-classification
inference: true
model-index:
- name: SetFit with intfloat/multilingual-e5-small
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.9333333333333333
      name: Accuracy
---

# 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 -->
<!-- - **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                                                                                                                                                         |
|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0     | <ul><li>'query: Értem. Mit csinálunk most?'</li><li>'query: Ola Luca, que tal? Rematache o traballo?'</li><li>'query: Lijepo je. Hvala.'</li></ul>               |
| 1     | <ul><li>'query: Жөнейін, кейін кездесеміз.'</li><li>'query: Така, ќе се видиме повторно.'</li><li>'query: ठीक है बाद में बात करते हैं मार्क अच्छा दिन'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9333   |

## 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: Tôi xin lỗi nhưng tôi phải đi")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 2   | 7.2168 | 25  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 346                   |
| 1     | 346                   |

### Training Hyperparameters
- batch_size: (16, 2)
- num_epochs: (1, 16)
- max_steps: 2500
- sampling_strategy: undersampling
- body_learning_rate: (1e-06, 1e-06)
- head_learning_rate: 0.001
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- 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: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0002 | 1    | 0.3607        | -               |
| 0.0100 | 50   | 0.3634        | 0.3452          |
| 0.0200 | 100  | 0.3493        | 0.3377          |
| 0.0300 | 150  | 0.3244        | 0.3234          |
| 0.0400 | 200  | 0.3244        | 0.3034          |
| 0.0500 | 250  | 0.2931        | 0.2731          |
| 0.0600 | 300  | 0.2471        | 0.2398          |
| 0.0700 | 350  | 0.237         | 0.2168          |
| 0.0800 | 400  | 0.1964        | 0.2082          |
| 0.0900 | 450  | 0.2319        | 0.198           |
| 0.1000 | 500  | 0.2003        | 0.1968          |
| 0.1100 | 550  | 0.2014        | 0.1968          |
| 0.1200 | 600  | 0.1617        | 0.1879          |
| 0.1300 | 650  | 0.2214        | 0.1798          |
| 0.1400 | 700  | 0.2498        | 0.1768          |
| 0.1500 | 750  | 0.1527        | 0.1764          |
| 0.1600 | 800  | 0.1134        | 0.1733          |
| 0.1700 | 850  | 0.1393        | 0.1614          |
| 0.1800 | 900  | 0.1052        | 0.1549          |
| 0.1900 | 950  | 0.1772        | 0.149           |
| 0.2000 | 1000 | 0.1065        | 0.1504          |
| 0.2100 | 1050 | 0.087         | 0.1392          |
| 0.2200 | 1100 | 0.1416        | 0.1333          |
| 0.2300 | 1150 | 0.0767        | 0.1279          |
| 0.2400 | 1200 | 0.1228        | 0.1243          |
| 0.2500 | 1250 | 0.099         | 0.1128          |
| 0.2599 | 1300 | 0.1125        | 0.1106          |
| 0.2699 | 1350 | 0.1012        | 0.1156          |
| 0.2799 | 1400 | 0.0343        | 0.1022          |
| 0.2899 | 1450 | 0.0814        | 0.1012          |
| 0.2999 | 1500 | 0.0947        | 0.0965          |
| 0.3099 | 1550 | 0.0799        | 0.0964          |
| 0.3199 | 1600 | 0.113         | 0.0942          |
| 0.3299 | 1650 | 0.1125        | 0.0917          |
| 0.3399 | 1700 | 0.0507        | 0.0899          |
| 0.3499 | 1750 | 0.0986        | 0.0938          |
| 0.3599 | 1800 | 0.0885        | 0.0913          |
| 0.3699 | 1850 | 0.0712        | 0.0841          |
| 0.3799 | 1900 | 0.1131        | 0.0851          |
| 0.3899 | 1950 | 0.0701        | 0.0852          |
| 0.3999 | 2000 | 0.0805        | 0.0878          |
| 0.4099 | 2050 | 0.0375        | 0.0814          |
| 0.4199 | 2100 | 0.1236        | 0.0797          |
| 0.4299 | 2150 | 0.0532        | 0.0881          |
| 0.4399 | 2200 | 0.0265        | 0.0806          |
| 0.4499 | 2250 | 0.1268        | 0.0801          |
| 0.4599 | 2300 | 0.0557        | 0.0797          |
| 0.4699 | 2350 | 0.0956        | 0.0832          |
| 0.4799 | 2400 | 0.0671        | 0.081           |
| 0.4899 | 2450 | 0.1394        | 0.0794          |
| 0.4999 | 2500 | 0.1165        | 0.0798          |

### Framework Versions
- Python: 3.10.11
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.39.3
- 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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