---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: intfloat/e5-small-v2
metrics:
- accuracy
widget:
- text: 'query: 对的，关于上次讨论的项目，我有几个问题需要确认一下。'
- text: 'query: Sopii, nähdään silloin!'
- text: 'query: Hyvin menee, kiitos. Entä sinulla?'
- text: 'query: 好的，那就先这样，李先生，再见。'
- text: 'query: Jeg har det også godt. Skal vi mødes senere?'
pipeline_tag: text-classification
inference: true
---

# SetFit with intfloat/e5-small-v2

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [intfloat/e5-small-v2](https://huggingface.co/intfloat/e5-small-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:** [intfloat/e5-small-v2](https://huggingface.co/intfloat/e5-small-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:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/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                                                                                                                                                                                                                             |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0     | <ul><li>'query: Oi Pedro, você viu o novo filme que estreou semana passada?'</li><li>'query: Também gostei muito. Quem sabe podemos assistir juntos na próxima vez.'</li><li>'query: Jeg har det godt, tak. Hvad med dig?'</li></ul> |
| 1     | <ul><li>'query: Combinado! Vamos marcar um dia. Até mais!'</li><li>'query: Måske. Skal vi tale om det senere?'</li><li>'query: Absolument. On se voit ce soir pour fêter ça. À plus tard!'</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: 好的，那就先这样，李先生，再见。")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 2   | 6.2674 | 18  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 85                    |
| 1     | 87                    |

### Training Hyperparameters
- batch_size: (4, 1)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: undersampling
- body_learning_rate: (1e-06, 1e-06)
- head_learning_rate: 8e-06
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.05
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- run_name: intfloat/e5-small-v2
- eval_max_steps: -1
- load_best_model_at_end: True

### Training Results
| Epoch      | Step     | Training Loss | Validation Loss |
|:----------:|:--------:|:-------------:|:---------------:|
| 0.0003     | 1        | 0.3851        | -               |
| 0.0135     | 50       | 0.3455        | -               |
| 0.0270     | 100      | 0.3359        | 0.3522          |
| 0.0406     | 150      | 0.3459        | -               |
| 0.0541     | 200      | 0.3645        | 0.3221          |
| 0.0676     | 250      | 0.3264        | -               |
| 0.0811     | 300      | 0.2955        | 0.2759          |
| 0.0946     | 350      | 0.2546        | -               |
| 0.1082     | 400      | 0.2253        | 0.2373          |
| 0.1217     | 450      | 0.2004        | -               |
| 0.1352     | 500      | 0.3578        | 0.2318          |
| 0.1487     | 550      | 0.2628        | -               |
| 0.1622     | 600      | 0.2614        | 0.2222          |
| 0.1758     | 650      | 0.2095        | -               |
| 0.1893     | 700      | 0.2345        | 0.2196          |
| 0.2028     | 750      | 0.1842        | -               |
| 0.2163     | 800      | 0.1942        | 0.2326          |
| 0.2299     | 850      | 0.218         | -               |
| 0.2434     | 900      | 0.3134        | 0.2422          |
| 0.2569     | 950      | 0.1639        | -               |
| 0.2704     | 1000     | 0.2138        | 0.23            |
| 0.2839     | 1050     | 0.3102        | -               |
| 0.2975     | 1100     | 0.1347        | 0.2348          |
| 0.3110     | 1150     | 0.1698        | -               |
| 0.3245     | 1200     | 0.2467        | 0.2547          |
| 0.3380     | 1250     | 0.1064        | -               |
| 0.3515     | 1300     | 0.1757        | 0.2383          |
| 0.3651     | 1350     | 0.1093        | -               |
| 0.3786     | 1400     | 0.2869        | 0.2393          |
| 0.3921     | 1450     | 0.2519        | -               |
| 0.4056     | 1500     | 0.2344        | 0.2323          |
| 0.4191     | 1550     | 0.2804        | -               |
| 0.4327     | 1600     | 0.1082        | 0.2403          |
| 0.4462     | 1650     | 0.2025        | -               |
| 0.4597     | 1700     | 0.2213        | 0.2547          |
| 0.4732     | 1750     | 0.1302        | -               |
| 0.4867     | 1800     | 0.1517        | 0.2345          |
| 0.5003     | 1850     | 0.2779        | -               |
| 0.5138     | 1900     | 0.1918        | 0.2339          |
| 0.5273     | 1950     | 0.1132        | -               |
| 0.5408     | 2000     | 0.2075        | 0.253           |
| 0.5544     | 2050     | 0.2488        | -               |
| 0.5679     | 2100     | 0.0579        | 0.2526          |
| 0.5814     | 2150     | 0.3789        | -               |
| 0.5949     | 2200     | 0.167         | 0.2573          |
| 0.6084     | 2250     | 0.199         | -               |
| 0.6220     | 2300     | 0.0824        | 0.2258          |
| 0.6355     | 2350     | 0.1396        | -               |
| 0.6490     | 2400     | 0.3674        | 0.2527          |
| 0.6625     | 2450     | 0.2448        | -               |
| 0.6760     | 2500     | 0.1623        | 0.249           |
| 0.6896     | 2550     | 0.2198        | -               |
| 0.7031     | 2600     | 0.118         | 0.2613          |
| 0.7166     | 2650     | 0.1511        | -               |
| 0.7301     | 2700     | 0.1162        | 0.2351          |
| 0.7436     | 2750     | 0.1393        | -               |
| 0.7572     | 2800     | 0.1845        | 0.2418          |
| 0.7707     | 2850     | 0.1821        | -               |
| 0.7842     | 2900     | 0.1762        | 0.254           |
| 0.7977     | 2950     | 0.0477        | -               |
| 0.8112     | 3000     | 0.1928        | 0.2633          |
| 0.8248     | 3050     | 0.1363        | -               |
| 0.8383     | 3100     | 0.0811        | 0.261           |
| 0.8518     | 3150     | 0.0734        | -               |
| **0.8653** | **3200** | **0.0917**    | **0.2202**      |
| 0.8789     | 3250     | 0.3027        | -               |
| 0.8924     | 3300     | 0.1528        | 0.2767          |
| 0.9059     | 3350     | 0.2234        | -               |
| 0.9194     | 3400     | 0.1048        | 0.2667          |
| 0.9329     | 3450     | 0.1865        | -               |
| 0.9465     | 3500     | 0.051         | 0.2612          |
| 0.9600     | 3550     | 0.0218        | -               |
| 0.9735     | 3600     | 0.1524        | 0.243           |
| 0.9870     | 3650     | 0.1759        | -               |

* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.11
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.39.0
- PyTorch: 2.3.1
- 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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