---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: intfloat/multilingual-e5-small
metrics:
- accuracy
widget:
- text: 'query: Ναι, ας πάμε!'
- text: 'query: 信じられない！それはすごいね。熊を見たなんて！'
- text: 'query: Taky dobře. Potkáme se zítra?'
- text: 'query: Sì, mi farebbe piacere. A tra poco!'
- text: 'query: Γεια σου, πώς είσαι;'
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
### 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 | - 'query: ਚੰਗਾ ਜੀ, ਫਿਰ ਮਿਲਦੇ ਹਾਂ.'
- 'query: Agur, gero arte.'
- "query: Me'n vaig ara."
|
| 0 | - 'query: Dobro, hvala. Kaj pa ti?'
- 'query: हाँ अगली बार जब तुम जाओ मुझे भी ले चलो मुझे भी प्रकृति में और गतिविधियाँ करनी हैं'
- 'query: Mirë, faleminderit. Po ju?'
|
## 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: Ναι, ας πάμε!")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 2 | 7.4364 | 21 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 292 |
| 1 | 290 |
### 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: intfloat/multilingual-e5-small
- eval_max_steps: -1
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:----------:|:--------:|:-------------:|:---------------:|
| 0.0001 | 1 | 0.3645 | - |
| 0.0047 | 50 | 0.3527 | - |
| 0.0094 | 100 | 0.3424 | 0.3165 |
| 0.0142 | 150 | 0.3108 | - |
| 0.0189 | 200 | 0.2684 | 0.2215 |
| 0.0236 | 250 | 0.2197 | - |
| 0.0283 | 300 | 0.1707 | 0.1792 |
| 0.0331 | 350 | 0.1501 | - |
| 0.0378 | 400 | 0.0865 | 0.1607 |
| 0.0425 | 450 | 0.0534 | - |
| 0.0472 | 500 | 0.0307 | 0.1519 |
| 0.0520 | 550 | 0.0342 | - |
| 0.0567 | 600 | 0.0078 | 0.1478 |
| 0.0614 | 650 | 0.0144 | - |
| 0.0661 | 700 | 0.0658 | 0.1399 |
| 0.0709 | 750 | 0.0021 | - |
| 0.0756 | 800 | 0.0009 | 0.1512 |
| 0.0803 | 850 | 0.0005 | - |
| 0.0850 | 900 | 0.0018 | 0.1516 |
| 0.0897 | 950 | 0.0011 | - |
| 0.0945 | 1000 | 0.0012 | 0.1541 |
| 0.0992 | 1050 | 0.0003 | - |
| 0.1039 | 1100 | 0.0003 | 0.1415 |
| 0.1086 | 1150 | 0.0003 | - |
| 0.1134 | 1200 | 0.0002 | 0.1442 |
| 0.1181 | 1250 | 0.0006 | - |
| 0.1228 | 1300 | 0.0002 | 0.1298 |
| 0.1275 | 1350 | 0.0002 | - |
| 0.1323 | 1400 | 0.0001 | 0.1356 |
| 0.1370 | 1450 | 0.0002 | - |
| 0.1417 | 1500 | 0.0003 | 0.1493 |
| 0.1464 | 1550 | 0.0003 | - |
| 0.1512 | 1600 | 0.0002 | 0.15 |
| 0.1559 | 1650 | 0.0002 | - |
| 0.1606 | 1700 | 0.0003 | 0.1469 |
| 0.1653 | 1750 | 0.0001 | - |
| 0.1701 | 1800 | 0.0001 | 0.1554 |
| 0.1748 | 1850 | 0.0002 | - |
| 0.1795 | 1900 | 0.0001 | 0.168 |
| 0.1842 | 1950 | 0.0001 | - |
| 0.1889 | 2000 | 0.0004 | 0.1568 |
| 0.1937 | 2050 | 0.0001 | - |
| 0.1984 | 2100 | 0.0001 | 0.1513 |
| 0.2031 | 2150 | 0.0001 | - |
| 0.2078 | 2200 | 0.0003 | 0.1503 |
| 0.2126 | 2250 | 0.0002 | - |
| 0.2173 | 2300 | 0.0604 | 0.155 |
| 0.2220 | 2350 | 0.0001 | - |
| 0.2267 | 2400 | 0.0002 | 0.1739 |
| 0.2315 | 2450 | 0.0006 | - |
| 0.2362 | 2500 | 0.0002 | 0.1558 |
| 0.2409 | 2550 | 0.0002 | - |
| 0.2456 | 2600 | 0.0001 | 0.1393 |
| 0.2504 | 2650 | 0.0004 | - |
| 0.2551 | 2700 | 0.0003 | 0.1642 |
| 0.2598 | 2750 | 0.0002 | - |
| 0.2645 | 2800 | 0.0002 | 0.1776 |
| 0.2692 | 2850 | 0.0 | - |
| 0.2740 | 2900 | 0.0002 | 0.1794 |
| 0.2787 | 2950 | 0.0001 | - |
| 0.2834 | 3000 | 0.0001 | 0.183 |
| 0.2881 | 3050 | 0.0001 | - |
| 0.2929 | 3100 | 0.0001 | 0.1805 |
| 0.2976 | 3150 | 0.0001 | - |
| 0.3023 | 3200 | 0.0001 | 0.1757 |
| 0.3070 | 3250 | 0.0001 | - |
| 0.3118 | 3300 | 0.0001 | 0.1302 |
| 0.3165 | 3350 | 0.0001 | - |
| 0.3212 | 3400 | 0.0001 | 0.1348 |
| 0.3259 | 3450 | 0.0001 | - |
| 0.3307 | 3500 | 0.0005 | 0.1623 |
| 0.3354 | 3550 | 0.0 | - |
| 0.3401 | 3600 | 0.0 | 0.1286 |
| 0.3448 | 3650 | 0.0 | - |
| 0.3496 | 3700 | 0.0001 | 0.1736 |
| 0.3543 | 3750 | 0.0 | - |
| 0.3590 | 3800 | 0.0 | 0.127 |
| 0.3637 | 3850 | 0.0 | - |
| 0.3684 | 3900 | 0.0001 | 0.1231 |
| 0.3732 | 3950 | 0.0 | - |
| **0.3779** | **4000** | **0.0001** | **0.1261** |
| 0.3826 | 4050 | 0.0001 | - |
| 0.3873 | 4100 | 0.0 | 0.1216 |
| 0.3921 | 4150 | 0.0 | - |
| 0.3968 | 4200 | 0.0 | 0.1404 |
| 0.4015 | 4250 | 0.0 | - |
| 0.4062 | 4300 | 0.0 | 0.1466 |
| 0.4110 | 4350 | 0.0 | - |
| 0.4157 | 4400 | 0.0 | 0.1482 |
| 0.4204 | 4450 | 0.0 | - |
| 0.4251 | 4500 | 0.0 | 0.1547 |
| 0.4299 | 4550 | 0.0 | - |
| 0.4346 | 4600 | 0.0 | 0.1566 |
| 0.4393 | 4650 | 0.0 | - |
| 0.4440 | 4700 | 0.0 | 0.1684 |
| 0.4487 | 4750 | 0.0 | - |
| 0.4535 | 4800 | 0.0 | 0.1746 |
| 0.4582 | 4850 | 0.0 | - |
| 0.4629 | 4900 | 0.0 | 0.167 |
| 0.4676 | 4950 | 0.0 | - |
| 0.4724 | 5000 | 0.0001 | 0.1683 |
| 0.4771 | 5050 | 0.0 | - |
| 0.4818 | 5100 | 0.0 | 0.1693 |
| 0.4865 | 5150 | 0.0 | - |
| 0.4913 | 5200 | 0.0 | 0.1694 |
| 0.4960 | 5250 | 0.0 | - |
| 0.5007 | 5300 | 0.0 | 0.162 |
| 0.5054 | 5350 | 0.0 | - |
| 0.5102 | 5400 | 0.0 | 0.1388 |
| 0.5149 | 5450 | 0.0 | - |
| 0.5196 | 5500 | 0.0 | 0.1353 |
| 0.5243 | 5550 | 0.0 | - |
| 0.5291 | 5600 | 0.0 | 0.1401 |
| 0.5338 | 5650 | 0.0 | - |
| 0.5385 | 5700 | 0.0 | 0.1466 |
| 0.5432 | 5750 | 0.0 | - |
| 0.5479 | 5800 | 0.0 | 0.1529 |
| 0.5527 | 5850 | 0.0 | - |
| 0.5574 | 5900 | 0.0 | 0.1488 |
| 0.5621 | 5950 | 0.0 | - |
| 0.5668 | 6000 | 0.0 | 0.147 |
| 0.5716 | 6050 | 0.0 | - |
| 0.5763 | 6100 | 0.0 | 0.1493 |
| 0.5810 | 6150 | 0.0 | - |
| 0.5857 | 6200 | 0.0 | 0.1525 |
| 0.5905 | 6250 | 0.0 | - |
| 0.5952 | 6300 | 0.0 | 0.1505 |
| 0.5999 | 6350 | 0.0 | - |
| 0.6046 | 6400 | 0.0 | 0.1554 |
| 0.6094 | 6450 | 0.0 | - |
| 0.6141 | 6500 | 0.0 | 0.1546 |
| 0.6188 | 6550 | 0.0 | - |
| 0.6235 | 6600 | 0.0 | 0.1598 |
| 0.6282 | 6650 | 0.0 | - |
| 0.6330 | 6700 | 0.0 | 0.179 |
| 0.6377 | 6750 | 0.0 | - |
| 0.6424 | 6800 | 0.0 | 0.1719 |
| 0.6471 | 6850 | 0.0001 | - |
| 0.6519 | 6900 | 0.0 | 0.1812 |
| 0.6566 | 6950 | 0.0 | - |
| 0.6613 | 7000 | 0.0 | 0.1648 |
| 0.6660 | 7050 | 0.0 | - |
| 0.6708 | 7100 | 0.0 | 0.1717 |
| 0.6755 | 7150 | 0.0 | - |
| 0.6802 | 7200 | 0.0 | 0.1793 |
| 0.6849 | 7250 | 0.0 | - |
| 0.6897 | 7300 | 0.0 | 0.1766 |
| 0.6944 | 7350 | 0.0 | - |
| 0.6991 | 7400 | 0.0 | 0.177 |
| 0.7038 | 7450 | 0.0 | - |
| 0.7085 | 7500 | 0.0 | 0.1749 |
| 0.7133 | 7550 | 0.0 | - |
| 0.7180 | 7600 | 0.0 | 0.1814 |
| 0.7227 | 7650 | 0.0 | - |
| 0.7274 | 7700 | 0.0 | 0.1742 |
| 0.7322 | 7750 | 0.0 | - |
| 0.7369 | 7800 | 0.0 | 0.179 |
| 0.7416 | 7850 | 0.0 | - |
| 0.7463 | 7900 | 0.0 | 0.1767 |
| 0.7511 | 7950 | 0.0 | - |
| 0.7558 | 8000 | 0.0 | 0.1809 |
| 0.7605 | 8050 | 0.0 | - |
| 0.7652 | 8100 | 0.0 | 0.1767 |
| 0.7700 | 8150 | 0.0 | - |
| 0.7747 | 8200 | 0.0 | 0.1698 |
| 0.7794 | 8250 | 0.0 | - |
| 0.7841 | 8300 | 0.0 | 0.1772 |
| 0.7889 | 8350 | 0.0 | - |
| 0.7936 | 8400 | 0.0 | 0.1722 |
| 0.7983 | 8450 | 0.0 | - |
| 0.8030 | 8500 | 0.0 | 0.1671 |
| 0.8077 | 8550 | 0.0 | - |
| 0.8125 | 8600 | 0.0 | 0.181 |
| 0.8172 | 8650 | 0.0 | - |
| 0.8219 | 8700 | 0.0 | 0.1788 |
| 0.8266 | 8750 | 0.0 | - |
| 0.8314 | 8800 | 0.0 | 0.1784 |
| 0.8361 | 8850 | 0.0 | - |
| 0.8408 | 8900 | 0.0 | 0.1806 |
| 0.8455 | 8950 | 0.0 | - |
| 0.8503 | 9000 | 0.0 | 0.1783 |
| 0.8550 | 9050 | 0.0 | - |
| 0.8597 | 9100 | 0.0 | 0.1783 |
| 0.8644 | 9150 | 0.0 | - |
| 0.8692 | 9200 | 0.0 | 0.1785 |
| 0.8739 | 9250 | 0.0 | - |
| 0.8786 | 9300 | 0.0 | 0.1772 |
| 0.8833 | 9350 | 0.0 | - |
| 0.8880 | 9400 | 0.0 | 0.1816 |
| 0.8928 | 9450 | 0.0 | - |
| 0.8975 | 9500 | 0.0 | 0.1794 |
| 0.9022 | 9550 | 0.0 | - |
| 0.9069 | 9600 | 0.0 | 0.168 |
| 0.9117 | 9650 | 0.0 | - |
| 0.9164 | 9700 | 0.0 | 0.1771 |
| 0.9211 | 9750 | 0.0 | - |
| 0.9258 | 9800 | 0.0 | 0.1675 |
| 0.9306 | 9850 | 0.0 | - |
| 0.9353 | 9900 | 0.0 | 0.1746 |
| 0.9400 | 9950 | 0.0 | - |
| 0.9447 | 10000 | 0.0 | 0.1769 |
| 0.9495 | 10050 | 0.0 | - |
| 0.9542 | 10100 | 0.0 | 0.177 |
| 0.9589 | 10150 | 0.0 | - |
| 0.9636 | 10200 | 0.0 | 0.1771 |
| 0.9684 | 10250 | 0.0 | - |
| 0.9731 | 10300 | 0.0 | 0.1794 |
| 0.9778 | 10350 | 0.0 | - |
| 0.9825 | 10400 | 0.0 | 0.177 |
| 0.9872 | 10450 | 0.0 | - |
| 0.9920 | 10500 | 0.0 | 0.1794 |
| 0.9967 | 10550 | 0.0 | - |
* 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}
}
```