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	---
	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
	<!-- - 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 \|
	\|:------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 1 \| <ul><li>'query: ਚੰਗਾ ਜੀ, ਫਿਰ ਮਿਲਦੇ ਹਾਂ.'</li><li>'query: Agur, gero arte.'</li><li>"query: Me'n vaig ara."</li></ul> \|
	\| 0 \| <ul><li>'query: Dobro, hvala. Kaj pa ti?'</li><li>'query: हाँ अगली बार जब तुम जाओ मुझे भी ले चलो मुझे भी प्रकृति में और गतिविधियाँ करनी हैं'</li><li>'query: Mirë, faleminderit. Po ju?'</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: Ναι, ας πάμε!")
	```

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	## 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}
	}
	```

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