setfitemotions / README.md

Add SetFit model

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	---
	base_model: projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base
	library_name: setfit
	metrics:
	- accuracy
	pipeline_tag: text-classification
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: Aquest text és Varis
	- text: Aquest text és Mobiliari Urbà
	- text: Aquest text és Velocitat
	- text: Aquest text és Parcs i Jardins
	- text: Aquest text és Enllumenat
	inference: true
	---

	# SetFit with projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base

	This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base](https://huggingface.co/projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base) 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: [projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base](https://huggingface.co/projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base)
	- Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
	- Maximum Sequence Length: 128 tokens
	- Number of Classes: 14 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>'Aquest text és Arbrat'</li><li>'Aquest text és Arbrat'</li><li>'Aquest text és Arbrat'</li></ul> \|
	\| 1 \| <ul><li>'Aquest text és Circulació'</li><li>'Aquest text és Circulació'</li><li>'Aquest text és Circulació'</li></ul> \|
	\| 2 \| <ul><li>'Aquest text és Comentaris'</li><li>'Aquest text és Comentaris'</li><li>'Aquest text és Comentaris'</li></ul> \|
	\| 3 \| <ul><li>'Aquest text és Enllumenat'</li><li>'Aquest text és Enllumenat'</li><li>'Aquest text és Enllumenat'</li></ul> \|
	\| 4 \| <ul><li>'Aquest text és Informació'</li><li>'Aquest text és Informació'</li><li>'Aquest text és Informació'</li></ul> \|
	\| 5 \| <ul><li>'Aquest text és Manteniment'</li><li>'Aquest text és Manteniment'</li><li>'Aquest text és Manteniment'</li></ul> \|
	\| 6 \| <ul><li>'Aquest text és Mobiliari Urbà'</li><li>'Aquest text és Mobiliari Urbà'</li><li>'Aquest text és Mobiliari Urbà'</li></ul> \|
	\| 7 \| <ul><li>'Aquest text és Neteja'</li><li>'Aquest text és Neteja'</li><li>'Aquest text és Neteja'</li></ul> \|
	\| 8 \| <ul><li>'Aquest text és Parcs i Jardins'</li><li>'Aquest text és Parcs i Jardins'</li><li>'Aquest text és Parcs i Jardins'</li></ul> \|
	\| 9 \| <ul><li>'Aquest text és Senyalització'</li><li>'Aquest text és Senyalització'</li><li>'Aquest text és Senyalització'</li></ul> \|
	\| 10 \| <ul><li>'Aquest text és Sorolls'</li><li>'Aquest text és Sorolls'</li><li>'Aquest text és Sorolls'</li></ul> \|
	\| 11 \| <ul><li>'Aquest text és Suggeriments'</li><li>'Aquest text és Suggeriments'</li><li>'Aquest text és Suggeriments'</li></ul> \|
	\| 12 \| <ul><li>'Aquest text és Varis'</li><li>'Aquest text és Varis'</li><li>'Aquest text és Varis'</li></ul> \|
	\| 13 \| <ul><li>'Aquest text és Velocitat'</li><li>'Aquest text és Velocitat'</li><li>'Aquest text és Velocitat'</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("adriansanz/setfitemotions")
	# Run inference
	preds = model("Aquest text és Varis")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 4 \| 4.2143 \| 6 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0 \| 10 \|
	\| 1 \| 10 \|
	\| 2 \| 10 \|
	\| 3 \| 10 \|
	\| 4 \| 10 \|
	\| 5 \| 10 \|
	\| 6 \| 10 \|
	\| 7 \| 10 \|
	\| 8 \| 10 \|
	\| 9 \| 10 \|
	\| 10 \| 10 \|
	\| 11 \| 10 \|
	\| 12 \| 10 \|
	\| 13 \| 10 \|

	### Training Hyperparameters
	- batch_size: (16, 16)
	- num_epochs: (3, 3)
	- max_steps: -1
	- sampling_strategy: oversampling
	- body_learning_rate: (2e-05, 1e-05)
	- head_learning_rate: 0.01
	- loss: CosineSimilarityLoss
	- distance_metric: cosine_distance
	- margin: 0.25
	- end_to_end: False
	- use_amp: False
	- warmup_proportion: 0.1
	- seed: 42
	- eval_max_steps: -1
	- load_best_model_at_end: True

	### Training Results
	\| Epoch \| Step \| Training Loss \| Validation Loss \|
	\|:------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0009 \| 1 \| 0.2021 \| - \|
	\| 0.0439 \| 50 \| 0.0263 \| - \|
	\| 0.0879 \| 100 \| 0.0032 \| - \|
	\| 0.1318 \| 150 \| 0.0015 \| - \|
	\| 0.1757 \| 200 \| 0.0012 \| - \|
	\| 0.2197 \| 250 \| 0.0007 \| - \|
	\| 0.2636 \| 300 \| 0.0008 \| - \|
	\| 0.3076 \| 350 \| 0.0006 \| - \|
	\| 0.3515 \| 400 \| 0.0003 \| - \|
	\| 0.3954 \| 450 \| 0.0003 \| - \|
	\| 0.4394 \| 500 \| 0.0004 \| - \|
	\| 0.4833 \| 550 \| 0.0005 \| - \|
	\| 0.5272 \| 600 \| 0.0004 \| - \|
	\| 0.5712 \| 650 \| 0.0005 \| - \|
	\| 0.6151 \| 700 \| 0.0005 \| - \|
	\| 0.6591 \| 750 \| 0.0002 \| - \|
	\| 0.7030 \| 800 \| 0.0001 \| - \|
	\| 0.7469 \| 850 \| 0.0004 \| - \|
	\| 0.7909 \| 900 \| 0.0002 \| - \|
	\| 0.8348 \| 950 \| 0.0003 \| - \|
	\| 0.8787 \| 1000 \| 0.0002 \| - \|
	\| 0.9227 \| 1050 \| 0.0002 \| - \|
	\| 0.9666 \| 1100 \| 0.0003 \| - \|
	\| 1.0105 \| 1150 \| 0.0002 \| - \|
	\| 1.0545 \| 1200 \| 0.0002 \| - \|
	\| 1.0984 \| 1250 \| 0.0002 \| - \|
	\| 1.1424 \| 1300 \| 0.0003 \| - \|
	\| 1.1863 \| 1350 \| 0.0003 \| - \|
	\| 1.2302 \| 1400 \| 0.0001 \| - \|
	\| 1.2742 \| 1450 \| 0.0002 \| - \|
	\| 1.3181 \| 1500 \| 0.0001 \| - \|
	\| 1.3620 \| 1550 \| 0.0001 \| - \|
	\| 1.4060 \| 1600 \| 0.0003 \| - \|
	\| 1.4499 \| 1650 \| 0.0001 \| - \|
	\| 1.4938 \| 1700 \| 0.0001 \| - \|
	\| 1.5378 \| 1750 \| 0.0001 \| - \|
	\| 1.5817 \| 1800 \| 0.0001 \| - \|
	\| 1.6257 \| 1850 \| 0.0001 \| - \|
	\| 1.6696 \| 1900 \| 0.0001 \| - \|
	\| 1.7135 \| 1950 \| 0.0001 \| - \|
	\| 1.7575 \| 2000 \| 0.0002 \| - \|
	\| 1.8014 \| 2050 \| 0.0001 \| - \|
	\| 1.8453 \| 2100 \| 0.0001 \| - \|
	\| 1.8893 \| 2150 \| 0.0002 \| - \|
	\| 1.9332 \| 2200 \| 0.0001 \| - \|
	\| 1.9772 \| 2250 \| 0.0002 \| - \|
	\| 2.0211 \| 2300 \| 0.0001 \| - \|
	\| 2.0650 \| 2350 \| 0.0001 \| - \|
	\| 2.1090 \| 2400 \| 0.0001 \| - \|
	\| 2.1529 \| 2450 \| 0.0001 \| - \|
	\| 2.1968 \| 2500 \| 0.0001 \| - \|
	\| 2.2408 \| 2550 \| 0.0001 \| - \|
	\| 2.2847 \| 2600 \| 0.0 \| - \|
	\| 2.3286 \| 2650 \| 0.0001 \| - \|
	\| 2.3726 \| 2700 \| 0.0001 \| - \|
	\| 2.4165 \| 2750 \| 0.0001 \| - \|
	\| 2.4605 \| 2800 \| 0.0001 \| - \|
	\| 2.5044 \| 2850 \| 0.0001 \| - \|
	\| 2.5483 \| 2900 \| 0.0001 \| - \|
	\| 2.5923 \| 2950 \| 0.0001 \| - \|
	\| 2.6362 \| 3000 \| 0.0001 \| - \|
	\| 2.6801 \| 3050 \| 0.0001 \| - \|
	\| 2.7241 \| 3100 \| 0.0001 \| - \|
	\| 2.7680 \| 3150 \| 0.0001 \| - \|
	\| 2.8120 \| 3200 \| 0.0001 \| - \|
	\| 2.8559 \| 3250 \| 0.0001 \| - \|
	\| 2.8998 \| 3300 \| 0.0001 \| - \|
	\| 2.9438 \| 3350 \| 0.0001 \| - \|
	\| 2.9877 \| 3400 \| 0.0001 \| - \|

	### Framework Versions
	- Python: 3.10.12
	- SetFit: 1.0.3
	- Sentence Transformers: 3.0.1
	- Transformers: 4.39.0
	- PyTorch: 2.3.1+cu121
	- 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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