Setfit_designed_sample_svm_head / README.md

Add SetFit model

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	---
	library_name: setfit
	metrics:
	- accuracy
	pipeline_tag: text-classification
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: Now that the baffling, elongated, hyperreal coronation has occurred—no, not
	that one—and Liz Truss has become Prime Minister, a degree of intervention and
	action on energy bills has emerged, ahead of the looming socioeconomic catastrophe
	facing the country this winter.
	- text: But it needs to go much further.
	- text: What could possibly go wrong?
	- text: If you are White you might feel bad about hurting others or you might feel
	afraid to lose this privilege….Overcoming White privilege is a job that must start
	with the White community….
	- text: '[JF: Obviously, immigration wasn’t stopped: the current population of the
	United States is 329.5 million—it passed 300 million in 2006.'
	inference: true
	---

	# SetFit

	This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A SVC 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: [Unknown](https://huggingface.co/unknown) -->
	- Classification head: a SVC instance
	- Maximum Sequence Length: 384 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>'Gone are the days when they led the world in recession-busting'</li><li>'Who so mean that he will not himself be taxed, who so mindful of wealth that he will not favor increasing the popular taxes, in aid of these defective children?'</li><li>'That state has sixty-two counties and sixty cities … In addition there are 932 towns, 507 villages, and, at the last count, 9,600 school districts … Just try to render efficient service … amid the diffused identities and inevitable jealousies of, roughly, 11,000 independent administrative officers or boards!'</li></ul> \|
	\| 0 \| <ul><li>'Is this a warning of what’s to come?'</li><li>'This unique set of circumstances has brought PCL back into focus as the safe haven of choice for global players seeking somewhere to stash their cash.'</li><li>'Socialists believe that, if everyone cannot have something, no one shall.'</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("SOUMYADEEPSAR/Setfit_designed_sample_svm_head")
	# Run inference
	preds = model("What could possibly go wrong?")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:--------\|:----\|
	\| Word count \| 3 \| 36.5327 \| 97 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0 \| 100 \|
	\| 1 \| 114 \|

	### Training Hyperparameters
	- batch_size: (8, 8)
	- num_epochs: (1, 1)
	- max_steps: -1
	- sampling_strategy: oversampling
	- body_learning_rate: (2e-05, 2e-05)
	- head_learning_rate: 2e-05
	- 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: False

	### Training Results
	\| Epoch \| Step \| Training Loss \| Validation Loss \|
	\|:------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0003 \| 1 \| 0.3597 \| - \|
	\| 0.0161 \| 50 \| 0.2693 \| - \|
	\| 0.0323 \| 100 \| 0.2501 \| - \|
	\| 0.0484 \| 150 \| 0.2691 \| - \|
	\| 0.0645 \| 200 \| 0.063 \| - \|
	\| 0.0806 \| 250 \| 0.0179 \| - \|
	\| 0.0968 \| 300 \| 0.0044 \| - \|
	\| 0.1129 \| 350 \| 0.0003 \| - \|
	\| 0.1290 \| 400 \| 0.0005 \| - \|
	\| 0.1452 \| 450 \| 0.0002 \| - \|
	\| 0.1613 \| 500 \| 0.0003 \| - \|
	\| 0.1774 \| 550 \| 0.0001 \| - \|
	\| 0.1935 \| 600 \| 0.0001 \| - \|
	\| 0.2097 \| 650 \| 0.0001 \| - \|
	\| 0.2258 \| 700 \| 0.0001 \| - \|
	\| 0.2419 \| 750 \| 0.0001 \| - \|
	\| 0.2581 \| 800 \| 0.0 \| - \|
	\| 0.2742 \| 850 \| 0.0001 \| - \|
	\| 0.2903 \| 900 \| 0.0002 \| - \|
	\| 0.3065 \| 950 \| 0.0 \| - \|
	\| 0.3226 \| 1000 \| 0.0 \| - \|
	\| 0.3387 \| 1050 \| 0.0002 \| - \|
	\| 0.3548 \| 1100 \| 0.0 \| - \|
	\| 0.3710 \| 1150 \| 0.0001 \| - \|
	\| 0.3871 \| 1200 \| 0.0001 \| - \|
	\| 0.4032 \| 1250 \| 0.0 \| - \|
	\| 0.4194 \| 1300 \| 0.0 \| - \|
	\| 0.4355 \| 1350 \| 0.0 \| - \|
	\| 0.4516 \| 1400 \| 0.0001 \| - \|
	\| 0.4677 \| 1450 \| 0.0 \| - \|
	\| 0.4839 \| 1500 \| 0.0 \| - \|
	\| 0.5 \| 1550 \| 0.0001 \| - \|
	\| 0.5161 \| 1600 \| 0.0001 \| - \|
	\| 0.5323 \| 1650 \| 0.0 \| - \|
	\| 0.5484 \| 1700 \| 0.0 \| - \|
	\| 0.5645 \| 1750 \| 0.0 \| - \|
	\| 0.5806 \| 1800 \| 0.0 \| - \|
	\| 0.5968 \| 1850 \| 0.0 \| - \|
	\| 0.6129 \| 1900 \| 0.0 \| - \|
	\| 0.6290 \| 1950 \| 0.0001 \| - \|
	\| 0.6452 \| 2000 \| 0.0 \| - \|
	\| 0.6613 \| 2050 \| 0.0 \| - \|
	\| 0.6774 \| 2100 \| 0.0 \| - \|
	\| 0.6935 \| 2150 \| 0.0001 \| - \|
	\| 0.7097 \| 2200 \| 0.0 \| - \|
	\| 0.7258 \| 2250 \| 0.0 \| - \|
	\| 0.7419 \| 2300 \| 0.0001 \| - \|
	\| 0.7581 \| 2350 \| 0.0001 \| - \|
	\| 0.7742 \| 2400 \| 0.0001 \| - \|
	\| 0.7903 \| 2450 \| 0.0 \| - \|
	\| 0.8065 \| 2500 \| 0.0 \| - \|
	\| 0.8226 \| 2550 \| 0.0 \| - \|
	\| 0.8387 \| 2600 \| 0.0 \| - \|
	\| 0.8548 \| 2650 \| 0.0001 \| - \|
	\| 0.8710 \| 2700 \| 0.0001 \| - \|
	\| 0.8871 \| 2750 \| 0.0 \| - \|
	\| 0.9032 \| 2800 \| 0.0 \| - \|
	\| 0.9194 \| 2850 \| 0.0 \| - \|
	\| 0.9355 \| 2900 \| 0.0001 \| - \|
	\| 0.9516 \| 2950 \| 0.0 \| - \|
	\| 0.9677 \| 3000 \| 0.0001 \| - \|
	\| 0.9839 \| 3050 \| 0.0 \| - \|
	\| 1.0 \| 3100 \| 0.0 \| - \|
	\| 0.0003 \| 1 \| 0.326 \| - \|
	\| 0.0172 \| 50 \| 0.2514 \| - \|
	\| 0.0345 \| 100 \| 0.434 \| - \|
	\| 0.0517 \| 150 \| 0.1265 \| - \|
	\| 0.0689 \| 200 \| 0.125 \| - \|
	\| 0.0861 \| 250 \| 0.2375 \| - \|
	\| 0.1034 \| 300 \| 0.0014 \| - \|
	\| 0.1206 \| 350 \| 0.1192 \| - \|
	\| 0.1378 \| 400 \| 0.0166 \| - \|
	\| 0.1551 \| 450 \| 0.0002 \| - \|
	\| 0.1723 \| 500 \| 0.0001 \| - \|
	\| 0.1895 \| 550 \| 0.0 \| - \|
	\| 0.2068 \| 600 \| 0.0 \| - \|
	\| 0.2240 \| 650 \| 0.0001 \| - \|
	\| 0.2412 \| 700 \| 0.0 \| - \|
	\| 0.2584 \| 750 \| 0.0 \| - \|
	\| 0.2757 \| 800 \| 0.0 \| - \|
	\| 0.2929 \| 850 \| 0.0 \| - \|
	\| 0.3101 \| 900 \| 0.0 \| - \|
	\| 0.3274 \| 950 \| 0.0001 \| - \|
	\| 0.3446 \| 1000 \| 0.0 \| - \|
	\| 0.3618 \| 1050 \| 0.0001 \| - \|
	\| 0.3790 \| 1100 \| 0.0 \| - \|
	\| 0.3963 \| 1150 \| 0.0001 \| - \|
	\| 0.4135 \| 1200 \| 0.0 \| - \|
	\| 0.4307 \| 1250 \| 0.0001 \| - \|
	\| 0.4480 \| 1300 \| 0.0 \| - \|
	\| 0.4652 \| 1350 \| 0.0 \| - \|
	\| 0.4824 \| 1400 \| 0.0 \| - \|
	\| 0.4997 \| 1450 \| 0.0 \| - \|
	\| 0.5169 \| 1500 \| 0.0 \| - \|
	\| 0.5341 \| 1550 \| 0.0001 \| - \|
	\| 0.5513 \| 1600 \| 0.0 \| - \|
	\| 0.5686 \| 1650 \| 0.0 \| - \|
	\| 0.5858 \| 1700 \| 0.0 \| - \|
	\| 0.6030 \| 1750 \| 0.0 \| - \|
	\| 0.6203 \| 1800 \| 0.0 \| - \|
	\| 0.6375 \| 1850 \| 0.0 \| - \|
	\| 0.6547 \| 1900 \| 0.0001 \| - \|
	\| 0.6720 \| 1950 \| 0.0001 \| - \|
	\| 0.6892 \| 2000 \| 0.0 \| - \|
	\| 0.7064 \| 2050 \| 0.0 \| - \|
	\| 0.7236 \| 2100 \| 0.0 \| - \|
	\| 0.7409 \| 2150 \| 0.0 \| - \|
	\| 0.7581 \| 2200 \| 0.0 \| - \|
	\| 0.7753 \| 2250 \| 0.0 \| - \|
	\| 0.7926 \| 2300 \| 0.0 \| - \|
	\| 0.8098 \| 2350 \| 0.0 \| - \|
	\| 0.8270 \| 2400 \| 0.0 \| - \|
	\| 0.8442 \| 2450 \| 0.0001 \| - \|
	\| 0.8615 \| 2500 \| 0.0 \| - \|
	\| 0.8787 \| 2550 \| 0.0 \| - \|
	\| 0.8959 \| 2600 \| 0.0 \| - \|
	\| 0.9132 \| 2650 \| 0.0 \| - \|
	\| 0.9304 \| 2700 \| 0.0 \| - \|
	\| 0.9476 \| 2750 \| 0.0 \| - \|
	\| 0.9649 \| 2800 \| 0.0 \| - \|
	\| 0.9821 \| 2850 \| 0.0 \| - \|
	\| 0.9993 \| 2900 \| 0.0 \| - \|

	### Framework Versions
	- Python: 3.10.12
	- SetFit: 1.0.3
	- Sentence Transformers: 3.0.1
	- Transformers: 4.39.0
	- PyTorch: 2.3.0+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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