classifier_woog_base_oos_combined / README.md

Add SetFit model

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	---
	base_model: sentence-transformers/paraphrase-mpnet-base-v2
	library_name: setfit
	metrics:
	- accuracy
	pipeline_tag: text-classification
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: Do you have the Nike Blazer Mid sacai Snow Beach in size 9?
	- text: How can I adapt K-beauty routines for dry weather?
	- text: I like to listen to classical music
	- text: If this product is for weight management, what is the sub-category?
	- text: How long does it take to receive a refund after returning a product?
	inference: true
	model-index:
	- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
	results:
	- task:
	type: text-classification
	name: Text Classification
	dataset:
	name: Unknown
	type: unknown
	split: test
	metrics:
	- type: accuracy
	value: 0.8711340206185567
	name: Accuracy
	---

	# SetFit with sentence-transformers/paraphrase-mpnet-base-v2

	This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-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: [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-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: 6 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 \|
	\|:------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| product policy \| <ul><li>'Do you offer a gift wrapping service for sneakers?'</li><li>'What are the consequences if my account is suspended or terminated for any reason?'</li><li>'Do you share my personal information with third parties?'</li></ul> \|
	\| general faq \| <ul><li>'Can you explain why Mashru silk is considered more comfortable to wear compared to pure silk sarees?'</li><li>'What are some tips for maximizing the antioxidant content when brewing green tea?'</li><li>'Can you recommend K-beauty products for hot and humid climates?'</li></ul> \|
	\| product discoverability \| <ul><li>'Are there any sarees with Kadwa Weave technique?'</li><li>'cookie boxes with dividers'</li><li>'Are there any products for dry skin?'</li></ul> \|
	\| Out of Scope \| <ul><li>'Is this website secure?'</li><li>'How do you handle intellectual property disputes?'</li><li>'Do you know how to play the piano?'</li></ul> \|
	\| order tracking \| <ul><li>'I want to deliver candle supplies to Jaipur, how many days will it take to deliver?'</li><li>'I want to deliver bags to Pune, how many days will it take to deliver?'</li><li>'I need to change the delivery address for my recent order, how can I do that?'</li></ul> \|
	\| product faq \| <ul><li>'Does this product help with dark spots?'</li><li>'3. Is this product currently in stock?'</li><li>'Is the product in stock?'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.8711 \|

	## 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("I like to listen to classical music")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 4 \| 10.66 \| 28 \|

	\| Label \| Training Sample Count \|
	\|:------------------------\|:----------------------\|
	\| Out of Scope \| 50 \|
	\| general faq \| 50 \|
	\| order tracking \| 50 \|
	\| product discoverability \| 50 \|
	\| product faq \| 50 \|
	\| product policy \| 50 \|

	### Training Hyperparameters
	- batch_size: (16, 16)
	- num_epochs: (2, 2)
	- 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 \|
	\|:------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0002 \| 1 \| 0.2592 \| - \|
	\| 0.0107 \| 50 \| 0.2424 \| - \|
	\| 0.0213 \| 100 \| 0.1506 \| - \|
	\| 0.0320 \| 150 \| 0.222 \| - \|
	\| 0.0427 \| 200 \| 0.1227 \| - \|
	\| 0.0533 \| 250 \| 0.1801 \| - \|
	\| 0.0640 \| 300 \| 0.1111 \| - \|
	\| 0.0747 \| 350 \| 0.0346 \| - \|
	\| 0.0853 \| 400 \| 0.0313 \| - \|
	\| 0.0960 \| 450 \| 0.0048 \| - \|
	\| 0.1067 \| 500 \| 0.0023 \| - \|
	\| 0.1173 \| 550 \| 0.0018 \| - \|
	\| 0.1280 \| 600 \| 0.0133 \| - \|
	\| 0.1387 \| 650 \| 0.0008 \| - \|
	\| 0.1493 \| 700 \| 0.0006 \| - \|
	\| 0.1600 \| 750 \| 0.0005 \| - \|
	\| 0.1706 \| 800 \| 0.0008 \| - \|
	\| 0.1813 \| 850 \| 0.0007 \| - \|
	\| 0.1920 \| 900 \| 0.0006 \| - \|
	\| 0.2026 \| 950 \| 0.0006 \| - \|
	\| 0.2133 \| 1000 \| 0.0003 \| - \|
	\| 0.2240 \| 1050 \| 0.0026 \| - \|
	\| 0.2346 \| 1100 \| 0.0004 \| - \|
	\| 0.2453 \| 1150 \| 0.0004 \| - \|
	\| 0.2560 \| 1200 \| 0.0004 \| - \|
	\| 0.2666 \| 1250 \| 0.0005 \| - \|
	\| 0.2773 \| 1300 \| 0.0005 \| - \|
	\| 0.2880 \| 1350 \| 0.0003 \| - \|
	\| 0.2986 \| 1400 \| 0.0001 \| - \|
	\| 0.3093 \| 1450 \| 0.0001 \| - \|
	\| 0.3200 \| 1500 \| 0.0002 \| - \|
	\| 0.3306 \| 1550 \| 0.0002 \| - \|
	\| 0.3413 \| 1600 \| 0.0002 \| - \|
	\| 0.3520 \| 1650 \| 0.0001 \| - \|
	\| 0.3626 \| 1700 \| 0.0004 \| - \|
	\| 0.3733 \| 1750 \| 0.0002 \| - \|
	\| 0.3840 \| 1800 \| 0.0005 \| - \|
	\| 0.3946 \| 1850 \| 0.0002 \| - \|
	\| 0.4053 \| 1900 \| 0.0002 \| - \|
	\| 0.4160 \| 1950 \| 0.0001 \| - \|
	\| 0.4266 \| 2000 \| 0.0001 \| - \|
	\| 0.4373 \| 2050 \| 0.0001 \| - \|
	\| 0.4480 \| 2100 \| 0.0001 \| - \|
	\| 0.4586 \| 2150 \| 0.0001 \| - \|
	\| 0.4693 \| 2200 \| 0.0002 \| - \|
	\| 0.4799 \| 2250 \| 0.0048 \| - \|
	\| 0.4906 \| 2300 \| 0.0001 \| - \|
	\| 0.5013 \| 2350 \| 0.001 \| - \|
	\| 0.5119 \| 2400 \| 0.0002 \| - \|
	\| 0.5226 \| 2450 \| 0.0002 \| - \|
	\| 0.5333 \| 2500 \| 0.0001 \| - \|
	\| 0.5439 \| 2550 \| 0.0001 \| - \|
	\| 0.5546 \| 2600 \| 0.0001 \| - \|
	\| 0.5653 \| 2650 \| 0.0001 \| - \|
	\| 0.5759 \| 2700 \| 0.0001 \| - \|
	\| 0.5866 \| 2750 \| 0.0001 \| - \|
	\| 0.5973 \| 2800 \| 0.0001 \| - \|
	\| 0.6079 \| 2850 \| 0.0001 \| - \|
	\| 0.6186 \| 2900 \| 0.0001 \| - \|
	\| 0.6293 \| 2950 \| 0.0001 \| - \|
	\| 0.6399 \| 3000 \| 0.0001 \| - \|
	\| 0.6506 \| 3050 \| 0.0001 \| - \|
	\| 0.6613 \| 3100 \| 0.0001 \| - \|
	\| 0.6719 \| 3150 \| 0.0001 \| - \|
	\| 0.6826 \| 3200 \| 0.0001 \| - \|
	\| 0.6933 \| 3250 \| 0.0001 \| - \|
	\| 0.7039 \| 3300 \| 0.0001 \| - \|
	\| 0.7146 \| 3350 \| 0.0001 \| - \|
	\| 0.7253 \| 3400 \| 0.0001 \| - \|
	\| 0.7359 \| 3450 \| 0.0001 \| - \|
	\| 0.7466 \| 3500 \| 0.0001 \| - \|
	\| 0.7573 \| 3550 \| 0.0001 \| - \|
	\| 0.7679 \| 3600 \| 0.0001 \| - \|
	\| 0.7786 \| 3650 \| 0.0001 \| - \|
	\| 0.7892 \| 3700 \| 0.0001 \| - \|
	\| 0.7999 \| 3750 \| 0.0001 \| - \|
	\| 0.8106 \| 3800 \| 0.0001 \| - \|
	\| 0.8212 \| 3850 \| 0.0 \| - \|
	\| 0.8319 \| 3900 \| 0.0001 \| - \|
	\| 0.8426 \| 3950 \| 0.0001 \| - \|
	\| 0.8532 \| 4000 \| 0.0001 \| - \|
	\| 0.8639 \| 4050 \| 0.0001 \| - \|
	\| 0.8746 \| 4100 \| 0.0001 \| - \|
	\| 0.8852 \| 4150 \| 0.0 \| - \|
	\| 0.8959 \| 4200 \| 0.0001 \| - \|
	\| 0.9066 \| 4250 \| 0.0001 \| - \|
	\| 0.9172 \| 4300 \| 0.0001 \| - \|
	\| 0.9279 \| 4350 \| 0.0001 \| - \|
	\| 0.9386 \| 4400 \| 0.0001 \| - \|
	\| 0.9492 \| 4450 \| 0.0001 \| - \|
	\| 0.9599 \| 4500 \| 0.0001 \| - \|
	\| 0.9706 \| 4550 \| 0.0001 \| - \|
	\| 0.9812 \| 4600 \| 0.0 \| - \|
	\| 0.9919 \| 4650 \| 0.0001 \| - \|
	\| 1.0026 \| 4700 \| 0.0 \| - \|
	\| 1.0132 \| 4750 \| 0.0001 \| - \|
	\| 1.0239 \| 4800 \| 0.0001 \| - \|
	\| 1.0346 \| 4850 \| 0.0001 \| - \|
	\| 1.0452 \| 4900 \| 0.0001 \| - \|
	\| 1.0559 \| 4950 \| 0.0001 \| - \|
	\| 1.0666 \| 5000 \| 0.0 \| - \|
	\| 1.0772 \| 5050 \| 0.0 \| - \|
	\| 1.0879 \| 5100 \| 0.0001 \| - \|
	\| 1.0985 \| 5150 \| 0.0 \| - \|
	\| 1.1092 \| 5200 \| 0.0 \| - \|
	\| 1.1199 \| 5250 \| 0.0 \| - \|
	\| 1.1305 \| 5300 \| 0.0001 \| - \|
	\| 1.1412 \| 5350 \| 0.0001 \| - \|
	\| 1.1519 \| 5400 \| 0.0 \| - \|
	\| 1.1625 \| 5450 \| 0.0001 \| - \|
	\| 1.1732 \| 5500 \| 0.0001 \| - \|
	\| 1.1839 \| 5550 \| 0.0002 \| - \|
	\| 1.1945 \| 5600 \| 0.0 \| - \|
	\| 1.2052 \| 5650 \| 0.0 \| - \|
	\| 1.2159 \| 5700 \| 0.0 \| - \|
	\| 1.2265 \| 5750 \| 0.0 \| - \|
	\| 1.2372 \| 5800 \| 0.0001 \| - \|
	\| 1.2479 \| 5850 \| 0.0001 \| - \|
	\| 1.2585 \| 5900 \| 0.0001 \| - \|
	\| 1.2692 \| 5950 \| 0.0 \| - \|
	\| 1.2799 \| 6000 \| 0.0 \| - \|
	\| 1.2905 \| 6050 \| 0.0 \| - \|
	\| 1.3012 \| 6100 \| 0.0001 \| - \|
	\| 1.3119 \| 6150 \| 0.0 \| - \|
	\| 1.3225 \| 6200 \| 0.0 \| - \|
	\| 1.3332 \| 6250 \| 0.0 \| - \|
	\| 1.3439 \| 6300 \| 0.0 \| - \|
	\| 1.3545 \| 6350 \| 0.0 \| - \|
	\| 1.3652 \| 6400 \| 0.0 \| - \|
	\| 1.3759 \| 6450 \| 0.0 \| - \|
	\| 1.3865 \| 6500 \| 0.0 \| - \|
	\| 1.3972 \| 6550 \| 0.0 \| - \|
	\| 1.4078 \| 6600 \| 0.0 \| - \|
	\| 1.4185 \| 6650 \| 0.0 \| - \|
	\| 1.4292 \| 6700 \| 0.0 \| - \|
	\| 1.4398 \| 6750 \| 0.0 \| - \|
	\| 1.4505 \| 6800 \| 0.0 \| - \|
	\| 1.4612 \| 6850 \| 0.0 \| - \|
	\| 1.4718 \| 6900 \| 0.0001 \| - \|
	\| 1.4825 \| 6950 \| 0.0001 \| - \|
	\| 1.4932 \| 7000 \| 0.0 \| - \|
	\| 1.5038 \| 7050 \| 0.0 \| - \|
	\| 1.5145 \| 7100 \| 0.0001 \| - \|
	\| 1.5252 \| 7150 \| 0.0001 \| - \|
	\| 1.5358 \| 7200 \| 0.0001 \| - \|
	\| 1.5465 \| 7250 \| 0.0001 \| - \|
	\| 1.5572 \| 7300 \| 0.0 \| - \|
	\| 1.5678 \| 7350 \| 0.0 \| - \|
	\| 1.5785 \| 7400 \| 0.0 \| - \|
	\| 1.5892 \| 7450 \| 0.0001 \| - \|
	\| 1.5998 \| 7500 \| 0.0 \| - \|
	\| 1.6105 \| 7550 \| 0.0 \| - \|
	\| 1.6212 \| 7600 \| 0.0 \| - \|
	\| 1.6318 \| 7650 \| 0.0 \| - \|
	\| 1.6425 \| 7700 \| 0.0 \| - \|
	\| 1.6532 \| 7750 \| 0.0 \| - \|
	\| 1.6638 \| 7800 \| 0.0 \| - \|
	\| 1.6745 \| 7850 \| 0.0 \| - \|
	\| 1.6852 \| 7900 \| 0.0 \| - \|
	\| 1.6958 \| 7950 \| 0.0 \| - \|
	\| 1.7065 \| 8000 \| 0.0 \| - \|
	\| 1.7172 \| 8050 \| 0.0 \| - \|
	\| 1.7278 \| 8100 \| 0.0 \| - \|
	\| 1.7385 \| 8150 \| 0.0001 \| - \|
	\| 1.7491 \| 8200 \| 0.0 \| - \|
	\| 1.7598 \| 8250 \| 0.0 \| - \|
	\| 1.7705 \| 8300 \| 0.0 \| - \|
	\| 1.7811 \| 8350 \| 0.0001 \| - \|
	\| 1.7918 \| 8400 \| 0.0 \| - \|
	\| 1.8025 \| 8450 \| 0.0 \| - \|
	\| 1.8131 \| 8500 \| 0.0 \| - \|
	\| 1.8238 \| 8550 \| 0.0 \| - \|
	\| 1.8345 \| 8600 \| 0.0001 \| - \|
	\| 1.8451 \| 8650 \| 0.0 \| - \|
	\| 1.8558 \| 8700 \| 0.0 \| - \|
	\| 1.8665 \| 8750 \| 0.0001 \| - \|
	\| 1.8771 \| 8800 \| 0.0 \| - \|
	\| 1.8878 \| 8850 \| 0.0 \| - \|
	\| 1.8985 \| 8900 \| 0.0 \| - \|
	\| 1.9091 \| 8950 \| 0.0001 \| - \|
	\| 1.9198 \| 9000 \| 0.0 \| - \|
	\| 1.9305 \| 9050 \| 0.0 \| - \|
	\| 1.9411 \| 9100 \| 0.0 \| - \|
	\| 1.9518 \| 9150 \| 0.0 \| - \|
	\| 1.9625 \| 9200 \| 0.0 \| - \|
	\| 1.9731 \| 9250 \| 0.0 \| - \|
	\| 1.9838 \| 9300 \| 0.0 \| - \|
	\| 1.9945 \| 9350 \| 0.0 \| - \|

	### Framework Versions
	- Python: 3.10.16
	- SetFit: 1.0.3
	- Sentence Transformers: 2.7.0
	- Transformers: 4.40.2
	- PyTorch: 2.2.2
	- Datasets: 2.19.1
	- Tokenizers: 0.19.1

	## 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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