Add SetFit model

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	---
	library_name: setfit
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	base_model: sentence-transformers/paraphrase-mpnet-base-v2
	metrics:
	- accuracy
	widget:
	- text: What is the price of the organic honey?
	- text: Variety of cookie boxes
	- text: Is the Popcorn Box available in a pack of 50?
	- text: What is the price range for the sugarfree chocolate heart sugarfree chocolate
	box pack of 5?
	- text: Do you have the Off-White x Air Jordan 2 Retro Low SP Black Varsity Royal
	in size 10?
	pipeline_tag: text-classification
	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: 0.8533333333333334
	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: 5 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 faq \| <ul><li>'Does the Meenakari jal jangla -Rani saree have meenakari?'</li><li>'Is the Nike Dunk Low Premium Bacon available in size 7?'</li><li>'What is the best way to recycle the packaging boxes for wholesale orders for wholesale orders?'</li></ul> \|
	\| order tracking \| <ul><li>'I ordered the Cake Boards 7 days ago with order no 43210 how long 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 want to deliver packaging to Surat, how many days will it take to deliver?'</li></ul> \|
	\| product policy \| <ul><li>'What is the procedure for returning a product that was part of a special promotion occasion?'</li><li>'Can I return an item if it was damaged during delivery preparation?'</li><li>'What is the procedure for returning a product that was part of a special occasion promotion?'</li></ul> \|
	\| general faq \| <ul><li>'What are the key factors to consider when developing a personalized diet plan for weight loss?'</li><li>'What are some tips for maximizing the antioxidant content when brewing green tea?'</li><li>'Can you explain why Mashru silk is considered more comfortable to wear compared to pure silk sarees?'</li></ul> \|
	\| product discoverability \| <ul><li>'Can you show me sarees in bright colors suitable for weddings?'</li><li>'Do you have adidas Superstar shoes?'</li><li>'Do you have any bestseller teas available?'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.8533 \|

	## 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("Shankhdhar/classifier_woog_firstbud")
	# Run inference
	preds = model("Variety of cookie boxes")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:--------\|:----\|
	\| Word count \| 4 \| 12.1961 \| 28 \|

	\| Label \| Training Sample Count \|
	\|:------------------------\|:----------------------\|
	\| general faq \| 24 \|
	\| order tracking \| 32 \|
	\| product discoverability \| 50 \|
	\| product faq \| 50 \|
	\| product policy \| 48 \|

	### 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 \|
	\|:------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0005 \| 1 \| 0.2265 \| - \|
	\| 0.0244 \| 50 \| 0.1831 \| - \|
	\| 0.0489 \| 100 \| 0.1876 \| - \|
	\| 0.0733 \| 150 \| 0.1221 \| - \|
	\| 0.0978 \| 200 \| 0.0228 \| - \|
	\| 0.1222 \| 250 \| 0.0072 \| - \|
	\| 0.1467 \| 300 \| 0.0282 \| - \|
	\| 0.1711 \| 350 \| 0.0015 \| - \|
	\| 0.1956 \| 400 \| 0.0005 \| - \|
	\| 0.2200 \| 450 \| 0.0008 \| - \|
	\| 0.2445 \| 500 \| 0.0004 \| - \|
	\| 0.2689 \| 550 \| 0.0003 \| - \|
	\| 0.2934 \| 600 \| 0.0003 \| - \|
	\| 0.3178 \| 650 \| 0.0002 \| - \|
	\| 0.3423 \| 700 \| 0.0002 \| - \|
	\| 0.3667 \| 750 \| 0.0002 \| - \|
	\| 0.3912 \| 800 \| 0.0003 \| - \|
	\| 0.4156 \| 850 \| 0.0002 \| - \|
	\| 0.4401 \| 900 \| 0.0002 \| - \|
	\| 0.4645 \| 950 \| 0.0001 \| - \|
	\| 0.4890 \| 1000 \| 0.0001 \| - \|
	\| 0.5134 \| 1050 \| 0.0001 \| - \|
	\| 0.5379 \| 1100 \| 0.0001 \| - \|
	\| 0.5623 \| 1150 \| 0.0002 \| - \|
	\| 0.5868 \| 1200 \| 0.0002 \| - \|
	\| 0.6112 \| 1250 \| 0.0001 \| - \|
	\| 0.6357 \| 1300 \| 0.0001 \| - \|
	\| 0.6601 \| 1350 \| 0.0001 \| - \|
	\| 0.6846 \| 1400 \| 0.0001 \| - \|
	\| 0.7090 \| 1450 \| 0.0001 \| - \|
	\| 0.7335 \| 1500 \| 0.0001 \| - \|
	\| 0.7579 \| 1550 \| 0.0001 \| - \|
	\| 0.7824 \| 1600 \| 0.0001 \| - \|
	\| 0.8068 \| 1650 \| 0.0001 \| - \|
	\| 0.8313 \| 1700 \| 0.0001 \| - \|
	\| 0.8557 \| 1750 \| 0.0011 \| - \|
	\| 0.8802 \| 1800 \| 0.0002 \| - \|
	\| 0.9046 \| 1850 \| 0.0001 \| - \|
	\| 0.9291 \| 1900 \| 0.0001 \| - \|
	\| 0.9535 \| 1950 \| 0.0002 \| - \|
	\| 0.9780 \| 2000 \| 0.0001 \| - \|
	\| 1.0024 \| 2050 \| 0.0001 \| - \|
	\| 1.0269 \| 2100 \| 0.0002 \| - \|
	\| 1.0513 \| 2150 \| 0.0001 \| - \|
	\| 1.0758 \| 2200 \| 0.0001 \| - \|
	\| 1.1002 \| 2250 \| 0.0001 \| - \|
	\| 1.1247 \| 2300 \| 0.0001 \| - \|
	\| 1.1491 \| 2350 \| 0.0001 \| - \|
	\| 1.1736 \| 2400 \| 0.0001 \| - \|
	\| 1.1980 \| 2450 \| 0.0001 \| - \|
	\| 1.2225 \| 2500 \| 0.0001 \| - \|
	\| 1.2469 \| 2550 \| 0.0001 \| - \|
	\| 1.2714 \| 2600 \| 0.0001 \| - \|
	\| 1.2958 \| 2650 \| 0.0001 \| - \|
	\| 1.3203 \| 2700 \| 0.0001 \| - \|
	\| 1.3447 \| 2750 \| 0.0001 \| - \|
	\| 1.3692 \| 2800 \| 0.0001 \| - \|
	\| 1.3936 \| 2850 \| 0.0001 \| - \|
	\| 1.4181 \| 2900 \| 0.0001 \| - \|
	\| 1.4425 \| 2950 \| 0.0001 \| - \|
	\| 1.4670 \| 3000 \| 0.0001 \| - \|
	\| 1.4914 \| 3050 \| 0.0001 \| - \|
	\| 1.5159 \| 3100 \| 0.0001 \| - \|
	\| 1.5403 \| 3150 \| 0.0001 \| - \|
	\| 1.5648 \| 3200 \| 0.0001 \| - \|
	\| 1.5892 \| 3250 \| 0.0001 \| - \|
	\| 1.6137 \| 3300 \| 0.0001 \| - \|
	\| 1.6381 \| 3350 \| 0.0001 \| - \|
	\| 1.6626 \| 3400 \| 0.0001 \| - \|
	\| 1.6870 \| 3450 \| 0.0001 \| - \|
	\| 1.7115 \| 3500 \| 0.0001 \| - \|
	\| 1.7359 \| 3550 \| 0.0 \| - \|
	\| 1.7604 \| 3600 \| 0.0001 \| - \|
	\| 1.7848 \| 3650 \| 0.0001 \| - \|
	\| 1.8093 \| 3700 \| 0.0001 \| - \|
	\| 1.8337 \| 3750 \| 0.0 \| - \|
	\| 1.8582 \| 3800 \| 0.0001 \| - \|
	\| 1.8826 \| 3850 \| 0.0001 \| - \|
	\| 1.9071 \| 3900 \| 0.0001 \| - \|
	\| 1.9315 \| 3950 \| 0.0 \| - \|
	\| 1.9560 \| 4000 \| 0.0 \| - \|
	\| 1.9804 \| 4050 \| 0.0001 \| - \|

	### Framework Versions
	- Python: 3.10.13
	- SetFit: 1.0.3
	- Sentence Transformers: 3.0.1
	- Transformers: 4.39.0
	- PyTorch: 2.2.2+cu121
	- Datasets: 2.19.2
	- 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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