Add SetFit model

3ef6f00 verified 9 days ago

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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 makeup products do you have for eyes?
	- text: How can I prevent acne if I have oily skin?
	- text: What is the estimated delivery time for orders within the same country?
	- text: Can you recommend a good moisturizer for winter skin care?
	- text: Is the Beachy-Floral-Citrus Mini Eau De Parfum Gift Set suitable for all skin
	types?
	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: 0.9166666666666666
	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 discoverability \| <ul><li>'Can you show me all the products for oily skin?'</li><li>'Do you have any makeup remover?'</li><li>'Can you show me all the products for dark spots?'</li></ul> \|
	\| order tracking \| <ul><li>'What is the estimated delivery time for orders within the same state?'</li><li>'I need to know the status of my recent order. Can you check if it has been dispatched?'</li><li>'I ordered the Cake Decorating Kit 4 days ago, can you provide the tracking information?'</li></ul> \|
	\| product faq \| <ul><li>'What are the different shades available in the Color Affair Nail Polish Pixie Dust Collection?'</li><li>'Is the Touch-N-Go Lip & Cheek Tint a vegan and cruelty-free product?'</li><li>'Is this product suitable for oily skin?'</li></ul> \|
	\| general faq \| <ul><li>'How often should I use exfoliants to reduce open pores?'</li><li>'What are the most effective ingredients for treating acne?'</li><li>'Are home remedies effective for severe acne?'</li></ul> \|
	\| product policy \| <ul><li>'Are your products suitable for sensitive skin?'</li><li>'How can I track my order on the Plum Goodness app?'</li><li>'What is the contact number for customer support?'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.9167 \|

	## 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("What makeup products do you have for eyes?")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 4 \| 11.0 \| 24 \|

	\| Label \| Training Sample Count \|
	\|:------------------------\|:----------------------\|
	\| general faq \| 20 \|
	\| order tracking \| 24 \|
	\| product discoverability \| 16 \|
	\| product faq \| 24 \|
	\| product policy \| 12 \|

	### 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 \|
	\|:------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0022 \| 1 \| 0.2082 \| - \|
	\| 0.1101 \| 50 \| 0.1229 \| - \|
	\| 0.2203 \| 100 \| 0.0262 \| - \|
	\| 0.3304 \| 150 \| 0.0015 \| - \|
	\| 0.4405 \| 200 \| 0.001 \| - \|
	\| 0.5507 \| 250 \| 0.0008 \| - \|
	\| 0.6608 \| 300 \| 0.0005 \| - \|
	\| 0.7709 \| 350 \| 0.0004 \| - \|
	\| 0.8811 \| 400 \| 0.0003 \| - \|
	\| 0.9912 \| 450 \| 0.0003 \| - \|
	\| 1.1013 \| 500 \| 0.0002 \| - \|
	\| 1.2115 \| 550 \| 0.0002 \| - \|
	\| 1.3216 \| 600 \| 0.0004 \| - \|
	\| 1.4317 \| 650 \| 0.0002 \| - \|
	\| 1.5419 \| 700 \| 0.0003 \| - \|
	\| 1.6520 \| 750 \| 0.0002 \| - \|
	\| 1.7621 \| 800 \| 0.0002 \| - \|
	\| 1.8722 \| 850 \| 0.0002 \| - \|
	\| 1.9824 \| 900 \| 0.0003 \| - \|

	### Framework Versions
	- Python: 3.9.19
	- 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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