lifestyle_disease_classifier / README.md

Add SetFit model

6241c2e verified 10 months ago

9.92 kB

	---
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: "most of the results look perfectly healthy, but there are a few that are\
	\ over thresholds, they are: \n\n "
	- text: 'so here''s my question: is it possible to have a very slow natural breathing
	rate and be healthy?'
	- text: 'never had an issue with reflux before, i eat very healthy....but gave it
	a go. '
	- text: does every other person at their healthy weight range feel like this all the
	time?
	- text: penis overall just looks very unhealthy compared to last year and i have no
	idea what it could be and everywhere i’ve looked suggest it is penile cancer.
	metrics:
	- accuracy
	- precision
	- recall
	- f1
	pipeline_tag: text-classification
	library_name: setfit
	inference: true
	base_model: sentence-transformers/paraphrase-mpnet-base-v2
	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: 0.9411764705882353
	name: Accuracy
	- type: precision
	value: 0.9411764705882353
	name: Precision
	- type: recall
	value: 0.9411764705882353
	name: Recall
	- type: f1
	value: 0.9411764705882353
	name: F1
	---

	# 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: 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 \|
	\|:----------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| lifestyle \| <ul><li>'i am 21, live a healthy lifestyle, i don’t smoke and only drink socially every once in a while.'</li><li>'i know staying up all night and sleeping during the day isnt good for you, brain wise and hormonaly, i will try my best to eat healthy and have good sleep hygiene, but am i risking my health or anything ?'</li><li>'i have been eating a bit more unhealthy foods like fried foods.\n\n'</li></ul> \|
	\| disease \| <ul><li>'i was told there’s no way to know what caused it & no treatment options or ways to help fix it besides med options to help manage symptoms but my doc doesn’t want to start that yet due to me being “young & healthy”.'</li><li>"i gave the whole history because i've been very ill like this for 6 years now after being healthy."</li><li>'no baseline medical information included, so the following assumes you are healthy.'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \| Precision \| Recall \| F1 \|
	\|:--------\|:---------\|:----------\|:-------\|:-------\|
	\| all \| 0.9412 \| 0.9412 \| 0.9412 \| 0.9412 \|

	## 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("never had an issue with reflux before, i eat very healthy....but gave it a go. ")
	```

	<!--
	### Downstream Use

	List how someone could finetune this model on their own dataset.
	-->

	<!--
	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
	-->

	<!--
	## Bias, Risks and Limitations

	What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.
	-->

	<!--
	### Recommendations

	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
	-->

	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:--------\|:----\|
	\| Word count \| 12 \| 25.8308 \| 60 \|

	\| Label \| Training Sample Count \|
	\|:----------\|:----------------------\|
	\| disease \| 30 \|
	\| lifestyle \| 35 \|

	### Training Hyperparameters
	- batch_size: (16, 16)
	- num_epochs: (10, 10)
	- max_steps: -1
	- sampling_strategy: oversampling
	- num_iterations: 20
	- 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
	- l2_weight: 0.01
	- seed: 3786
	- eval_max_steps: -1
	- load_best_model_at_end: False

	### Training Results
	\| Epoch \| Step \| Training Loss \| Validation Loss \|
	\|:------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0061 \| 1 \| 0.2143 \| - \|
	\| 0.3067 \| 50 \| 0.2243 \| - \|
	\| 0.6135 \| 100 \| 0.0812 \| - \|
	\| 0.9202 \| 150 \| 0.0019 \| - \|
	\| 1.2270 \| 200 \| 0.0003 \| - \|
	\| 1.5337 \| 250 \| 0.0002 \| - \|
	\| 1.8405 \| 300 \| 0.0002 \| - \|
	\| 2.1472 \| 350 \| 0.0001 \| - \|
	\| 2.4540 \| 400 \| 0.0001 \| - \|
	\| 2.7607 \| 450 \| 0.0001 \| - \|
	\| 3.0675 \| 500 \| 0.0001 \| - \|
	\| 3.3742 \| 550 \| 0.0001 \| - \|
	\| 3.6810 \| 600 \| 0.0001 \| - \|
	\| 3.9877 \| 650 \| 0.0001 \| - \|
	\| 4.2945 \| 700 \| 0.0001 \| - \|
	\| 4.6012 \| 750 \| 0.0001 \| - \|
	\| 4.9080 \| 800 \| 0.0001 \| - \|
	\| 5.2147 \| 850 \| 0.0001 \| - \|
	\| 5.5215 \| 900 \| 0.0001 \| - \|
	\| 5.8282 \| 950 \| 0.0001 \| - \|
	\| 6.1350 \| 1000 \| 0.0 \| - \|
	\| 6.4417 \| 1050 \| 0.0 \| - \|
	\| 6.7485 \| 1100 \| 0.0 \| - \|
	\| 7.0552 \| 1150 \| 0.0 \| - \|
	\| 7.3620 \| 1200 \| 0.0 \| - \|
	\| 7.6687 \| 1250 \| 0.0 \| - \|
	\| 7.9755 \| 1300 \| 0.0 \| - \|
	\| 8.2822 \| 1350 \| 0.0 \| - \|
	\| 8.5890 \| 1400 \| 0.0 \| - \|
	\| 8.8957 \| 1450 \| 0.0 \| - \|
	\| 9.2025 \| 1500 \| 0.0 \| - \|
	\| 9.5092 \| 1550 \| 0.0 \| - \|
	\| 9.8160 \| 1600 \| 0.0 \| - \|

	### Framework Versions
	- Python: 3.11.7
	- SetFit: 1.1.1
	- Sentence Transformers: 3.3.1
	- Transformers: 4.47.1
	- PyTorch: 2.5.1
	- Datasets: 3.2.0
	- Tokenizers: 0.21.0

	## 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}
	}
	```

	<!--
	## Glossary

	Clearly define terms in order to be accessible across audiences.
	-->

	<!--
	## Model Card Authors

	Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.
	-->

	<!--
	## Model Card Contact

	Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.
	-->