job_level_model / README.md

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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: Proof Reader
	- text: product owner
	- text: chief community officer
	- text: planner
	- text: information technology administrator
	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: 1.0
	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: 4 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 \|
	\|:------\|:---------------------------------------------------------------------------------------------------------------\|
	\| 3 \| <ul><li>'academic head'</li><li>'admin director'</li><li>'admin head'</li></ul> \|
	\| 4 \| <ul><li>'account director'</li><li>'area vice president'</li><li>'assistant chief executive officer'</li></ul> \|
	\| 2 \| <ul><li>'account manager'</li><li>'admin'</li><li>'admin officer'</li></ul> \|
	\| 1 \| <ul><li>'accountant'</li><li>'administrator'</li><li>'adviser'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 1.0 \|

	## 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("planner")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 1 \| 2.1124 \| 6 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 1 \| 380 \|
	\| 2 \| 107 \|
	\| 3 \| 67 \|
	\| 4 \| 193 \|

	### Training Hyperparameters
	- batch_size: (16, 16)
	- num_epochs: (3, 3)
	- 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
	- seed: 42
	- eval_max_steps: -1
	- load_best_model_at_end: False

	### Training Results
	\| Epoch \| Step \| Training Loss \| Validation Loss \|
	\|:------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0005 \| 1 \| 0.2621 \| - \|
	\| 0.0268 \| 50 \| 0.2631 \| - \|
	\| 0.0535 \| 100 \| 0.2043 \| - \|
	\| 0.0803 \| 150 \| 0.1561 \| - \|
	\| 0.1071 \| 200 \| 0.203 \| - \|
	\| 0.1338 \| 250 \| 0.1823 \| - \|
	\| 0.1606 \| 300 \| 0.1082 \| - \|
	\| 0.1874 \| 350 \| 0.0702 \| - \|
	\| 0.2141 \| 400 \| 0.1159 \| - \|
	\| 0.2409 \| 450 \| 0.0532 \| - \|
	\| 0.2677 \| 500 \| 0.0767 \| - \|
	\| 0.2944 \| 550 \| 0.0965 \| - \|
	\| 0.3212 \| 600 \| 0.0479 \| - \|
	\| 0.3480 \| 650 \| 0.0353 \| - \|
	\| 0.3747 \| 700 \| 0.0235 \| - \|
	\| 0.4015 \| 750 \| 0.0028 \| - \|
	\| 0.4283 \| 800 \| 0.004 \| - \|
	\| 0.4550 \| 850 \| 0.0908 \| - \|
	\| 0.4818 \| 900 \| 0.0078 \| - \|
	\| 0.5086 \| 950 \| 0.0149 \| - \|
	\| 0.5353 \| 1000 \| 0.0841 \| - \|
	\| 0.5621 \| 1050 \| 0.0141 \| - \|
	\| 0.5889 \| 1100 \| 0.0328 \| - \|
	\| 0.6156 \| 1150 \| 0.0031 \| - \|
	\| 0.6424 \| 1200 \| 0.0027 \| - \|
	\| 0.6692 \| 1250 \| 0.0205 \| - \|
	\| 0.6959 \| 1300 \| 0.0584 \| - \|
	\| 0.7227 \| 1350 \| 0.002 \| - \|
	\| 0.7495 \| 1400 \| 0.0009 \| - \|
	\| 0.7762 \| 1450 \| 0.0018 \| - \|
	\| 0.8030 \| 1500 \| 0.001 \| - \|
	\| 0.8298 \| 1550 \| 0.0004 \| - \|
	\| 0.8565 \| 1600 \| 0.0008 \| - \|
	\| 0.8833 \| 1650 \| 0.0006 \| - \|
	\| 0.9101 \| 1700 \| 0.0021 \| - \|
	\| 0.9368 \| 1750 \| 0.009 \| - \|
	\| 0.9636 \| 1800 \| 0.0031 \| - \|
	\| 0.9904 \| 1850 \| 0.0024 \| - \|
	\| 1.0171 \| 1900 \| 0.0327 \| - \|
	\| 1.0439 \| 1950 \| 0.0257 \| - \|
	\| 1.0707 \| 2000 \| 0.0006 \| - \|
	\| 1.0974 \| 2050 \| 0.0009 \| - \|
	\| 1.1242 \| 2100 \| 0.0006 \| - \|
	\| 1.1510 \| 2150 \| 0.0004 \| - \|
	\| 1.1777 \| 2200 \| 0.0011 \| - \|
	\| 1.2045 \| 2250 \| 0.0004 \| - \|
	\| 1.2313 \| 2300 \| 0.0012 \| - \|
	\| 1.2580 \| 2350 \| 0.0005 \| - \|
	\| 1.2848 \| 2400 \| 0.0013 \| - \|
	\| 1.3116 \| 2450 \| 0.0007 \| - \|
	\| 1.3383 \| 2500 \| 0.0002 \| - \|
	\| 1.3651 \| 2550 \| 0.0005 \| - \|
	\| 1.3919 \| 2600 \| 0.0006 \| - \|
	\| 1.4186 \| 2650 \| 0.0006 \| - \|
	\| 1.4454 \| 2700 \| 0.0004 \| - \|
	\| 1.4722 \| 2750 \| 0.0004 \| - \|
	\| 1.4989 \| 2800 \| 0.0008 \| - \|
	\| 1.5257 \| 2850 \| 0.0003 \| - \|
	\| 1.5525 \| 2900 \| 0.0012 \| - \|
	\| 1.5792 \| 2950 \| 0.0006 \| - \|
	\| 1.6060 \| 3000 \| 0.0003 \| - \|
	\| 1.6328 \| 3050 \| 0.0002 \| - \|
	\| 1.6595 \| 3100 \| 0.0026 \| - \|
	\| 1.6863 \| 3150 \| 0.0003 \| - \|
	\| 1.7131 \| 3200 \| 0.0003 \| - \|
	\| 1.7398 \| 3250 \| 0.0003 \| - \|
	\| 1.7666 \| 3300 \| 0.0003 \| - \|
	\| 1.7934 \| 3350 \| 0.0003 \| - \|
	\| 1.8201 \| 3400 \| 0.0004 \| - \|
	\| 1.8469 \| 3450 \| 0.0003 \| - \|
	\| 1.8737 \| 3500 \| 0.0005 \| - \|
	\| 1.9004 \| 3550 \| 0.0003 \| - \|
	\| 1.9272 \| 3600 \| 0.0003 \| - \|
	\| 1.9540 \| 3650 \| 0.0002 \| - \|
	\| 1.9807 \| 3700 \| 0.0003 \| - \|
	\| 2.0075 \| 3750 \| 0.0003 \| - \|
	\| 2.0343 \| 3800 \| 0.0003 \| - \|
	\| 2.0610 \| 3850 \| 0.0002 \| - \|
	\| 2.0878 \| 3900 \| 0.0004 \| - \|
	\| 2.1146 \| 3950 \| 0.0003 \| - \|
	\| 2.1413 \| 4000 \| 0.0003 \| - \|
	\| 2.1681 \| 4050 \| 0.0002 \| - \|
	\| 2.1949 \| 4100 \| 0.0541 \| - \|
	\| 2.2216 \| 4150 \| 0.0002 \| - \|
	\| 2.2484 \| 4200 \| 0.0003 \| - \|
	\| 2.2752 \| 4250 \| 0.0582 \| - \|
	\| 2.3019 \| 4300 \| 0.0003 \| - \|
	\| 2.3287 \| 4350 \| 0.0002 \| - \|
	\| 2.3555 \| 4400 \| 0.0003 \| - \|
	\| 2.3822 \| 4450 \| 0.0005 \| - \|
	\| 2.4090 \| 4500 \| 0.0004 \| - \|
	\| 2.4358 \| 4550 \| 0.0003 \| - \|
	\| 2.4625 \| 4600 \| 0.0003 \| - \|
	\| 2.4893 \| 4650 \| 0.0002 \| - \|
	\| 2.5161 \| 4700 \| 0.0002 \| - \|
	\| 2.5428 \| 4750 \| 0.0003 \| - \|
	\| 2.5696 \| 4800 \| 0.0008 \| - \|
	\| 2.5964 \| 4850 \| 0.0002 \| - \|
	\| 2.6231 \| 4900 \| 0.0002 \| - \|
	\| 2.6499 \| 4950 \| 0.0005 \| - \|
	\| 2.6767 \| 5000 \| 0.0003 \| - \|
	\| 2.7034 \| 5050 \| 0.0002 \| - \|
	\| 2.7302 \| 5100 \| 0.0004 \| - \|
	\| 2.7570 \| 5150 \| 0.0002 \| - \|
	\| 2.7837 \| 5200 \| 0.0005 \| - \|
	\| 2.8105 \| 5250 \| 0.0004 \| - \|
	\| 2.8373 \| 5300 \| 0.0394 \| - \|
	\| 2.8640 \| 5350 \| 0.0002 \| - \|
	\| 2.8908 \| 5400 \| 0.0399 \| - \|
	\| 2.9176 \| 5450 \| 0.0002 \| - \|
	\| 2.9443 \| 5500 \| 0.0002 \| - \|
	\| 2.9711 \| 5550 \| 0.0002 \| - \|
	\| 2.9979 \| 5600 \| 0.0002 \| - \|

	### Framework Versions
	- Python: 3.10.12
	- SetFit: 1.0.3
	- Sentence Transformers: 3.0.1
	- Transformers: 4.39.0
	- PyTorch: 2.3.1+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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