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	---
	library_name: setfit
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	metrics:
	- accuracy
	- f1
	- precision
	- recall
	widget:
	- text: it's not enough that product is integrating brand in product search results
	but is also looking to add it to product, word and outlook. this could be transformative
	for productivity at work in the future if it works! product could be under siege
	soon!
	- text: 'Copilot in Windows 11 is a game changer!! Here is a list of things it can
	do: It can answer your questions in natural language. It can summarize content
	to give you a brief overview It can adjust your PCs settings It can help troubleshoot
	issues. 1/2'
	- text: 1/2 Hello Clif! He didn't want to use ChatGPT, its data or openai. Hes using
	the French LLM Mistral and currently training it on his own data articles/books
	he personally published, and hes been requesting book publishers permission to
	use their books
	- text: 'Protecting data in the era of generative AI: Nightfall AI launches innovative
	security platform dlvr.it/StD9vP'
	- text: All I want from my Mac is GODDAM DROPDOWN MENUS Please stop with the icons.
	Im talking to you, Apple, and PARTICULARLY to you, Microsoft Word. Death to thy
	ribbon, and be damned
	pipeline_tag: text-classification
	inference: true
	base_model: BAAI/bge-base-en-v1.5
	model-index:
	- name: SetFit with BAAI/bge-base-en-v1.5
	results:
	- task:
	type: text-classification
	name: Text Classification
	dataset:
	name: Unknown
	type: unknown
	split: test
	metrics:
	- type: accuracy
	value: 0.7915057915057915
	name: Accuracy
	- type: f1
	value:
	- 0.3720930232558139
	- 0.4615384615384615
	- 0.8747044917257684
	name: F1
	- type: precision
	value:
	- 0.23529411764705882
	- 0.3076923076923077
	- 0.9946236559139785
	name: Precision
	- type: recall
	value:
	- 0.8888888888888888
	- 0.9230769230769231
	- 0.7805907172995781
	name: Recall
	---

	# SetFit with BAAI/bge-base-en-v1.5

	This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5) 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: [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5)
	- 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: 3 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 \|
	\|:--------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| neither \| <ul><li>'product cloud fails to cash in on product - as enterprises optimize cloud spending, product has registered its slowest growth in three years.'</li><li>'what do those things have to do with product? and its funny youre trying to argue facts by bringing your god into this.'</li><li>'your question didn\'t mean what you think it meant. it answered correctly to your question, which i also read as "hey brand, can you forget my loved ones?"'</li></ul> \|
	\| peak \| <ul><li>'chatbrandandme product brand product dang, my product msftadvertising experience is already so smooth and satisfying wow. they even gave me a free landing page for my product and product. i love msftadvertising and product for buying out brand and making gpt my best friend even more'</li><li>'i asked my physics teacher for help on a question i didnt understand on a test and she sent me back a 5 slide product with audio explaining each part of the question. she 100% is my fav teacher now.'</li><li>'brand!! it helped me finish my resume. i just asked it if it could write my resume based on horribly written descriptions i came up with. and it made it all pretty:)'</li></ul> \|
	\| pit \| <ul><li>'do not upgrade to product, it is a complete joke of an operating system. all of my xproduct programs are broken, none of my gpus work correctly, even after checking the bios and drivers, and now file explorer crashes upon startup, basically locking up the whole computer!'</li><li>'yes, and it would be great if product stops changing the format of data from other sources automatically, that is really annoying when 10-1-2 becomes "magically and wrongly" 2010/01/02. we are in the age of data and product just cannot handle them well..'</li><li>'it\'s a pity that the product doesn\'t work such as the "normal chat" does, but with 18,000 chars lim. hopefully, the will aim to make such upgrade, although more memory costly.'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \| F1 \| Precision \| Recall \|
	\|:--------\|:---------\|:-------------------------------------------------------------\|:--------------------------------------------------------------\|:-------------------------------------------------------------\|
	\| all \| 0.7915 \| [0.3720930232558139, 0.4615384615384615, 0.8747044917257684] \| [0.23529411764705882, 0.3076923076923077, 0.9946236559139785] \| [0.8888888888888888, 0.9230769230769231, 0.7805907172995781] \|

	## 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("tjmooney98/725_tm-setfit-bge-base-en-v1.5")
	# Run inference
	preds = model("Protecting data in the era of generative AI: Nightfall AI launches innovative security platform dlvr.it/StD9vP")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:--------\|:----\|
	\| Word count \| 9 \| 37.1711 \| 98 \|

	\| Label \| Training Sample Count \|
	\|:--------\|:----------------------\|
	\| pit \| 150 \|
	\| peak \| 150 \|
	\| neither \| 150 \|

	### Training Hyperparameters
	- batch_size: (32, 32)
	- num_epochs: (1, 1)
	- 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: False

	### Training Results
	\| Epoch \| Step \| Training Loss \| Validation Loss \|
	\|:------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0002 \| 1 \| 0.2384 \| - \|
	\| 0.0119 \| 50 \| 0.2399 \| - \|
	\| 0.0237 \| 100 \| 0.2136 \| - \|
	\| 0.0356 \| 150 \| 0.1323 \| - \|
	\| 0.0474 \| 200 \| 0.0703 \| - \|
	\| 0.0593 \| 250 \| 0.01 \| - \|
	\| 0.0711 \| 300 \| 0.0063 \| - \|
	\| 0.0830 \| 350 \| 0.0028 \| - \|
	\| 0.0948 \| 400 \| 0.0026 \| - \|
	\| 0.1067 \| 450 \| 0.0021 \| - \|
	\| 0.1185 \| 500 \| 0.0018 \| - \|
	\| 0.1304 \| 550 \| 0.0016 \| - \|
	\| 0.1422 \| 600 \| 0.0014 \| - \|
	\| 0.1541 \| 650 \| 0.0015 \| - \|
	\| 0.1659 \| 700 \| 0.0013 \| - \|
	\| 0.1778 \| 750 \| 0.0012 \| - \|
	\| 0.1896 \| 800 \| 0.0012 \| - \|
	\| 0.2015 \| 850 \| 0.0012 \| - \|
	\| 0.2133 \| 900 \| 0.0011 \| - \|
	\| 0.2252 \| 950 \| 0.0011 \| - \|
	\| 0.2370 \| 1000 \| 0.0009 \| - \|
	\| 0.2489 \| 1050 \| 0.001 \| - \|
	\| 0.2607 \| 1100 \| 0.0009 \| - \|
	\| 0.2726 \| 1150 \| 0.0008 \| - \|
	\| 0.2844 \| 1200 \| 0.0008 \| - \|
	\| 0.2963 \| 1250 \| 0.0009 \| - \|
	\| 0.3081 \| 1300 \| 0.0008 \| - \|
	\| 0.3200 \| 1350 \| 0.0007 \| - \|
	\| 0.3318 \| 1400 \| 0.0007 \| - \|
	\| 0.3437 \| 1450 \| 0.0007 \| - \|
	\| 0.3555 \| 1500 \| 0.0006 \| - \|
	\| 0.3674 \| 1550 \| 0.0007 \| - \|
	\| 0.3792 \| 1600 \| 0.0007 \| - \|
	\| 0.3911 \| 1650 \| 0.0008 \| - \|
	\| 0.4029 \| 1700 \| 0.0006 \| - \|
	\| 0.4148 \| 1750 \| 0.0006 \| - \|
	\| 0.4266 \| 1800 \| 0.0006 \| - \|
	\| 0.4385 \| 1850 \| 0.0006 \| - \|
	\| 0.4503 \| 1900 \| 0.0006 \| - \|
	\| 0.4622 \| 1950 \| 0.0006 \| - \|
	\| 0.4740 \| 2000 \| 0.0006 \| - \|
	\| 0.4859 \| 2050 \| 0.0005 \| - \|
	\| 0.4977 \| 2100 \| 0.0006 \| - \|
	\| 0.5096 \| 2150 \| 0.0006 \| - \|
	\| 0.5215 \| 2200 \| 0.0005 \| - \|
	\| 0.5333 \| 2250 \| 0.0005 \| - \|
	\| 0.5452 \| 2300 \| 0.0005 \| - \|
	\| 0.5570 \| 2350 \| 0.0006 \| - \|
	\| 0.5689 \| 2400 \| 0.0005 \| - \|
	\| 0.5807 \| 2450 \| 0.0005 \| - \|
	\| 0.5926 \| 2500 \| 0.0006 \| - \|
	\| 0.6044 \| 2550 \| 0.0006 \| - \|
	\| 0.6163 \| 2600 \| 0.0005 \| - \|
	\| 0.6281 \| 2650 \| 0.0005 \| - \|
	\| 0.6400 \| 2700 \| 0.0005 \| - \|
	\| 0.6518 \| 2750 \| 0.0005 \| - \|
	\| 0.6637 \| 2800 \| 0.0005 \| - \|
	\| 0.6755 \| 2850 \| 0.0005 \| - \|
	\| 0.6874 \| 2900 \| 0.0005 \| - \|
	\| 0.6992 \| 2950 \| 0.0004 \| - \|
	\| 0.7111 \| 3000 \| 0.0004 \| - \|
	\| 0.7229 \| 3050 \| 0.0004 \| - \|
	\| 0.7348 \| 3100 \| 0.0005 \| - \|
	\| 0.7466 \| 3150 \| 0.0005 \| - \|
	\| 0.7585 \| 3200 \| 0.0005 \| - \|
	\| 0.7703 \| 3250 \| 0.0004 \| - \|
	\| 0.7822 \| 3300 \| 0.0004 \| - \|
	\| 0.7940 \| 3350 \| 0.0004 \| - \|
	\| 0.8059 \| 3400 \| 0.0004 \| - \|
	\| 0.8177 \| 3450 \| 0.0004 \| - \|
	\| 0.8296 \| 3500 \| 0.0004 \| - \|
	\| 0.8414 \| 3550 \| 0.0004 \| - \|
	\| 0.8533 \| 3600 \| 0.0004 \| - \|
	\| 0.8651 \| 3650 \| 0.0004 \| - \|
	\| 0.8770 \| 3700 \| 0.0004 \| - \|
	\| 0.8888 \| 3750 \| 0.0004 \| - \|
	\| 0.9007 \| 3800 \| 0.0004 \| - \|
	\| 0.9125 \| 3850 \| 0.0004 \| - \|
	\| 0.9244 \| 3900 \| 0.0005 \| - \|
	\| 0.9362 \| 3950 \| 0.0004 \| - \|
	\| 0.9481 \| 4000 \| 0.0004 \| - \|
	\| 0.9599 \| 4050 \| 0.0004 \| - \|
	\| 0.9718 \| 4100 \| 0.0004 \| - \|
	\| 0.9836 \| 4150 \| 0.0004 \| - \|
	\| 0.9955 \| 4200 \| 0.0004 \| - \|

	### Framework Versions
	- Python: 3.10.12
	- SetFit: 1.0.3
	- Sentence Transformers: 2.5.1
	- Transformers: 4.38.1
	- PyTorch: 2.1.0+cu121
	- Datasets: 2.18.0
	- 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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