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	---
	library_name: setfit
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	metrics:
	- accuracy
	- f1
	- precision
	- recall
	widget:
	- text: this is complete crap. i asked exactly five questions and he asked me to start
	a new topic, after which my daily limit was reached. why the hell did you add
	this restriction that makes the chat process completely useless??
	- text: brand wow, brands product is amazing! its definitely going to revolutionize
	product workflows! great job, brand!
	- text: why though? whats the harm in using ai as a tool. theres more to ai than product.
	- text: i got invited to participate in an early preview of the new product ai-powered
	product in product. as a scientific researcher, i'm finding this an amazingly
	powerful tool. this technology is simply revolutionary.
	- text: brand is the premier anti-fascist enterprise in the world today buy product!
	stop fascism!
	pipeline_tag: text-classification
	inference: true
	base_model: BAAI/bge-large-en-v1.5
	model-index:
	- name: SetFit with BAAI/bge-large-en-v1.5
	results:
	- task:
	type: text-classification
	name: Text Classification
	dataset:
	name: Unknown
	type: unknown
	split: test
	metrics:
	- type: accuracy
	value: 0.88
	name: Accuracy
	- type: f1
	value:
	- 0.8846153846153847
	- 0.6666666666666666
	- 0.9222520107238605
	name: F1
	- type: precision
	value:
	- 0.8214285714285714
	- 0.5
	- 1.0
	name: Precision
	- type: recall
	value:
	- 0.9583333333333334
	- 1.0
	- 0.8557213930348259
	name: Recall
	---

	# SetFit with BAAI/bge-large-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-large-en-v1.5](https://huggingface.co/BAAI/bge-large-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-large-en-v1.5](https://huggingface.co/BAAI/bge-large-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 \|
	\|:--------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| peak \| <ul><li>'after using product to summarize and gather main points of hundreds of research articles that are 50+ pages, i think i can confidently say that brand is on the right track with regards to implementing product in their business. truly extraordinary.'</li><li>'i was stuck in a error for 2+ hours and my bingey bot cleared it!! awesome ai product'</li><li>'product in teams: in teams, product transforms meetings. it organizes thoughts, maintains context, and facilitates collaborative brainstorming, making every meeting more productive.'</li></ul> \|
	\| neither \| <ul><li>">youll receive the test via email and will have two hours to complete it. finally, youll return to zoom with the analyst to go over your results together i don't think it's live. op will get the assigment and he/she has 2 hours to complete it. if this is correct, then op is an idiot because there are thousands of examples online and then there's product. op, start working on the fundamentals and pay the $20 product suscription for product."</li><li>'utilising advanced technologies with brand to perform a practical demonstration for a client on themes of cyber security, product, product, digital transformation, product, the product and more. these skills are rapidly being adopted for safety and efficielnkd.in/ghumbffm'</li><li>"another great example of the elites in the tech world using control of the information to infl your thoughts and actions. as product becomes more prevalent doing your own research will be essential. will be interesting to see if anyone finds success with designing a true 'unbiased' product"</li></ul> \|
	\| pit \| <ul><li>"the utter disappointment of learning from an amazing passionate teacher for two years who gives you decades of knowledge in 2 years and then you continue the subject and get some bland intellectual from the capital who can't even make a product presentation"</li><li>'the amount of times that product has been forced on me against my will after updates is just infuriating. product just taking advantage of the market position they (illegally) established long ago. near-universal software compatibility and being the default os of the general market are why people keep using them. they are in the position where they can fail upwards. and it sucks for the rest of us.'</li><li>'literally canceling my subscription on my product because this is terrible business practice. forcing subscription services to squeeze out every last dollar is disgusting especially when your whole program is a rip off of another established program. cringe'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \| F1 \| Precision \| Recall \|
	\|:--------\|:---------\|:-------------------------------------------------------------\|:-------------------------------\|:----------------------------------------------\|
	\| all \| 0.88 \| [0.8846153846153847, 0.6666666666666666, 0.9222520107238605] \| [0.8214285714285714, 0.5, 1.0] \| [0.9583333333333334, 1.0, 0.8557213930348259] \|

	## 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("jamiehudson/725_model_v6")
	# Run inference
	preds = model("why though? whats the harm in using ai as a tool. theres more to ai than product.")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 10 \| 37.08 \| 98 \|

	\| Label \| Training Sample Count \|
	\|:--------\|:----------------------\|
	\| pit \| 50 \|
	\| peak \| 50 \|
	\| neither \| 50 \|

	### Training Hyperparameters
	- batch_size: (16, 16)
	- num_epochs: (3, 3)
	- 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 \|
	\|:------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0011 \| 1 \| 0.2299 \| - \|
	\| 0.0533 \| 50 \| 0.1604 \| - \|
	\| 0.1066 \| 100 \| 0.0071 \| - \|
	\| 0.1599 \| 150 \| 0.0016 \| - \|
	\| 0.2132 \| 200 \| 0.0012 \| - \|
	\| 0.2665 \| 250 \| 0.0012 \| - \|
	\| 0.3198 \| 300 \| 0.0011 \| - \|
	\| 0.3731 \| 350 \| 0.0009 \| - \|
	\| 0.4264 \| 400 \| 0.0008 \| - \|
	\| 0.4797 \| 450 \| 0.0009 \| - \|
	\| 0.5330 \| 500 \| 0.0007 \| - \|
	\| 0.5864 \| 550 \| 0.0008 \| - \|
	\| 0.6397 \| 600 \| 0.0007 \| - \|
	\| 0.6930 \| 650 \| 0.0007 \| - \|
	\| 0.7463 \| 700 \| 0.0007 \| - \|
	\| 0.7996 \| 750 \| 0.0006 \| - \|
	\| 0.8529 \| 800 \| 0.0006 \| - \|
	\| 0.9062 \| 850 \| 0.0006 \| - \|
	\| 0.9595 \| 900 \| 0.0006 \| - \|
	\| 0.0011 \| 1 \| 0.0006 \| - \|
	\| 0.0533 \| 50 \| 0.0005 \| - \|
	\| 0.1066 \| 100 \| 0.0005 \| - \|
	\| 0.1599 \| 150 \| 0.0005 \| - \|
	\| 0.2132 \| 200 \| 0.0004 \| - \|
	\| 0.2665 \| 250 \| 0.0003 \| - \|
	\| 0.3198 \| 300 \| 0.0004 \| - \|
	\| 0.3731 \| 350 \| 0.0003 \| - \|
	\| 0.4264 \| 400 \| 0.0004 \| - \|
	\| 0.4797 \| 450 \| 0.0004 \| - \|
	\| 0.5330 \| 500 \| 0.0002 \| - \|
	\| 0.5864 \| 550 \| 0.0002 \| - \|
	\| 0.6397 \| 600 \| 0.0002 \| - \|
	\| 0.6930 \| 650 \| 0.0002 \| - \|
	\| 0.7463 \| 700 \| 0.0002 \| - \|
	\| 0.7996 \| 750 \| 0.0003 \| - \|
	\| 0.8529 \| 800 \| 0.0002 \| - \|
	\| 0.9062 \| 850 \| 0.0002 \| - \|
	\| 0.9595 \| 900 \| 0.0001 \| - \|
	\| 1.0128 \| 950 \| 0.0002 \| - \|
	\| 1.0661 \| 1000 \| 0.0002 \| - \|
	\| 1.1194 \| 1050 \| 0.0002 \| - \|
	\| 1.1727 \| 1100 \| 0.0001 \| - \|
	\| 1.2260 \| 1150 \| 0.0001 \| - \|
	\| 1.2793 \| 1200 \| 0.0001 \| - \|
	\| 1.3326 \| 1250 \| 0.0001 \| - \|
	\| 1.3859 \| 1300 \| 0.0001 \| - \|
	\| 1.4392 \| 1350 \| 0.0001 \| - \|
	\| 1.4925 \| 1400 \| 0.0001 \| - \|
	\| 1.5458 \| 1450 \| 0.0001 \| - \|
	\| 1.5991 \| 1500 \| 0.0001 \| - \|
	\| 1.6525 \| 1550 \| 0.0001 \| - \|
	\| 1.7058 \| 1600 \| 0.0001 \| - \|
	\| 1.7591 \| 1650 \| 0.0001 \| - \|
	\| 1.8124 \| 1700 \| 0.0001 \| - \|
	\| 1.8657 \| 1750 \| 0.0001 \| - \|
	\| 1.9190 \| 1800 \| 0.0001 \| - \|
	\| 1.9723 \| 1850 \| 0.0001 \| - \|
	\| 2.0256 \| 1900 \| 0.0001 \| - \|
	\| 2.0789 \| 1950 \| 0.0001 \| - \|
	\| 2.1322 \| 2000 \| 0.0001 \| - \|
	\| 2.1855 \| 2050 \| 0.0001 \| - \|
	\| 2.2388 \| 2100 \| 0.0001 \| - \|
	\| 2.2921 \| 2150 \| 0.0001 \| - \|
	\| 2.3454 \| 2200 \| 0.0001 \| - \|
	\| 2.3987 \| 2250 \| 0.0001 \| - \|
	\| 2.4520 \| 2300 \| 0.0001 \| - \|
	\| 2.5053 \| 2350 \| 0.0001 \| - \|
	\| 2.5586 \| 2400 \| 0.0001 \| - \|
	\| 2.6119 \| 2450 \| 0.0001 \| - \|
	\| 2.6652 \| 2500 \| 0.0001 \| - \|
	\| 2.7186 \| 2550 \| 0.0001 \| - \|
	\| 2.7719 \| 2600 \| 0.0001 \| - \|
	\| 2.8252 \| 2650 \| 0.0001 \| - \|
	\| 2.8785 \| 2700 \| 0.0001 \| - \|
	\| 2.9318 \| 2750 \| 0.0001 \| - \|
	\| 2.9851 \| 2800 \| 0.0001 \| - \|

	### Framework Versions
	- Python: 3.10.12
	- SetFit: 1.0.3
	- Sentence Transformers: 2.5.1
	- Transformers: 4.38.2
	- 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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