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	---
	library_name: setfit
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	metrics:
	- accuracy
	widget:
	- text: Please email the information to me.
	- text: Give me a second, please.
	- text: Is it possible to talk to a higher authority?
	- text: Sorry, too busy to chat right now.
	- text: I already own one, thanks.
	pipeline_tag: text-classification
	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: 0.9333333333333333
	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: 25 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 \|
	\|:---------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| do_not_qualify \| <ul><li>"Your target age group doesn't include me."</li><li>"I'm outside the age range for this."</li><li>"I'm not in the age group you're looking for."</li></ul> \|
	\| can_you_email \| <ul><li>'I prefer email, can you write to me?'</li><li>'Email is more convenient for me, can you use that?'</li><li>'Can you send me the details by email?'</li></ul> \|
	\| say_again \| <ul><li>'Can you repeat that, please?'</li><li>'I missed that, can you say it again?'</li><li>'Could you please repeat what you just said?'</li></ul> \|
	\| hold_a_sec \| <ul><li>'One moment, please hold.'</li><li>'Hang on for a bit, please.'</li><li>'Just a minute, please.'</li></ul> \|
	\| language_barrier \| <ul><li>'English is hard for me, ¿puedo hablar en español?'</li><li>'I struggle with English, ¿puede ser en español?'</li><li>"I'm more comfortable in Spanish, ¿podemos continuar en español?"</li></ul> \|
	\| decline \| <ul><li>'wrong'</li><li>'Never'</li><li>"I don't want this, thank you."</li></ul> \|
	\| transfer_request \| <ul><li>'Can you transfer this call to your superior?'</li><li>'I need to speak with someone in charge.'</li><li>'Can I speak with your manager?'</li></ul> \|
	\| scam \| <ul><li>"I'm skeptical, this doesn't sound right."</li><li>"I'm wary, this feels like a scam."</li><li>"Are you sure this isn't a scam?"</li></ul> \|
	\| who_are_you \| <ul><li>"I would like to know who's calling."</li><li>"Who's calling, please?"</li><li>'Who are you and why are you calling?'</li></ul> \|
	\| where_did_you_get_my_info \| <ul><li>'Can you explain how you got my contact info?'</li><li>"What's the source of my details you have?"</li><li>"I didn't give you my number, where did you get it?"</li></ul> \|
	\| do_not_call \| <ul><li>"Stop calling me, it's annoying!"</li><li>"I don't want to be contacted again."</li><li>"Enough calls, I'm not interested!"</li></ul> \|
	\| where_are_you_calling_from \| <ul><li>'Where are you calling from?'</li><li>'From which city or country are you calling?'</li><li>'Could you inform me of your current location?'</li></ul> \|
	\| complain_calls \| <ul><li>"Too many calls like this, it's irritating."</li><li>"I've had several calls like this, it's annoying."</li><li>"I keep getting these calls, it's too much."</li></ul> \|
	\| busy \| <ul><li>"Right now isn't good, I'm busy with something."</li><li>"I'm swamped at the moment, sorry."</li><li>"I'm busy right now, can't talk."</li></ul> \|
	\| greetings \| <ul><li>'Hi, how can I help you?'</li><li>'Hello, what can I help you with today?'</li><li>'Hello, yes?'</li></ul> \|
	\| sorry_greeting \| <ul><li>"I'm not at my best, what do you need?"</li><li>"Sorry, it's a bad time, I'm sick."</li><li>"Not a great time, I'm dealing with a personal issue."</li></ul> \|
	\| GreetBack \| <ul><li>'Doing well, how about yourself?'</li><li>'Pretty good, what about you?'</li><li>"Not bad, and how's it going on your end?"</li></ul> \|
	\| calling_about \| <ul><li>'Why are you calling me?'</li><li>"What's the matter, why the call?"</li><li>'May I know the reason for your call?'</li></ul> \|
	\| answering_machine \| <ul><li>"Leave a message and I'll get back to you."</li><li>"You're speaking to an answering machine, leave a message."</li><li>"This is an answering machine, I'm not available."</li></ul> \|
	\| weather \| <ul><li>"Sunny skies here, what's it like where you are?"</li><li>"It's a bit cloudy here, is it the same there?"</li><li>"It's warm here, what about where you are?"</li></ul> \|
	\| are_you_bot \| <ul><li>'Is this a bot calling me?'</li><li>'Is this a recorded message or are you real?'</li><li>'Are you a live person or a recording?'</li></ul> \|
	\| affirmation \| <ul><li>'yes'</li><li>"That's true, yes."</li><li>"Precisely, that's right."</li></ul> \|
	\| not_interested \| <ul><li>"This doesn't interest me, sorry."</li><li>"This offer isn't relevant to my interests."</li><li>"Thanks, but this isn't something I need."</li></ul> \|
	\| already \| <ul><li>"I've made this purchase before."</li><li>"This isn't new to me, I have it already."</li><li>"I've been using this for a while now."</li></ul> \|
	\| abusibve \| <ul><li>"This is unacceptable, I won't tolerate this!"</li><li>'I demand you stop this abusive calling!'</li><li>"Stop calling me, it's harassment!"</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.9333 \|

	## 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("Give me a second, please.")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 1 \| 6.8375 \| 13 \|

	\| Label \| Training Sample Count \|
	\|:---------------------------\|:----------------------\|
	\| GreetBack \| 9 \|
	\| abusibve \| 9 \|
	\| affirmation \| 10 \|
	\| already \| 10 \|
	\| answering_machine \| 8 \|
	\| are_you_bot \| 8 \|
	\| busy \| 9 \|
	\| calling_about \| 8 \|
	\| can_you_email \| 11 \|
	\| complain_calls \| 11 \|
	\| decline \| 10 \|
	\| do_not_call \| 12 \|
	\| do_not_qualify \| 9 \|
	\| greetings \| 8 \|
	\| hold_a_sec \| 8 \|
	\| language_barrier \| 10 \|
	\| not_interested \| 11 \|
	\| say_again \| 12 \|
	\| scam \| 9 \|
	\| sorry_greeting \| 9 \|
	\| transfer_request \| 8 \|
	\| weather \| 10 \|
	\| where_are_you_calling_from \| 9 \|
	\| where_did_you_get_my_info \| 11 \|
	\| who_are_you \| 11 \|

	### Training Hyperparameters
	- batch_size: (8, 8)
	- 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 \|
	\|:------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0008 \| 1 \| 0.1054 \| - \|
	\| 0.0417 \| 50 \| 0.1111 \| - \|
	\| 0.0833 \| 100 \| 0.0798 \| - \|
	\| 0.125 \| 150 \| 0.0826 \| - \|
	\| 0.1667 \| 200 \| 0.0308 \| - \|
	\| 0.2083 \| 250 \| 0.0324 \| - \|
	\| 0.25 \| 300 \| 0.0607 \| - \|
	\| 0.2917 \| 350 \| 0.0042 \| - \|
	\| 0.3333 \| 400 \| 0.0116 \| - \|
	\| 0.375 \| 450 \| 0.0049 \| - \|
	\| 0.4167 \| 500 \| 0.0154 \| - \|
	\| 0.4583 \| 550 \| 0.0158 \| - \|
	\| 0.5 \| 600 \| 0.0036 \| - \|
	\| 0.5417 \| 650 \| 0.001 \| - \|
	\| 0.5833 \| 700 \| 0.0015 \| - \|
	\| 0.625 \| 750 \| 0.0012 \| - \|
	\| 0.6667 \| 800 \| 0.0009 \| - \|
	\| 0.7083 \| 850 \| 0.0008 \| - \|
	\| 0.75 \| 900 \| 0.0008 \| - \|
	\| 0.7917 \| 950 \| 0.0014 \| - \|
	\| 0.8333 \| 1000 \| 0.0005 \| - \|
	\| 0.875 \| 1050 \| 0.0027 \| - \|
	\| 0.9167 \| 1100 \| 0.0007 \| - \|
	\| 0.9583 \| 1150 \| 0.0008 \| - \|
	\| 1.0 \| 1200 \| 0.0012 \| - \|
	\| 1.0417 \| 1250 \| 0.0012 \| - \|
	\| 1.0833 \| 1300 \| 0.0006 \| - \|
	\| 1.125 \| 1350 \| 0.0005 \| - \|
	\| 1.1667 \| 1400 \| 0.0003 \| - \|
	\| 1.2083 \| 1450 \| 0.0012 \| - \|
	\| 1.25 \| 1500 \| 0.0006 \| - \|
	\| 1.2917 \| 1550 \| 0.0008 \| - \|
	\| 1.3333 \| 1600 \| 0.0008 \| - \|
	\| 1.375 \| 1650 \| 0.0003 \| - \|
	\| 1.4167 \| 1700 \| 0.0004 \| - \|
	\| 1.4583 \| 1750 \| 0.0005 \| - \|
	\| 1.5 \| 1800 \| 0.0004 \| - \|
	\| 1.5417 \| 1850 \| 0.0004 \| - \|
	\| 1.5833 \| 1900 \| 0.0008 \| - \|
	\| 1.625 \| 1950 \| 0.0004 \| - \|
	\| 1.6667 \| 2000 \| 0.0004 \| - \|
	\| 1.7083 \| 2050 \| 0.0021 \| - \|
	\| 1.75 \| 2100 \| 0.0004 \| - \|
	\| 1.7917 \| 2150 \| 0.0002 \| - \|
	\| 1.8333 \| 2200 \| 0.0006 \| - \|
	\| 1.875 \| 2250 \| 0.0004 \| - \|
	\| 1.9167 \| 2300 \| 0.0006 \| - \|
	\| 1.9583 \| 2350 \| 0.0006 \| - \|
	\| 2.0 \| 2400 \| 0.0003 \| - \|
	\| 2.0417 \| 2450 \| 0.0002 \| - \|
	\| 2.0833 \| 2500 \| 0.0002 \| - \|
	\| 2.125 \| 2550 \| 0.0003 \| - \|
	\| 2.1667 \| 2600 \| 0.0004 \| - \|
	\| 2.2083 \| 2650 \| 0.0004 \| - \|
	\| 2.25 \| 2700 \| 0.0005 \| - \|
	\| 2.2917 \| 2750 \| 0.0005 \| - \|
	\| 2.3333 \| 2800 \| 0.0005 \| - \|
	\| 2.375 \| 2850 \| 0.0007 \| - \|
	\| 2.4167 \| 2900 \| 0.0002 \| - \|
	\| 2.4583 \| 2950 \| 0.0003 \| - \|
	\| 2.5 \| 3000 \| 0.0004 \| - \|
	\| 2.5417 \| 3050 \| 0.0002 \| - \|
	\| 2.5833 \| 3100 \| 0.0004 \| - \|
	\| 2.625 \| 3150 \| 0.0002 \| - \|
	\| 2.6667 \| 3200 \| 0.0002 \| - \|
	\| 2.7083 \| 3250 \| 0.0003 \| - \|
	\| 2.75 \| 3300 \| 0.0002 \| - \|
	\| 2.7917 \| 3350 \| 0.0002 \| - \|
	\| 2.8333 \| 3400 \| 0.0003 \| - \|
	\| 2.875 \| 3450 \| 0.0002 \| - \|
	\| 2.9167 \| 3500 \| 0.0002 \| - \|
	\| 2.9583 \| 3550 \| 0.0002 \| - \|
	\| 3.0 \| 3600 \| 0.0002 \| - \|

	### Framework Versions
	- Python: 3.10.13
	- SetFit: 1.0.1
	- Sentence Transformers: 2.2.2
	- Transformers: 4.35.0
	- PyTorch: 2.1.0
	- Datasets: 2.14.6
	- Tokenizers: 0.14.1

	## 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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