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	---
	language:
	- en
	license: apache-2.0
	library_name: setfit
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	datasets:
	- sst2
	metrics:
	- precision
	- recall
	- f1
	widget:
	- text: 'this is a story of two misfits who do n''t stand a chance alone , but together
	they are magnificent . '
	- text: 'it does n''t believe in itself , it has no sense of humor ... it ''s just
	plain bored . '
	- text: 'the band ''s courage in the face of official repression is inspiring , especially
	for aging hippies ( this one included ) . '
	- text: 'a fast , funny , highly enjoyable movie . '
	- text: 'the movie achieves as great an impact by keeping these thoughts hidden as
	... ( quills ) did by showing them . '
	pipeline_tag: text-classification
	co2_eq_emissions:
	emissions: 2.5933709269110308
	source: codecarbon
	training_type: fine-tuning
	on_cloud: false
	cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
	ram_total_size: 31.777088165283203
	hours_used: 0.027
	hardware_used: 1 x NVIDIA GeForce RTX 3090
	base_model: sentence-transformers/paraphrase-mpnet-base-v2
	model-index:
	- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2 on sst2
	results:
	- task:
	type: text-classification
	name: Text Classification
	dataset:
	name: sst2
	type: sst2
	split: test
	metrics:
	- type: accuracy
	value: 0.8588082901554405
	name: Accuracy
	---

	# SetFit with sentence-transformers/paraphrase-mpnet-base-v2 on sst2

	This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [sst2](https://huggingface.co/datasets/sst2) dataset 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. For classification, it uses a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance.

	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: [sst2](https://huggingface.co/datasets/sst2)
	- Language: en
	- License: apache-2.0

	### 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 \|
	\|:---------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| negative \| <ul><li>'stale and uninspired . '</li><li>"the film 's considered approach to its subject matter is too calm and thoughtful for agitprop , and the thinness of its characterizations makes it a failure as straight drama . ' "</li><li>"that their charm does n't do a load of good "</li></ul> \|
	\| positive \| <ul><li>"broomfield is energized by volletta wallace 's maternal fury , her fearlessness "</li><li>'flawless '</li><li>'insightfully written , delicately performed '</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.8588 \|

	## 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 🤗 Hub
	model = SetFitModel.from_pretrained("tomaarsen/setfit-paraphrase-mpnet-base-v2-sst2-8-shot")
	# Run inference
	preds = model("a fast , funny , highly enjoyable movie . ")
	```
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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:--------\|:----\|
	\| Word count \| 2 \| 11.4375 \| 33 \|

	\| Label \| Training Sample Count \|
	\|:---------\|:----------------------\|
	\| negative \| 8 \|
	\| positive \| 8 \|

	### Training Hyperparameters
	- batch_size: (16, 16)
	- num_epochs: (10, 10)
	- 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
	- load_best_model_at_end: True

	### Training Results
	\| Epoch \| Step \| Training Loss \| Validation Loss \|
	\|:----------:\|:------:\|:-------------:\|:---------------:\|
	\| 0.1111 \| 1 \| 0.2126 \| - \|
	\| 1.1111 \| 10 \| 0.1604 \| - \|
	\| 2.2222 \| 20 \| 0.0224 \| 0.1761 \|
	\| 3.3333 \| 30 \| 0.0039 \| - \|
	\| 4.4444 \| 40 \| 0.0029 \| 0.1935 \|
	\| 5.5556 \| 50 \| 0.0026 \| - \|
	\| 6.6667 \| 60 \| 0.0008 \| 0.1944 \|
	\| 7.7778 \| 70 \| 0.0009 \| - \|
	\| 8.8889 \| 80 \| 0.0027 \| 0.1941 \|
	\| 10.0 \| 90 \| 0.0004 \| - \|

	* The bold row denotes the saved checkpoint.
	### Environmental Impact
	Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
	- Carbon Emitted: 0.003 kg of CO2
	- Hours Used: 0.027 hours

	### Training Hardware
	- On Cloud: No
	- GPU Model: 1 x NVIDIA GeForce RTX 3090
	- CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
	- RAM Size: 31.78 GB

	### Framework Versions
	- Python: 3.9.16
	- SetFit: 1.0.0.dev0
	- Sentence Transformers: 2.2.2
	- Transformers: 4.29.0
	- PyTorch: 1.13.1+cu117
	- Datasets: 2.15.0
	- Tokenizers: 0.13.3

	## 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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