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	---
	library_name: setfit
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	metrics:
	- accuracy
	widget:
	- text: amy and matthew have a bit of a phony relationship , but the film works in
	spite of it .
	- text: it 's refreshing to see a romance this smart .
	- text: bogdanich is unashamedly pro-serbian and makes little attempt to give voice
	to the other side .
	- text: sayles has an eye for the ways people of different ethnicities talk to and
	about others outside the group .
	- text: eddie murphy and owen wilson have a cute partnership in i spy , but the movie
	around them is so often nearly nothing that their charm does n't do a load of
	good .
	pipeline_tag: text-classification
	inference: true
	base_model: BAAI/bge-small-en-v1.5
	model-index:
	- name: SetFit with BAAI/bge-small-en-v1.5
	results:
	- task:
	type: text-classification
	name: Text Classification
	dataset:
	name: Unknown
	type: unknown
	split: test
	metrics:
	- type: accuracy
	value: 0.8478857770455793
	name: Accuracy
	---

	# SetFit with BAAI/bge-small-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-small-en-v1.5](https://huggingface.co/BAAI/bge-small-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-small-en-v1.5](https://huggingface.co/BAAI/bge-small-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: 2 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 \|
	\|:---------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| negative \| <ul><li>'there might be some sort of credible gender-provoking philosophy submerged here , but who the hell cares ?'</li><li>'represents the depths to which the girls-behaving-badly film has fallen .'</li><li>'-lrb- a -rrb- crushing disappointment .'</li></ul> \|
	\| positive \| <ul><li>'what saves it ... and makes it one of the better video-game-based flicks , is that the film acknowledges upfront that the plot makes no sense , such that the lack of linearity is the point of emotional and moral departure for protagonist alice .'</li><li>'but it could be , by its art and heart , a necessary one .'</li><li>'a culture-clash comedy that , in addition to being very funny , captures some of the discomfort and embarrassment of being a bumbling american in europe .'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.8479 \|

	## 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("skylord/setfit-bge-small-v1.5-sst2-8-shot")
	# Run inference
	preds = model("it 's refreshing to see a romance this smart .")
	```

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 6 \| 22.5 \| 45 \|

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

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

	### Training Results
	\| Epoch \| Step \| Training Loss \| Validation Loss \|
	\|:-----:\|:----:\|:-------------:\|:---------------:\|
	\| 0.2 \| 1 \| 0.2109 \| - \|
	\| 10.0 \| 50 \| 0.01 \| - \|

	### Framework Versions
	- Python: 3.10.11
	- SetFit: 1.0.3
	- Sentence Transformers: 2.3.1
	- Transformers: 4.37.2
	- PyTorch: 2.2.0+cu121
	- Datasets: 2.16.1
	- Tokenizers: 0.15.1

	## 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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