Add SetFit model

e5b6a25 10 months ago

15.5 kB

	---
	library_name: setfit
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	metrics:
	- accuracy
	widget:
	- text: Fox News, The Washington Post, NBC News, The Associated Press and the Los
	Angeles Times are among the entities that have said they will file amicus briefs
	on behalf of CNN.
	- text: 'Tommy Robinson is in prison today because he violated a court order demanding
	that he not film videos outside the trials of Muslim rape gangs.

	'
	- text: As I wrote during the presidential campaign, Trump has no idea of Washington
	and no idea who to appoint who would support him rather than work against him.
	- text: IN MAY 2013, the Washington Post’s Greg Miller reported that the head of the
	CIA’s clandestine service was being shifted out of that position as a result of
	“a management shake-up” by then-Director John Brennan.
	- text: Columbus police are investigating the shootings.
	pipeline_tag: text-classification
	inference: false
	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: 0.602089552238806
	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 OneVsRestClassifier 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 OneVsRestClassifier instance
	- Maximum Sequence Length: 512 tokens
	<!-- - Number of Classes: Unknown -->
	<!-- - 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)

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.6021 \|

	## 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("anismahmahi/G2_replace_Whata_repetition_with_noPropaganda_SetFit")
	# Run inference
	preds = model("Columbus police are investigating the shootings.")
	```

	<!--
	### Downstream Use

	List how someone could finetune this model on their own dataset.
	-->

	<!--
	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
	-->

	<!--
	## Bias, Risks and Limitations

	What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.
	-->

	<!--
	### Recommendations

	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
	-->

	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:--------\|:----\|
	\| Word count \| 1 \| 23.1093 \| 129 \|

	### Training Hyperparameters
	- batch_size: (16, 16)
	- num_epochs: (2, 2)
	- max_steps: -1
	- sampling_strategy: oversampling
	- num_iterations: 10
	- 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: True

	### Training Results
	\| Epoch \| Step \| Training Loss \| Validation Loss \|
	\|:-------:\|:--------:\|:-------------:\|:---------------:\|
	\| 0.0002 \| 1 \| 0.3592 \| - \|
	\| 0.0121 \| 50 \| 0.2852 \| - \|
	\| 0.0243 \| 100 \| 0.2694 \| - \|
	\| 0.0364 \| 150 \| 0.2182 \| - \|
	\| 0.0486 \| 200 \| 0.2224 \| - \|
	\| 0.0607 \| 250 \| 0.2634 \| - \|
	\| 0.0729 \| 300 \| 0.2431 \| - \|
	\| 0.0850 \| 350 \| 0.2286 \| - \|
	\| 0.0971 \| 400 \| 0.197 \| - \|
	\| 0.1093 \| 450 \| 0.2466 \| - \|
	\| 0.1214 \| 500 \| 0.2374 \| - \|
	\| 0.1336 \| 550 \| 0.2134 \| - \|
	\| 0.1457 \| 600 \| 0.2092 \| - \|
	\| 0.1578 \| 650 \| 0.1987 \| - \|
	\| 0.1700 \| 700 \| 0.2288 \| - \|
	\| 0.1821 \| 750 \| 0.1562 \| - \|
	\| 0.1943 \| 800 \| 0.27 \| - \|
	\| 0.2064 \| 850 \| 0.1314 \| - \|
	\| 0.2186 \| 900 \| 0.2144 \| - \|
	\| 0.2307 \| 950 \| 0.184 \| - \|
	\| 0.2428 \| 1000 \| 0.2069 \| - \|
	\| 0.2550 \| 1050 \| 0.1773 \| - \|
	\| 0.2671 \| 1100 \| 0.0704 \| - \|
	\| 0.2793 \| 1150 \| 0.1139 \| - \|
	\| 0.2914 \| 1200 \| 0.2398 \| - \|
	\| 0.3035 \| 1250 \| 0.0672 \| - \|
	\| 0.3157 \| 1300 \| 0.1321 \| - \|
	\| 0.3278 \| 1350 \| 0.0803 \| - \|
	\| 0.3400 \| 1400 \| 0.0589 \| - \|
	\| 0.3521 \| 1450 \| 0.0428 \| - \|
	\| 0.3643 \| 1500 \| 0.0886 \| - \|
	\| 0.3764 \| 1550 \| 0.0839 \| - \|
	\| 0.3885 \| 1600 \| 0.1843 \| - \|
	\| 0.4007 \| 1650 \| 0.0375 \| - \|
	\| 0.4128 \| 1700 \| 0.114 \| - \|
	\| 0.4250 \| 1750 \| 0.1264 \| - \|
	\| 0.4371 \| 1800 \| 0.0585 \| - \|
	\| 0.4492 \| 1850 \| 0.0586 \| - \|
	\| 0.4614 \| 1900 \| 0.0805 \| - \|
	\| 0.4735 \| 1950 \| 0.0686 \| - \|
	\| 0.4857 \| 2000 \| 0.0684 \| - \|
	\| 0.4978 \| 2050 \| 0.0803 \| - \|
	\| 0.5100 \| 2100 \| 0.076 \| - \|
	\| 0.5221 \| 2150 \| 0.0888 \| - \|
	\| 0.5342 \| 2200 \| 0.1091 \| - \|
	\| 0.5464 \| 2250 \| 0.038 \| - \|
	\| 0.5585 \| 2300 \| 0.0674 \| - \|
	\| 0.5707 \| 2350 \| 0.0562 \| - \|
	\| 0.5828 \| 2400 \| 0.0603 \| - \|
	\| 0.5949 \| 2450 \| 0.0669 \| - \|
	\| 0.6071 \| 2500 \| 0.0829 \| - \|
	\| 0.6192 \| 2550 \| 0.1442 \| - \|
	\| 0.6314 \| 2600 \| 0.0914 \| - \|
	\| 0.6435 \| 2650 \| 0.0357 \| - \|
	\| 0.6557 \| 2700 \| 0.0546 \| - \|
	\| 0.6678 \| 2750 \| 0.0748 \| - \|
	\| 0.6799 \| 2800 \| 0.0149 \| - \|
	\| 0.6921 \| 2850 \| 0.1067 \| - \|
	\| 0.7042 \| 2900 \| 0.0054 \| - \|
	\| 0.7164 \| 2950 \| 0.0878 \| - \|
	\| 0.7285 \| 3000 \| 0.0385 \| - \|
	\| 0.7407 \| 3050 \| 0.036 \| - \|
	\| 0.7528 \| 3100 \| 0.0902 \| - \|
	\| 0.7649 \| 3150 \| 0.0734 \| - \|
	\| 0.7771 \| 3200 \| 0.0369 \| - \|
	\| 0.7892 \| 3250 \| 0.0031 \| - \|
	\| 0.8014 \| 3300 \| 0.0113 \| - \|
	\| 0.8135 \| 3350 \| 0.0862 \| - \|
	\| 0.8256 \| 3400 \| 0.0549 \| - \|
	\| 0.8378 \| 3450 \| 0.0104 \| - \|
	\| 0.8499 \| 3500 \| 0.0072 \| - \|
	\| 0.8621 \| 3550 \| 0.0546 \| - \|
	\| 0.8742 \| 3600 \| 0.0579 \| - \|
	\| 0.8864 \| 3650 \| 0.0789 \| - \|
	\| 0.8985 \| 3700 \| 0.0711 \| - \|
	\| 0.9106 \| 3750 \| 0.0361 \| - \|
	\| 0.9228 \| 3800 \| 0.0292 \| - \|
	\| 0.9349 \| 3850 \| 0.0121 \| - \|
	\| 0.9471 \| 3900 \| 0.0066 \| - \|
	\| 0.9592 \| 3950 \| 0.0091 \| - \|
	\| 0.9713 \| 4000 \| 0.0027 \| - \|
	\| 0.9835 \| 4050 \| 0.0891 \| - \|
	\| 0.9956 \| 4100 \| 0.0186 \| - \|
	\| 1.0 \| 4118 \| - \| 0.2746 \|
	\| 1.0078 \| 4150 \| 0.0246 \| - \|
	\| 1.0199 \| 4200 \| 0.0154 \| - \|
	\| 1.0321 \| 4250 \| 0.0056 \| - \|
	\| 1.0442 \| 4300 \| 0.0343 \| - \|
	\| 1.0563 \| 4350 \| 0.0375 \| - \|
	\| 1.0685 \| 4400 \| 0.0106 \| - \|
	\| 1.0806 \| 4450 \| 0.0025 \| - \|
	\| 1.0928 \| 4500 \| 0.0425 \| - \|
	\| 1.1049 \| 4550 \| 0.0019 \| - \|
	\| 1.1170 \| 4600 \| 0.0014 \| - \|
	\| 1.1292 \| 4650 \| 0.0883 \| - \|
	\| 1.1413 \| 4700 \| 0.0176 \| - \|
	\| 1.1535 \| 4750 \| 0.0204 \| - \|
	\| 1.1656 \| 4800 \| 0.0011 \| - \|
	\| 1.1778 \| 4850 \| 0.005 \| - \|
	\| 1.1899 \| 4900 \| 0.0238 \| - \|
	\| 1.2020 \| 4950 \| 0.0362 \| - \|
	\| 1.2142 \| 5000 \| 0.0219 \| - \|
	\| 1.2263 \| 5050 \| 0.0487 \| - \|
	\| 1.2385 \| 5100 \| 0.0609 \| - \|
	\| 1.2506 \| 5150 \| 0.0464 \| - \|
	\| 1.2627 \| 5200 \| 0.0033 \| - \|
	\| 1.2749 \| 5250 \| 0.0087 \| - \|
	\| 1.2870 \| 5300 \| 0.0101 \| - \|
	\| 1.2992 \| 5350 \| 0.0529 \| - \|
	\| 1.3113 \| 5400 \| 0.0243 \| - \|
	\| 1.3235 \| 5450 \| 0.001 \| - \|
	\| 1.3356 \| 5500 \| 0.0102 \| - \|
	\| 1.3477 \| 5550 \| 0.0047 \| - \|
	\| 1.3599 \| 5600 \| 0.0034 \| - \|
	\| 1.3720 \| 5650 \| 0.0118 \| - \|
	\| 1.3842 \| 5700 \| 0.0742 \| - \|
	\| 1.3963 \| 5750 \| 0.0538 \| - \|
	\| 1.4085 \| 5800 \| 0.0162 \| - \|
	\| 1.4206 \| 5850 \| 0.0079 \| - \|
	\| 1.4327 \| 5900 \| 0.0027 \| - \|
	\| 1.4449 \| 5950 \| 0.0035 \| - \|
	\| 1.4570 \| 6000 \| 0.0581 \| - \|
	\| 1.4692 \| 6050 \| 0.0813 \| - \|
	\| 1.4813 \| 6100 \| 0.0339 \| - \|
	\| 1.4934 \| 6150 \| 0.0312 \| - \|
	\| 1.5056 \| 6200 \| 0.0323 \| - \|
	\| 1.5177 \| 6250 \| 0.0521 \| - \|
	\| 1.5299 \| 6300 \| 0.0016 \| - \|
	\| 1.5420 \| 6350 \| 0.0009 \| - \|
	\| 1.5542 \| 6400 \| 0.0967 \| - \|
	\| 1.5663 \| 6450 \| 0.0009 \| - \|
	\| 1.5784 \| 6500 \| 0.031 \| - \|
	\| 1.5906 \| 6550 \| 0.0114 \| - \|
	\| 1.6027 \| 6600 \| 0.0599 \| - \|
	\| 1.6149 \| 6650 \| 0.0416 \| - \|
	\| 1.6270 \| 6700 \| 0.0047 \| - \|
	\| 1.6391 \| 6750 \| 0.0234 \| - \|
	\| 1.6513 \| 6800 \| 0.0609 \| - \|
	\| 1.6634 \| 6850 \| 0.022 \| - \|
	\| 1.6756 \| 6900 \| 0.0042 \| - \|
	\| 1.6877 \| 6950 \| 0.0336 \| - \|
	\| 1.6999 \| 7000 \| 0.0592 \| - \|
	\| 1.7120 \| 7050 \| 0.0536 \| - \|
	\| 1.7241 \| 7100 \| 0.1198 \| - \|
	\| 1.7363 \| 7150 \| 0.1035 \| - \|
	\| 1.7484 \| 7200 \| 0.0549 \| - \|
	\| 1.7606 \| 7250 \| 0.027 \| - \|
	\| 1.7727 \| 7300 \| 0.0251 \| - \|
	\| 1.7848 \| 7350 \| 0.0225 \| - \|
	\| 1.7970 \| 7400 \| 0.0027 \| - \|
	\| 1.8091 \| 7450 \| 0.0309 \| - \|
	\| 1.8213 \| 7500 \| 0.024 \| - \|
	\| 1.8334 \| 7550 \| 0.0355 \| - \|
	\| 1.8456 \| 7600 \| 0.0239 \| - \|
	\| 1.8577 \| 7650 \| 0.0377 \| - \|
	\| 1.8698 \| 7700 \| 0.012 \| - \|
	\| 1.8820 \| 7750 \| 0.0233 \| - \|
	\| 1.8941 \| 7800 \| 0.0184 \| - \|
	\| 1.9063 \| 7850 \| 0.0022 \| - \|
	\| 1.9184 \| 7900 \| 0.0043 \| - \|
	\| 1.9305 \| 7950 \| 0.014 \| - \|
	\| 1.9427 \| 8000 \| 0.0083 \| - \|
	\| 1.9548 \| 8050 \| 0.0084 \| - \|
	\| 1.9670 \| 8100 \| 0.0009 \| - \|
	\| 1.9791 \| 8150 \| 0.002 \| - \|
	\| 1.9913 \| 8200 \| 0.0002 \| - \|
	\| 2.0 \| 8236 \| - \| 0.2768 \|

	* The bold row denotes the saved checkpoint.
	### Framework Versions
	- Python: 3.10.12
	- SetFit: 1.0.1
	- Sentence Transformers: 2.2.2
	- Transformers: 4.35.2
	- PyTorch: 2.1.0+cu121
	- Datasets: 2.16.1
	- Tokenizers: 0.15.0

	## 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}
	}
	```

	<!--
	## Glossary

	Clearly define terms in order to be accessible across audiences.
	-->

	<!--
	## Model Card Authors

	Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.
	-->

	<!--
	## Model Card Contact

	Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.
	-->