L1-classifier / README.md

Add SetFit model

ca3a017 verified 10 months ago

18.1 kB

	---
	library_name: setfit
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	metrics:
	- accuracy
	widget:
	- text: According to the second chart the most popular country visited by UK residents
	at this period of time was France, which was visited by about 11 millions of people
	of people.
	- text: According to first diagramm, half of Yemen's population in 2000 was children
	0-14 years old.
	- text: After 1980 part old people in USA rose slight and in Sweden this point stay
	unchanged.
	- text: According to this charts people from the group 0-14 years take the biggest
	proportion from Yemen citizens in 2001.
	- text: 'After 1996 the numbers in the USA and Sweden began to differ: while in the
	USA the number of aged people fluctuated at the point of 14,8%, the population
	of Sweden outlived a considerable growth from 13% to 20% in 2010.'
	pipeline_tag: text-classification
	inference: true
	base_model: sentence-transformers/all-MiniLM-L6-v2
	model-index:
	- name: SetFit with sentence-transformers/all-MiniLM-L6-v2
	results:
	- task:
	type: text-classification
	name: Text Classification
	dataset:
	name: Unknown
	type: unknown
	split: test
	metrics:
	- type: accuracy
	value: 0.6197183098591549
	name: Accuracy
	---

	# SetFit with sentence-transformers/all-MiniLM-L6-v2

	This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
	- Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
	- Maximum Sequence Length: 256 tokens
	- Number of Classes: 5 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 \|
	\|:-----------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| Word form transmission \| <ul><li>"Mother should take care of her own child at first, by this quote we simply can see that problems of government's own country should be placed on the first position."</li><li>"A building's style may say a lot about its history."</li><li>'A lot of artists and entertainment organisations have financional costs because of free using of their contents in the Internet.'</li></ul> \|
	\| Tense semantics \| <ul><li>'Samsung, "Blackberry" and "HTC" in 2015 have almost the same percentage share.'</li><li>'(5,9%) Overall, almost all unemployment rates have remained on the same level between 2014 and 2015, except EU, Latin America and Middle East.'</li><li>'15% consist of things which are transported by rail in Eastern Europe in 2008.'</li></ul> \|
	\| Synonyms \| <ul><li>'(the destination between Moscow and Saint Petersburg, for instance, can be easily overcame by "Lastochka" train for 5 hours).'</li><li>'(the destination between Moscow and Saint Petersburg, for instance, can be easily overcame by "Lastochka" train for 5 hours).'</li><li>'There is an extremely clear difference: there are too many men on a tech subjects.'</li></ul> \|
	\| Copying expression \| <ul><li>'15-59 years people in Yemen are increasing, while in Italy this number decreases.'</li><li>'2013 year is a key one.'</li><li>'3,6% are people have age 60+ years.'</li></ul> \|
	\| Transliteration \| <ul><li>'A closer look at graphic revails that goods transported by rail had good products, which massive 11%.'</li><li>"According to first diagramm, half of Yemen's population in 2000 was children 0-14 years old."</li><li>'According to my opinion different fabrics make much more harm for our nature.'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.6197 \|

	## 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("Zlovoblachko/L1-classifier")
	# Run inference
	preds = model("After 1980 part old people in USA rose slight and in Sweden this point stay unchanged.")
	```

	<!--
	### 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 \| 2 \| 21.005 \| 47 \|

	\| Label \| Training Sample Count \|
	\|:-----------------------\|:----------------------\|
	\| Synonyms \| 99 \|
	\| Copying expression \| 26 \|
	\| Tense semantics \| 27 \|
	\| Word form transmission \| 40 \|
	\| Transliteration \| 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 \|
	\|:------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0012 \| 1 \| 0.3375 \| - \|
	\| 0.0590 \| 50 \| 0.3628 \| - \|
	\| 0.1179 \| 100 \| 0.3312 \| - \|
	\| 0.1769 \| 150 \| 0.2342 \| - \|
	\| 0.2358 \| 200 \| 0.2665 \| - \|
	\| 0.2948 \| 250 \| 0.1857 \| - \|
	\| 0.3538 \| 300 \| 0.2134 \| - \|
	\| 0.4127 \| 350 \| 0.1786 \| - \|
	\| 0.4717 \| 400 \| 0.092 \| - \|
	\| 0.5307 \| 450 \| 0.2031 \| - \|
	\| 0.5896 \| 500 \| 0.1449 \| - \|
	\| 0.6486 \| 550 \| 0.1234 \| - \|
	\| 0.7075 \| 600 \| 0.0552 \| - \|
	\| 0.7665 \| 650 \| 0.0693 \| - \|
	\| 0.8255 \| 700 \| 0.097 \| - \|
	\| 0.8844 \| 750 \| 0.0448 \| - \|
	\| 0.9434 \| 800 \| 0.041 \| - \|
	\| 1.0024 \| 850 \| 0.0431 \| - \|
	\| 1.0613 \| 900 \| 0.0227 \| - \|
	\| 1.1203 \| 950 \| 0.061 \| - \|
	\| 1.1792 \| 1000 \| 0.0209 \| - \|
	\| 1.2382 \| 1050 \| 0.0071 \| - \|
	\| 1.2972 \| 1100 \| 0.0285 \| - \|
	\| 1.3561 \| 1150 \| 0.0039 \| - \|
	\| 1.4151 \| 1200 \| 0.0029 \| - \|
	\| 1.4741 \| 1250 \| 0.0097 \| - \|
	\| 1.5330 \| 1300 \| 0.0076 \| - \|
	\| 1.5920 \| 1350 \| 0.0021 \| - \|
	\| 1.6509 \| 1400 \| 0.015 \| - \|
	\| 1.7099 \| 1450 \| 0.0027 \| - \|
	\| 1.7689 \| 1500 \| 0.0204 \| - \|
	\| 1.8278 \| 1550 \| 0.013 \| - \|
	\| 1.8868 \| 1600 \| 0.0222 \| - \|
	\| 1.9458 \| 1650 \| 0.0427 \| - \|
	\| 2.0047 \| 1700 \| 0.0181 \| - \|
	\| 2.0637 \| 1750 \| 0.0232 \| - \|
	\| 2.1226 \| 1800 \| 0.0053 \| - \|
	\| 2.1816 \| 1850 \| 0.0169 \| - \|
	\| 2.2406 \| 1900 \| 0.006 \| - \|
	\| 2.2995 \| 1950 \| 0.0108 \| - \|
	\| 2.3585 \| 2000 \| 0.0034 \| - \|
	\| 2.4175 \| 2050 \| 0.0198 \| - \|
	\| 2.4764 \| 2100 \| 0.0006 \| - \|
	\| 2.5354 \| 2150 \| 0.0142 \| - \|
	\| 2.5943 \| 2200 \| 0.0038 \| - \|
	\| 2.6533 \| 2250 \| 0.0006 \| - \|
	\| 2.7123 \| 2300 \| 0.0007 \| - \|
	\| 2.7712 \| 2350 \| 0.0012 \| - \|
	\| 2.8302 \| 2400 \| 0.0003 \| - \|
	\| 2.8892 \| 2450 \| 0.0127 \| - \|
	\| 2.9481 \| 2500 \| 0.0181 \| - \|
	\| 3.0071 \| 2550 \| 0.006 \| - \|
	\| 3.0660 \| 2600 \| 0.0006 \| - \|
	\| 3.125 \| 2650 \| 0.0156 \| - \|
	\| 3.1840 \| 2700 \| 0.0427 \| - \|
	\| 3.2429 \| 2750 \| 0.0004 \| - \|
	\| 3.3019 \| 2800 \| 0.0013 \| - \|
	\| 3.3608 \| 2850 \| 0.0241 \| - \|
	\| 3.4198 \| 2900 \| 0.0004 \| - \|
	\| 3.4788 \| 2950 \| 0.0048 \| - \|
	\| 3.5377 \| 3000 \| 0.0004 \| - \|
	\| 3.5967 \| 3050 \| 0.0006 \| - \|
	\| 3.6557 \| 3100 \| 0.0044 \| - \|
	\| 3.7146 \| 3150 \| 0.0142 \| - \|
	\| 3.7736 \| 3200 \| 0.005 \| - \|
	\| 3.8325 \| 3250 \| 0.0022 \| - \|
	\| 3.8915 \| 3300 \| 0.0033 \| - \|
	\| 3.9505 \| 3350 \| 0.0033 \| - \|
	\| 4.0094 \| 3400 \| 0.0005 \| - \|
	\| 4.0684 \| 3450 \| 0.0299 \| - \|
	\| 4.1274 \| 3500 \| 0.0172 \| - \|
	\| 4.1863 \| 3550 \| 0.0079 \| - \|
	\| 4.2453 \| 3600 \| 0.0012 \| - \|
	\| 4.3042 \| 3650 \| 0.0093 \| - \|
	\| 4.3632 \| 3700 \| 0.0175 \| - \|
	\| 4.4222 \| 3750 \| 0.0278 \| - \|
	\| 4.4811 \| 3800 \| 0.0004 \| - \|
	\| 4.5401 \| 3850 \| 0.0054 \| - \|
	\| 4.5991 \| 3900 \| 0.002 \| - \|
	\| 4.6580 \| 3950 \| 0.0248 \| - \|
	\| 4.7170 \| 4000 \| 0.0173 \| - \|
	\| 4.7759 \| 4050 \| 0.0004 \| - \|
	\| 4.8349 \| 4100 \| 0.0154 \| - \|
	\| 4.8939 \| 4150 \| 0.0162 \| - \|
	\| 4.9528 \| 4200 \| 0.0052 \| - \|
	\| 5.0118 \| 4250 \| 0.0142 \| - \|
	\| 5.0708 \| 4300 \| 0.0109 \| - \|
	\| 5.1297 \| 4350 \| 0.0003 \| - \|
	\| 5.1887 \| 4400 \| 0.0002 \| - \|
	\| 5.2476 \| 4450 \| 0.0003 \| - \|
	\| 5.3066 \| 4500 \| 0.0081 \| - \|
	\| 5.3656 \| 4550 \| 0.0005 \| - \|
	\| 5.4245 \| 4600 \| 0.0229 \| - \|
	\| 5.4835 \| 4650 \| 0.0002 \| - \|
	\| 5.5425 \| 4700 \| 0.0004 \| - \|
	\| 5.6014 \| 4750 \| 0.0233 \| - \|
	\| 5.6604 \| 4800 \| 0.0086 \| - \|
	\| 5.7193 \| 4850 \| 0.0084 \| - \|
	\| 5.7783 \| 4900 \| 0.0177 \| - \|
	\| 5.8373 \| 4950 \| 0.0102 \| - \|
	\| 5.8962 \| 5000 \| 0.017 \| - \|
	\| 5.9552 \| 5050 \| 0.0037 \| - \|
	\| 6.0142 \| 5100 \| 0.005 \| - \|
	\| 6.0731 \| 5150 \| 0.0002 \| - \|
	\| 6.1321 \| 5200 \| 0.0188 \| - \|
	\| 6.1910 \| 5250 \| 0.0037 \| - \|
	\| 6.25 \| 5300 \| 0.0003 \| - \|
	\| 6.3090 \| 5350 \| 0.0137 \| - \|
	\| 6.3679 \| 5400 \| 0.0107 \| - \|
	\| 6.4269 \| 5450 \| 0.0045 \| - \|
	\| 6.4858 \| 5500 \| 0.0002 \| - \|
	\| 6.5448 \| 5550 \| 0.0238 \| - \|
	\| 6.6038 \| 5600 \| 0.0209 \| - \|
	\| 6.6627 \| 5650 \| 0.0003 \| - \|
	\| 6.7217 \| 5700 \| 0.0002 \| - \|
	\| 6.7807 \| 5750 \| 0.0029 \| - \|
	\| 6.8396 \| 5800 \| 0.0177 \| - \|
	\| 6.8986 \| 5850 \| 0.0165 \| - \|
	\| 6.9575 \| 5900 \| 0.0045 \| - \|
	\| 7.0165 \| 5950 \| 0.0203 \| - \|
	\| 7.0755 \| 6000 \| 0.0048 \| - \|
	\| 7.1344 \| 6050 \| 0.0251 \| - \|
	\| 7.1934 \| 6100 \| 0.0147 \| - \|
	\| 7.2524 \| 6150 \| 0.0033 \| - \|
	\| 7.3113 \| 6200 \| 0.0166 \| - \|
	\| 7.3703 \| 6250 \| 0.0129 \| - \|
	\| 7.4292 \| 6300 \| 0.0169 \| - \|
	\| 7.4882 \| 6350 \| 0.0001 \| - \|
	\| 7.5472 \| 6400 \| 0.0002 \| - \|
	\| 7.6061 \| 6450 \| 0.0029 \| - \|
	\| 7.6651 \| 6500 \| 0.0264 \| - \|
	\| 7.7241 \| 6550 \| 0.0079 \| - \|
	\| 7.7830 \| 6600 \| 0.0002 \| - \|
	\| 7.8420 \| 6650 \| 0.0157 \| - \|
	\| 7.9009 \| 6700 \| 0.0116 \| - \|
	\| 7.9599 \| 6750 \| 0.0031 \| - \|
	\| 8.0189 \| 6800 \| 0.0055 \| - \|
	\| 8.0778 \| 6850 \| 0.0113 \| - \|
	\| 8.1368 \| 6900 \| 0.0004 \| - \|
	\| 8.1958 \| 6950 \| 0.0301 \| - \|
	\| 8.2547 \| 7000 \| 0.0002 \| - \|
	\| 8.3137 \| 7050 \| 0.0169 \| - \|
	\| 8.3726 \| 7100 \| 0.0001 \| - \|
	\| 8.4316 \| 7150 \| 0.0165 \| - \|
	\| 8.4906 \| 7200 \| 0.0201 \| - \|
	\| 8.5495 \| 7250 \| 0.0168 \| - \|
	\| 8.6085 \| 7300 \| 0.0197 \| - \|
	\| 8.6675 \| 7350 \| 0.0165 \| - \|
	\| 8.7264 \| 7400 \| 0.0165 \| - \|
	\| 8.7854 \| 7450 \| 0.0002 \| - \|
	\| 8.8443 \| 7500 \| 0.0134 \| - \|
	\| 8.9033 \| 7550 \| 0.0037 \| - \|
	\| 8.9623 \| 7600 \| 0.0043 \| - \|
	\| 9.0212 \| 7650 \| 0.0001 \| - \|
	\| 9.0802 \| 7700 \| 0.0034 \| - \|
	\| 9.1392 \| 7750 \| 0.0036 \| - \|
	\| 9.1981 \| 7800 \| 0.0001 \| - \|
	\| 9.2571 \| 7850 \| 0.0069 \| - \|
	\| 9.3160 \| 7900 \| 0.0304 \| - \|
	\| 9.375 \| 7950 \| 0.0203 \| - \|
	\| 9.4340 \| 8000 \| 0.0002 \| - \|
	\| 9.4929 \| 8050 \| 0.0002 \| - \|
	\| 9.5519 \| 8100 \| 0.0058 \| - \|
	\| 9.6108 \| 8150 \| 0.0141 \| - \|
	\| 9.6698 \| 8200 \| 0.0031 \| - \|
	\| 9.7288 \| 8250 \| 0.0169 \| - \|
	\| 9.7877 \| 8300 \| 0.0002 \| - \|
	\| 9.8467 \| 8350 \| 0.0075 \| - \|
	\| 9.9057 \| 8400 \| 0.0192 \| - \|
	\| 9.9646 \| 8450 \| 0.0588 \| - \|

	### Framework Versions
	- Python: 3.10.12
	- SetFit: 1.1.0.dev0
	- Sentence Transformers: 2.6.1
	- Transformers: 4.38.2
	- PyTorch: 2.2.1+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}
	}
	```

	<!--
	## 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.
	-->