modelo_racismo_setfit_5jan24 / README.md

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	---
	library_name: setfit
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	metrics:
	- accuracy
	widget:
	- text: 'Janice Bryant Howroyd se tornou a primeira mulher negra a construir um negócio
	de mais de US$ 1 bilhão.


	Criada no sul dos Estados Unidos, em meio à segregação racial, ela enfrentou dificuldades
	na hora de buscar emprego e usou sua experiência quando decidiu abrir sua própria
	empresa de recursos humanos.'
	- text: O final q foi bom um homem igual a ele ia meter a porrada nele q pena q separaram
	😭 tinha q tomar muita porrada pra aprender a ser homem literalmente
	- text: macacos são mais espertos que homens pretos
	- text: Vai ser bom na primeira jogatina do jogo, tu já explodir cabeça de preto no
	asfalta Vou sentir os cara da ROTA aqui em SP "Matei mais um neguinho"
	- text: além disso, faz um trabalho desonesto(e é pra isso que ele tá lá) tentando
	igualar a escravização negra no quilombo dos palmares, com a escravização negra
	no Brasil, está que é puramente baseada em teorias supremacistas brancas.
	pipeline_tag: text-classification
	inference: true
	base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
	model-index:
	- name: SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
	results:
	- task:
	type: text-classification
	name: Text Classification
	dataset:
	name: Unknown
	type: unknown
	split: test
	metrics:
	- type: accuracy
	value: 0.8723404255319149
	name: Accuracy
	---

	# SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-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-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-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-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
	- Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
	- Maximum Sequence Length: 128 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 \|
	\|:------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 0 \| <ul><li>'O kit se chama GD Plus, a caixa preta é onde a energia da placa fica armazenada, ela tem três saídas para lâmpadas e uma USB para carregar qualquer coisa...'</li><li>'Faz uma oração antes e tenta conversar com ela'</li><li>'Vagabunda 🤮🤮🤮🤮💩💩💩💩, bruxa escrota...'</li></ul> \|
	\| 1 \| <ul><li>'Sério, ela era MUITO FEIA. Sabe a fêmea do macaco nesse nível, favelada e com linguajar de negra'</li><li>'Se tirar esse cabelo duro e clarear essa subaca preta encardida.... dá pra comer beijando'</li><li>'A verdade seja dita uma raça que foi introduzida no país, uma raça que em nenhum continente existia a não ser no africano, onde suq introdução serviu somente para um trabalho escravo e sem valor, de baixo custo operacional onde poderia ser substituido por outro, a qualquer momento, com o passar dos anos o trabalho escravo abolido, não foi desenvolvida nenhuma lei de devolução desta raça ao seu continente de origem, onde aqui ficando, se aglomeraram dando origem às favelas e toda vida marginal que temos hoje, e isto é fato, como hoje aceitar como normal, como igualar um negro a um branco, com toda essa agenda globalista, pra nos fazer aceitar goela abaixo isso como normal, em campanhas publicitárias, tv, novelas, filmes. Toda essa merda que vemos hoje no mundo é por colocar o negro numa posição social que não lhe convem, pois eles mesmos quando em ascensão são cheios de revoltas. Cada qual em seu lugar.'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.8723 \|

	## 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("leofn3/modelo_racismo_setfit_5jan24")
	# Run inference
	preds = model("macacos são mais espertos que homens pretos")
	```

	<!--
	### 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 \| 21.8855 \| 467 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0 \| 690 \|
	\| 1 \| 786 \|

	### Training Hyperparameters
	- batch_size: (16, 16)
	- num_epochs: (4, 4)
	- 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 \|
	\|:-------:\|:--------:\|:-------------:\|:---------------:\|
	\| 0.0005 \| 1 \| 0.264 \| - \|
	\| 0.0271 \| 50 \| 0.308 \| - \|
	\| 0.0542 \| 100 \| 0.2289 \| - \|
	\| 0.0813 \| 150 \| 0.2137 \| - \|
	\| 0.1084 \| 200 \| 0.1707 \| - \|
	\| 0.1355 \| 250 \| 0.2175 \| - \|
	\| 0.1626 \| 300 \| 0.2153 \| - \|
	\| 0.1897 \| 350 \| 0.2007 \| - \|
	\| 0.2168 \| 400 \| 0.2162 \| - \|
	\| 0.2439 \| 450 \| 0.205 \| - \|
	\| 0.2710 \| 500 \| 0.1994 \| - \|
	\| 0.2981 \| 550 \| 0.1056 \| - \|
	\| 0.3252 \| 600 \| 0.1551 \| - \|
	\| 0.3523 \| 650 \| 0.0454 \| - \|
	\| 0.3794 \| 700 \| 0.0636 \| - \|
	\| 0.4065 \| 750 \| 0.0928 \| - \|
	\| 0.4336 \| 800 \| 0.0191 \| - \|
	\| 0.4607 \| 850 \| 0.0279 \| - \|
	\| 0.4878 \| 900 \| 0.0395 \| - \|
	\| 0.5149 \| 950 \| 0.0124 \| - \|
	\| 0.5420 \| 1000 \| 0.0117 \| - \|
	\| 0.5691 \| 1050 \| 0.0037 \| - \|
	\| 0.5962 \| 1100 \| 0.0018 \| - \|
	\| 0.6233 \| 1150 \| 0.0004 \| - \|
	\| 0.6504 \| 1200 \| 0.0016 \| - \|
	\| 0.6775 \| 1250 \| 0.0012 \| - \|
	\| 0.7046 \| 1300 \| 0.0008 \| - \|
	\| 0.7317 \| 1350 \| 0.0006 \| - \|
	\| 0.7588 \| 1400 \| 0.0025 \| - \|
	\| 0.7859 \| 1450 \| 0.0003 \| - \|
	\| 0.8130 \| 1500 \| 0.0001 \| - \|
	\| 0.8401 \| 1550 \| 0.0002 \| - \|
	\| 0.8672 \| 1600 \| 0.0002 \| - \|
	\| 0.8943 \| 1650 \| 0.0002 \| - \|
	\| 0.9214 \| 1700 \| 0.0002 \| - \|
	\| 0.9485 \| 1750 \| 0.0001 \| - \|
	\| 0.9756 \| 1800 \| 0.0001 \| - \|
	\| 1.0 \| 1845 \| - \| 0.2148 \|
	\| 1.0027 \| 1850 \| 0.0014 \| - \|
	\| 1.0298 \| 1900 \| 0.0001 \| - \|
	\| 1.0569 \| 1950 \| 0.0001 \| - \|
	\| 1.0840 \| 2000 \| 0.0001 \| - \|
	\| 1.1111 \| 2050 \| 0.0001 \| - \|
	\| 1.1382 \| 2100 \| 0.0002 \| - \|
	\| 1.1653 \| 2150 \| 0.0001 \| - \|
	\| 1.1924 \| 2200 \| 0.0001 \| - \|
	\| 1.2195 \| 2250 \| 0.0001 \| - \|
	\| 1.2466 \| 2300 \| 0.0002 \| - \|
	\| 1.2737 \| 2350 \| 0.0001 \| - \|
	\| 1.3008 \| 2400 \| 0.0 \| - \|
	\| 1.3279 \| 2450 \| 0.0001 \| - \|
	\| 1.3550 \| 2500 \| 0.0001 \| - \|
	\| 1.3821 \| 2550 \| 0.0 \| - \|
	\| 1.4092 \| 2600 \| 0.0001 \| - \|
	\| 1.4363 \| 2650 \| 0.0002 \| - \|
	\| 1.4634 \| 2700 \| 0.0001 \| - \|
	\| 1.4905 \| 2750 \| 0.0 \| - \|
	\| 1.5176 \| 2800 \| 0.0 \| - \|
	\| 1.5447 \| 2850 \| 0.0001 \| - \|
	\| 1.5718 \| 2900 \| 0.0 \| - \|
	\| 1.5989 \| 2950 \| 0.0 \| - \|
	\| 1.6260 \| 3000 \| 0.0001 \| - \|
	\| 1.6531 \| 3050 \| 0.0001 \| - \|
	\| 1.6802 \| 3100 \| 0.0 \| - \|
	\| 1.7073 \| 3150 \| 0.0 \| - \|
	\| 1.7344 \| 3200 \| 0.0001 \| - \|
	\| 1.7615 \| 3250 \| 0.0 \| - \|
	\| 1.7886 \| 3300 \| 0.0 \| - \|
	\| 1.8157 \| 3350 \| 0.0007 \| - \|
	\| 1.8428 \| 3400 \| 0.0001 \| - \|
	\| 1.8699 \| 3450 \| 0.0002 \| - \|
	\| 1.8970 \| 3500 \| 0.0 \| - \|
	\| 1.9241 \| 3550 \| 0.0 \| - \|
	\| 1.9512 \| 3600 \| 0.0 \| - \|
	\| 1.9783 \| 3650 \| 0.0 \| - \|
	\| 2.0 \| 3690 \| - \| 0.2065 \|
	\| 2.0054 \| 3700 \| 0.0 \| - \|
	\| 2.0325 \| 3750 \| 0.0 \| - \|
	\| 2.0596 \| 3800 \| 0.0 \| - \|
	\| 2.0867 \| 3850 \| 0.0002 \| - \|
	\| 2.1138 \| 3900 \| 0.0 \| - \|
	\| 2.1409 \| 3950 \| 0.0 \| - \|
	\| 2.1680 \| 4000 \| 0.0 \| - \|
	\| 2.1951 \| 4050 \| 0.0 \| - \|
	\| 2.2222 \| 4100 \| 0.0 \| - \|
	\| 2.2493 \| 4150 \| 0.0 \| - \|
	\| 2.2764 \| 4200 \| 0.0002 \| - \|
	\| 2.3035 \| 4250 \| 0.0 \| - \|
	\| 2.3306 \| 4300 \| 0.0 \| - \|
	\| 2.3577 \| 4350 \| 0.0 \| - \|
	\| 2.3848 \| 4400 \| 0.0 \| - \|
	\| 2.4119 \| 4450 \| 0.0001 \| - \|
	\| 2.4390 \| 4500 \| 0.0 \| - \|
	\| 2.4661 \| 4550 \| 0.0 \| - \|
	\| 2.4932 \| 4600 \| 0.0 \| - \|
	\| 2.5203 \| 4650 \| 0.0 \| - \|
	\| 2.5474 \| 4700 \| 0.0 \| - \|
	\| 2.5745 \| 4750 \| 0.0 \| - \|
	\| 2.6016 \| 4800 \| 0.0 \| - \|
	\| 2.6287 \| 4850 \| 0.0 \| - \|
	\| 2.6558 \| 4900 \| 0.0 \| - \|
	\| 2.6829 \| 4950 \| 0.0 \| - \|
	\| 2.7100 \| 5000 \| 0.0 \| - \|
	\| 2.7371 \| 5050 \| 0.0 \| - \|
	\| 2.7642 \| 5100 \| 0.0 \| - \|
	\| 2.7913 \| 5150 \| 0.0 \| - \|
	\| 2.8184 \| 5200 \| 0.0 \| - \|
	\| 2.8455 \| 5250 \| 0.0 \| - \|
	\| 2.8726 \| 5300 \| 0.0 \| - \|
	\| 2.8997 \| 5350 \| 0.0 \| - \|
	\| 2.9268 \| 5400 \| 0.0 \| - \|
	\| 2.9539 \| 5450 \| 0.0 \| - \|
	\| 2.9810 \| 5500 \| 0.0 \| - \|
	\| 3.0 \| 5535 \| - \| 0.2189 \|
	\| 3.0081 \| 5550 \| 0.0 \| - \|
	\| 3.0352 \| 5600 \| 0.0 \| - \|
	\| 3.0623 \| 5650 \| 0.0 \| - \|
	\| 3.0894 \| 5700 \| 0.0 \| - \|
	\| 3.1165 \| 5750 \| 0.0 \| - \|
	\| 3.1436 \| 5800 \| 0.0 \| - \|
	\| 3.1707 \| 5850 \| 0.0 \| - \|
	\| 3.1978 \| 5900 \| 0.0 \| - \|
	\| 3.2249 \| 5950 \| 0.0 \| - \|
	\| 3.2520 \| 6000 \| 0.0 \| - \|
	\| 3.2791 \| 6050 \| 0.0 \| - \|
	\| 3.3062 \| 6100 \| 0.0 \| - \|
	\| 3.3333 \| 6150 \| 0.0 \| - \|
	\| 3.3604 \| 6200 \| 0.0 \| - \|
	\| 3.3875 \| 6250 \| 0.0 \| - \|
	\| 3.4146 \| 6300 \| 0.0 \| - \|
	\| 3.4417 \| 6350 \| 0.0 \| - \|
	\| 3.4688 \| 6400 \| 0.0 \| - \|
	\| 3.4959 \| 6450 \| 0.0 \| - \|
	\| 3.5230 \| 6500 \| 0.0 \| - \|
	\| 3.5501 \| 6550 \| 0.0 \| - \|
	\| 3.5772 \| 6600 \| 0.0 \| - \|
	\| 3.6043 \| 6650 \| 0.0 \| - \|
	\| 3.6314 \| 6700 \| 0.0 \| - \|
	\| 3.6585 \| 6750 \| 0.0365 \| - \|
	\| 3.6856 \| 6800 \| 0.0 \| - \|
	\| 3.7127 \| 6850 \| 0.0 \| - \|
	\| 3.7398 \| 6900 \| 0.0 \| - \|
	\| 3.7669 \| 6950 \| 0.0 \| - \|
	\| 3.7940 \| 7000 \| 0.0 \| - \|
	\| 3.8211 \| 7050 \| 0.0 \| - \|
	\| 3.8482 \| 7100 \| 0.0 \| - \|
	\| 3.8753 \| 7150 \| 0.0 \| - \|
	\| 3.9024 \| 7200 \| 0.0 \| - \|
	\| 3.9295 \| 7250 \| 0.0 \| - \|
	\| 3.9566 \| 7300 \| 0.0 \| - \|
	\| 3.9837 \| 7350 \| 0.0 \| - \|
	\| 4.0 \| 7380 \| - \| 0.206 \|

	* 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}
	}
	```

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