Text Classification
Transformers
Safetensors
Portuguese
bert
Portuguese
Brazilian
Language Classification
text-embeddings-inference
Instructions to use bastao/PeroVaz_PT-BR_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bastao/PeroVaz_PT-BR_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bastao/PeroVaz_PT-BR_Classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bastao/PeroVaz_PT-BR_Classifier") model = AutoModelForSequenceClassification.from_pretrained("bastao/PeroVaz_PT-BR_Classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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The PeroVazPT-PTBR Classifier is designed to classify text between European Portuguese (PT-PT) and Brazilian Portuguese (PT-BR).
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This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the [VeraCruz Dataset](https://huggingface.co/datasets/LemeExploreNau/VeraCruz).
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- Loss: 0.1791
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- Accuracy: 0.9461
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## Training Data
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The model was trained on the [VeraCruz Dataset](https://huggingface.co/datasets/LemeExploreNau/VeraCruz), a collection of text samples from both languages. The model was trained on a total of 500,000 examples, a evenly split between European Portuguese and Brazilian Portuguese, ensuring a balanced representation of both language variants.
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### Training hyperparameters
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The following hyperparameters were used during training:
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The PeroVazPT-PTBR Classifier is designed to classify text between European Portuguese (PT-PT) and Brazilian Portuguese (PT-BR).
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This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the [VeraCruz Dataset](https://huggingface.co/datasets/LemeExploreNau/VeraCruz).
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The model was trained on the [VeraCruz Dataset](https://huggingface.co/datasets/LemeExploreNau/VeraCruz), a collection of text samples from both languages. The model was trained on a total of 500,000 examples, a evenly split between European Portuguese and Brazilian Portuguese, ensuring a balanced representation of both language variants.
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It achieves the following results on an evaluation set of 50,000 examples:
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- Loss: 0.1791
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- Accuracy: 0.9461
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### Training hyperparameters
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The following hyperparameters were used during training:
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