Text Classification
Transformers
Safetensors
Portuguese
bert
Eval Results (legacy)
text-embeddings-inference
Instructions to use Silly-Machine/TuPy-Bert-Base-Multilabel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Silly-Machine/TuPy-Bert-Base-Multilabel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Silly-Machine/TuPy-Bert-Base-Multilabel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Silly-Machine/TuPy-Bert-Base-Multilabel") model = AutoModelForSequenceClassification.from_pretrained("Silly-Machine/TuPy-Bert-Base-Multilabel") - Notebooks
- Google Colab
- Kaggle
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## Introduction
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Tupy-BERT-Base is a fine-tuned BERT model designed specifically for
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For more details or specific inquiries, please refer to the [BERTimbau repository](https://github.com/neuralmind-ai/portuguese-bert/).
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The efficacy of Language Models can exhibit notable variations when confronted with a shift in domain between training and test data.
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## Available models
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## Introduction
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Tupy-BERT-Base is a fine-tuned BERT model designed specifically for multilabel classification of hate speech in Portuguese.
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Derived from the [BERTimbau base](https://huggingface.co/neuralmind/bert-base-portuguese-cased), TuPy-Base is a refined solution for addressing categorical hate speech concerns.
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For more details or specific inquiries, please refer to the [BERTimbau repository](https://github.com/neuralmind-ai/portuguese-bert/).
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The efficacy of Language Models can exhibit notable variations when confronted with a shift in domain between training and test data.
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In the creation of a specialized Portuguese Language Model tailored for hate speech classification,
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the original BERTimbau model underwent fine-tuning processe carried out on
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the [TuPy Hate Speech DataSet](https://huggingface.co/datasets/Silly-Machine/TuPyE-Dataset), sourced from diverse social networks.
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## Available models
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