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  # HateBERTimbau
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- <!-- Provide a quick summary of what the model is/does. -->
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- This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
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  ## Model Details
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  ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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  ### Training Procedure
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  <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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  #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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  ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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  ### Results
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- #### Summary
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  ## BibTeX Citation
 
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  # HateBERTimbau
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+ HateBERTimbau is a transformer-based encoder model for identifying hate speech in Portuguese social media text. It is a fine-tuned version of the [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) model, retrained on a dataset of 229,103 tweets specifically focused on potential hate speech.
 
 
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  ## Model Details
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  ### Training Data
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+ 229,103 tweets associated with offensive content were used
 
 
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  ### Training Procedure
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  <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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  #### Training Hyperparameters
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+ - Batch Size: 4 samples
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+ - Epochs: 100
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+ - Learning Rate: 5e-5 with Adam optimizer
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+ - Maximum Sequence Length: 512 sentence pieces
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  ## Evaluation
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+ ### Testing Data
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Results
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  ## BibTeX Citation