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---
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library_name: transformers
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license: apache-2.0
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base_model: bert-base-uncased
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tags:
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- generated_from_trainer
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datasets:
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- hate_speech_portuguese
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metrics:
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- accuracy
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model-index:
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- name: bert-hate-speech-test
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results:
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- task:
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name: Text Classification
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type: text-classification
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dataset:
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name: hate_speech_portuguese
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type: hate_speech_portuguese
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config: default
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split: train[:10%]
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args: default
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.5964912280701754
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# bert-hate-speech-test
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the hate_speech_portuguese dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.7279
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- Accuracy: 0.5965
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- num_epochs: 3
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### Training results
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### Framework versions
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- Transformers 4.49.0
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- Pytorch 2.6.0+cpu
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- Datasets 3.3.0
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- Tokenizers 0.21.0
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