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---
license: mit
base_model: neuralmind/bert-large-portuguese-cased
tags:
- generated_from_trainer
datasets:
- hate_speech_portuguese
metrics:
- accuracy
model-index:
- name: bertimbau_hate_speech
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: hate_speech_portuguese
type: hate_speech_portuguese
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.781433607520564
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bertimbau_hate_speech
This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co./neuralmind/bert-large-portuguese-cased) on the hate_speech_portuguese dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5009
- Accuracy: 0.7814
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 302 | 0.4593 | 0.7756 |
| 0.4361 | 2.0 | 604 | 0.5009 | 0.7814 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.1.0.dev20230816+cu121
- Datasets 2.14.4
- Tokenizers 0.13.3
|