bert-based_uncased-finetuned-binary_hate_speech
This model is a fine-tuned version of bert-base-cased on the ag_news dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.3032
- eval_accuracy: 0.9426
- eval_f1: 0.9426
- eval_precision: 0.9428
- eval_recall: 0.9426
- eval_runtime: 12.9777
- eval_samples_per_second: 585.618
- eval_steps_per_second: 18.339
- epoch: 2.0
- step: 7500
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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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