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bert-base-cased-hateeval-finetuned – AI Model by vignesh-waran | AlphaNeural AI
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vignesh-waran
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bert-base-cased-hateeval-finetuned
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transformers
safetensors
bert
text-classification
generated_from_trainer
google-bert/bert-base-cased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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This model is a fine-tuned version of
bert-base-cased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.4335
Accuracy: 0.792
F1: 0.7702
Precision: 0.7200
Recall: 0.8278
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: 108
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 3
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
F1
Precision
Recall
0.4881
1.0
250
0.4802
0.755
0.6843
0.7479
0.6306
0.3742
2.0
500
0.4335
0.792
0.7702
0.7200
0.8278
0.3529
3.0
750
0.4318
0.8
0.7653
0.7564
0.7743
Framework versions
Transformers 4.52.4
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1
