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bert-base-uncased-rte-mean-pooling – AI Model by GorkemG | AlphaNeural AI
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GorkemG
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bert-base-uncased-rte-mean-pooling
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transformers
tensorboard
safetensors
generated_from_trainer
google-bert/bert-base-uncased
finetune
apache-2.0
endpoints_compatible
us
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bert-base-uncased-rte-mean-pooling
This model is a fine-tuned version of
bert-base-uncased
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 1.3940
Accuracy: 0.6606
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: 2.8545132954566883e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
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
num_epochs: 4
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
No log
1.0
156
0.6694
0.5957
No log
2.0
312
0.6908
0.6173
No log
3.0
468
0.8995
0.6318
0.4967
4.0
624
1.3940
0.6606
Framework versions
Transformers 4.50.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1
