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gbert-base-weimar-domain-adaptation – AI Model by DavidSchweizer | AlphaNeural AI
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DavidSchweizer
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gbert-base-weimar-domain-adaptation
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transformers
safetensors
bert
fill-mask
generated_from_trainer
deepset/gbert-base
finetune
mit
autotrain_compatible
endpoints_compatible
us
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gbert-base-weimar-domain-adaptation
This model is a fine-tuned version of
deepset/gbert-base
on the None dataset. It achieves the following results on the evaluation set:
Loss: 1.2639
Model Preparation Time: 0.0863
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: 32
eval_batch_size: 32
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: 3.0
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Model Preparation Time
1.4941
1.0
9214
1.3347
0.0863
1.3917
2.0
18428
1.2840
0.0863
1.3477
3.0
27642
1.2648
0.0863
Framework versions
Transformers 4.53.0
Pytorch 2.6.0+cu124
Datasets 2.14.4
Tokenizers 0.21.2
