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gbert_success4_lora – AI Model by MB55 | AlphaNeural AI
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MB55
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gbert_success4_lora
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peft
safetensors
adapter
lora
transformers
deepset/gbert-base
mit
us
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gbert_success4_lora
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: 0.6821
Accuracy: 0.5788
Macro F1: 0.5714
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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
Macro F1
0.7478
1.0
340
0.7106
0.5729
0.5461
0.6929
2.0
680
0.6870
0.5773
0.5744
0.6908
3.0
1020
0.6821
0.5788
0.5714
Framework versions
PEFT 0.17.1
Transformers 4.56.1
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.22.0
