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bert_base_km_20_v1_wnli – AI Model by Hartunka | AlphaNeural AI
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Hartunka
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bert_base_km_20_v1_wnli
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transformers
tensorboard
safetensors
distilbert
text-classification
generated_from_trainer
en
glue
Hartunka/bert_base_km_20_v1
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autotrain_compatible
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bert_base_km_20_v1_wnli
This model is a fine-tuned version of
Hartunka/bert_base_km_20_v1
on the GLUE WNLI dataset. It achieves the following results on the evaluation set:
Loss: 0.7542
Accuracy: 0.3944
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: 5e-05
train_batch_size: 256
eval_batch_size: 256
seed: 10
optimizer: Use 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: 50
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
0.7377
1.0
3
0.7542
0.3944
0.7065
2.0
6
0.7640
0.2113
0.6898
3.0
9
0.7914
0.2254
0.692
4.0
12
0.8143
0.2676
0.6766
5.0
15
0.8582
0.2113
0.6757
6.0
18
0.8994
0.1690
Framework versions
Transformers 4.50.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.21.1
