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bert_base_km_10_v2_wnli – AI Model by Hartunka | AlphaNeural AI
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Hartunka
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bert_base_km_10_v2_wnli
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transformers
tensorboard
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distilbert
text-classification
generated_from_trainer
en
glue
Hartunka/bert_base_km_10_v2
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autotrain_compatible
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bert_base_km_10_v2_wnli
This model is a fine-tuned version of
Hartunka/bert_base_km_10_v2
on the GLUE WNLI dataset. It achieves the following results on the evaluation set:
Loss: 0.7448
Accuracy: 0.3380
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
0.7363
1.0
3
0.7448
0.3380
0.6975
2.0
6
0.7656
0.2535
0.6875
3.0
9
0.7908
0.1831
0.6822
4.0
12
0.8011
0.2394
0.6762
5.0
15
0.8423
0.1268
0.6704
6.0
18
0.8818
0.1690
Framework versions
Transformers 4.50.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.21.1
