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distilbert-implementation-1 – AI Model by Chung-Hsiung | AlphaNeural AI
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distilbert-implementation-1
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transformers
tensorboard
safetensors
distilbert
text-classification
generated_from_trainer
glue
distilbert/distilbert-base-uncased
finetune
apache-2.0
model-index
autotrain_compatible
endpoints_compatible
us
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distilbert-implementation-1
This model is a fine-tuned version of
distilbert-base-uncased
on the glue dataset. It achieves the following results on the evaluation set:
Loss: 0.6945
Matthews Correlation: 0.5185
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: 1.2885166618048824e-05
train_batch_size: 16
eval_batch_size: 16
seed: 25
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5
Training results
Training Loss
Epoch
Step
Validation Loss
Matthews Correlation
0.5306
1.0
535
0.4702
0.4543
0.3874
2.0
1070
0.4676
0.4977
0.2771
3.0
1605
0.5421
0.5126
0.2174
4.0
2140
0.6595
0.5016
0.1716
5.0
2675
0.6945
0.5185
Framework versions
Transformers 4.36.2
Pytorch 2.1.0+cu121
Datasets 2.15.0
Tokenizers 0.15.0
