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albert-base-v2-finetuned-wnli – AI Model by jinghan | AlphaNeural AI
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jinghan
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albert-base-v2-finetuned-wnli
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transformers
pytorch
tensorboard
albert
text-classification
generated_from_trainer
glue
apache-2.0
model-index
autotrain_compatible
endpoints_compatible
us
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albert-base-v2-finetuned-wnli
This model is a fine-tuned version of
albert-base-v2
on the glue dataset. It achieves the following results on the evaluation set:
Loss: 0.6981
Accuracy: 0.5634
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: 64
eval_batch_size: 64
seed: 42
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
Accuracy
No log
1.0
10
0.6954
0.4930
No log
2.0
20
0.6981
0.5634
No log
3.0
30
0.7036
0.4225
No log
4.0
40
0.7062
0.3944
No log
5.0
50
0.7035
0.4225
Framework versions
Transformers 4.21.0
Pytorch 1.12.0+cu113
Datasets 2.4.0
Tokenizers 0.12.1
