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multi_choice_bert-base-uncased_swag_finetune – AI Model by jonastokoliu | AlphaNeural AI
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jonastokoliu
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multi_choice_bert-base-uncased_swag_finetune
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transformers
pytorch
tensorboard
bert
multiple-choice
generated_from_trainer
swag
apache-2.0
endpoints_compatible
us
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multi_choice_bert-base-uncased_swag_finetune
This model is a fine-tuned version of
bert-base-uncased
on the swag dataset. It achieves the following results on the evaluation set:
Loss: 0.7488
Accuracy: 0.8004
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: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
0.7512
1.0
2299
0.5688
0.7868
0.3857
2.0
4598
0.5583
0.7983
0.1556
3.0
6897
0.7488
0.8004
Framework versions
Transformers 4.29.2
Pytorch 2.0.1+cu117
Datasets 2.12.0
Tokenizers 0.13.3
