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distilbert-base-uncased_swag_mqa – AI Model by Gladiator | AlphaNeural AI
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Gladiator
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distilbert-base-uncased_swag_mqa
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transformers
pytorch
tensorboard
distilbert
multiple-choice
generated_from_trainer
swag
apache-2.0
endpoints_compatible
us
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distilbert-base-uncased_swag_mqa
This model is a fine-tuned version of
distilbert-base-uncased
on the swag dataset. It achieves the following results on the evaluation set:
Loss: 0.8556
Accuracy: 0.6494
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: 4e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 1
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
0.9234
1.0
2000
0.8556
0.6494
Framework versions
Transformers 4.20.1
Pytorch 1.11.0
Datasets 2.1.0
Tokenizers 0.12.1
