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roberta-base-finetuned-swag – AI Model by Payoto | AlphaNeural AI
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roberta-base-finetuned-swag
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transformers
pytorch
optimum_graphcore
roberta
generated_from_trainer
swag
mit
endpoints_compatible
us
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roberta-base-finetuned-swag
This model is a fine-tuned version of
roberta-base
on the swag dataset. It achieves the following results on the evaluation set:
Loss: 0.4382
Accuracy: 0.8390
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: 2
eval_batch_size: 2
seed: 42
distributed_type: IPU
gradient_accumulation_steps: 16
total_train_batch_size: 128
total_eval_batch_size: 40
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3
training precision: Mixed Precision
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
0.5707
1.0
574
0.4990
0.8097
0.5092
2.0
1148
0.4321
0.8361
0.3597
3.0
1722
0.4382
0.8390
Framework versions
Transformers 4.20.1
Pytorch 1.10.0+cpu
Datasets 2.7.1
Tokenizers 0.12.1
