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distilbert-base-uncased-finetuned-soqa – AI Model by KJCHUA | AlphaNeural AI
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distilbert-base-uncased-finetuned-soqa
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transformers
tensorboard
safetensors
distilbert
fill-mask
generated_from_trainer
distilbert/distilbert-base-uncased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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distilbert-base-uncased-finetuned-soqa
This model is a fine-tuned version of
distilbert-base-uncased
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 1.5383
Model Preparation Time: 0.002
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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3.0
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Model Preparation Time
1.9501
1.0
1871
1.6644
0.002
1.6818
2.0
3742
1.5624
0.002
1.6149
3.0
5613
1.5463
0.002
Framework versions
Transformers 4.49.0
Pytorch 2.6.0+cu124
Datasets 3.4.1
Tokenizers 0.21.1
