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fine-tuned-DatasetQAS-IDK-MRC-with-indobert-base-uncased – AI Model by muhammadravi251001 | AlphaNeural AI
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fine-tuned-DatasetQAS-IDK-MRC-with-indobert-base-uncased
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transformers
pytorch
tensorboard
bert
question-answering
generated_from_trainer
mit
endpoints_compatible
us
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fine-tuned-DatasetQAS-IDK-MRC-with-indobert-base-uncased
This model is a fine-tuned version of
indolem/indobert-base-uncased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 5.9029
Accuracy: 0.0
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: 1e-05
train_batch_size: 128
eval_batch_size: 64
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 512
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.06
num_epochs: 1
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
1.4716
1.0
1
5.9029
0.0
Framework versions
Transformers 4.26.1
Pytorch 1.13.1+cu117
Datasets 2.2.0
Tokenizers 0.13.2
