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indobert-base-uncased-kisi-mcq-relevance – AI Model by hawalurahman | AlphaNeural AI
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indobert-base-uncased-kisi-mcq-relevance
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transformers
safetensors
bert
text-classification
generated_from_trainer
indolem/indobert-base-uncased
finetune
mit
text-embeddings-inference
endpoints_compatible
us
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indobert-base-uncased-kisi-mcq-relevance
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: 0.5873
Accuracy: 0.7406
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: 16
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 5
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
0.7241
1.0
67
0.6888
0.5602
0.6965
2.0
134
0.6754
0.5639
0.6159
3.0
201
0.6425
0.6992
0.4422
4.0
268
0.5921
0.6992
0.3351
5.0
335
0.5873
0.7406
Framework versions
Transformers 4.56.2
Pytorch 2.8.0+cu129
Datasets 4.2.0
Tokenizers 0.22.1
