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Kyungjin-Kim/
embc25_finetuned_30000_it

Name: embc25_finetuned_30000_it
Availability: InStock

generated_from_trainer

Kyungjin-Kim/mmc_roberta_500000_it

finetune

autotrain_compatible

endpoints_compatible

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embc25_finetuned_30000_it

This model is a fine-tuned version of Kyungjin-Kim/mmc_roberta_500000_it on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.6728
Accuracy: 0.764
Precision: 0.7484
Recall: 0.7953
F1: 0.7712

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: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.5959	1.1848	500	0.6060	0.679	0.6440	0.8007	0.7138
0.4823	2.3697	1000	0.5269	0.7327	0.7015	0.81	0.7519
0.4246	3.5545	1500	0.5046	0.756	0.7787	0.7153	0.7457
0.3739	4.7393	2000	0.5315	0.7513	0.7220	0.8173	0.7667
0.3237	5.9242	2500	0.5528	0.7657	0.7720	0.754	0.7629
0.2552	7.1090	3000	0.6178	0.7637	0.7410	0.8107	0.7743
0.2218	8.2938	3500	0.6422	0.762	0.7648	0.7567	0.7607
0.203	9.4787	4000	0.6730	0.7623	0.7440	0.8	0.7710

Framework versions

Transformers 4.48.1
Pytorch 2.3.1
Datasets 3.2.0
Tokenizers 0.21.0

Kyungjin-Kim/embc25_finetuned_30000_it

embc25_finetuned_30000_it

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Kyungjin-Kim/
embc25_finetuned_30000_it