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Mardiyyah/
CeLLaTe-ner-3class-bioformer16l-tapt-tokenizer-original-baseline

Name: CeLLaTe-ner-3class-bioformer16l-tapt-tokenizer-original-baseline
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CeLLaTe-ner-3class-bioformer16l-tapt-tokenizer-original-baseline

This model is a fine-tuned version of Mardiyyah/CeLLaTe-tapt-bioformer16l-tokenizer-original-baseline on the OTAR3088/CeLLaTe-ner-3class-iob_final dataset. It achieves the following results on the evaluation set:

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The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 3407
gradient_accumulation_steps: 2
total_train_batch_size: 32
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
lr_scheduler_warmup_ratio: 0.01
num_epochs: 20
mixed_precision_training: Native AMP

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	Micro F1	Weighted F1	Macro F1	Accuracy
0.4197	1.0	263	0.1065	0.3777	0.4234	0.3993	0.4005	0.3666	0.9628
0.0812	2.0	526	0.0739	0.7431	0.7007	0.7213	0.7203	0.7082	0.9787
0.0489	3.0	789	0.0731	0.7443	0.6936	0.7180	0.7181	0.7111	0.9786
0.0353	4.0	1052	0.0816	0.7927	0.7125	0.7505	0.7475	0.7342	0.9799
0.0275	5.0	1315	0.0807	0.7766	0.7271	0.7510	0.7506	0.7411	0.9800
0.0219	6.0	1578	0.0921	0.7278	0.6920	0.7095	0.7107	0.7041	0.9777
0.0181	7.0	1841	0.0907	0.7282	0.7176	0.7229	0.7244	0.7171	0.9780
0.0153	8.0	2104	0.1052	0.7320	0.6810	0.7056	0.7066	0.7006	0.9775
0.0123	9.0	2367	0.0961	0.7497	0.7397	0.7446	0.7452	0.7387	0.9791
0.0113	10.0	2630	0.0939	0.7597	0.7448	0.7522	0.7527	0.7454	0.9796
0.0098	11.0	2893	0.0974	0.7445	0.7483	0.7464	0.7482	0.7396	0.9795
0.0085	12.0	3156	0.1007	0.7839	0.7558	0.7696	0.7697	0.7605	0.9810
0.0078	13.0	3419	0.1090	0.7228	0.7314	0.7271	0.7291	0.7224	0.9782
0.0073	14.0	3682	0.1059	0.7746	0.7416	0.7577	0.7589	0.7514	0.9804
0.0062	15.0	3945	0.1060	0.7713	0.7625	0.7669	0.7679	0.7581	0.9806
0.0058	16.0	4208	0.1092	0.7483	0.7507	0.7495	0.7512	0.7442	0.9796
0.0052	17.0	4471	0.1136	0.7791	0.7460	0.7622	0.7626	0.7540	0.9802