CeLLaTe-ner-2class-pubmedbert-tapt-combData-tokenizer-adapted-baseline

Mardiyyah/CeLLaTe-ner-2class-pubmedbert-tapt-combData-tokenizer-adapted-baseline

CeLLaTe-ner-2class-pubmedbert-tapt-combData-tokenizer-adapted-baseline is a token classification model from Mardiyyah. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Token Classification
Library: transformers
License: apache-2.0
Model type: bert
Access: Public
Created: Aug 4, 2026
Updated: Aug 4, 2026
SHA: d8b173b0

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Base models

Mardiyyah/CeLLaTe-tapt-pubmedbert-tokenizer-adapted-baseline-combinedData

Model card

From the Hugging Face model README

CeLLaTe-ner-2class-pubmedbert-tapt-combData-tokenizer-adapted-baseline

This model is a fine-tuned version of Mardiyyah/CeLLaTe-tapt-pubmedbert-tokenizer-adapted-baseline-combinedData on the OTAR3088/CeLLaTe-ner-2class-iob_final dataset. It achieves the following results on the evaluation set:

Loss: 0.0997
Precision: 0.7821
Recall: 0.7535
Micro F1: 0.7675
Weighted F1: 0.7678
Macro F1: 0.7765
Accuracy: 0.9845

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: 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 results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	Micro F1	Weighted F1	Macro F1	Accuracy
0.2456	1.0	263	0.0655	0.5680	0.6957	0.6254	0.6265	0.6334	0.9776
0.0403	2.0	526	0.0568	0.7239	0.6795	0.7010	0.7009	0.6974	0.9822
0.0256	3.0	789	0.0579	0.7702	0.7234	0.7460	0.7461	0.7503	0.9837
0.0187	4.0	1052	0.0662	0.7530	0.7276	0.7401	0.7406	0.7524	0.9837
0.0143	5.0	1315	0.0653	0.7464	0.7096	0.7275	0.7275	0.7146	0.9833
0.0112	6.0	1578	0.0696	0.7356	0.7126	0.7239	0.7240	0.7135	0.9829
0.0083	7.0	1841	0.0747	0.7359	0.7589	0.7472	0.7476	0.7551	0.9844
0.0065	8.0	2104	0.0829	0.7665	0.7246	0.7450	0.7450	0.7471	0.9839
0.0056	9.0	2367	0.0845	0.7562	0.7462	0.7512	0.7513	0.7574	0.9841
0.0046	10.0	2630	0.0899	0.7752	0.7029	0.7373	0.7373	0.7402	0.9834
0.0039	11.0	2893	0.0848	0.7368	0.7661	0.7512	0.7521	0.7635	0.9839
0.0036	12.0	3156	0.0880	0.7762	0.7384	0.7569	0.7573	0.7663	0.9846
0.0029	13.0	3419	0.0942	0.7506	0.7619	0.7562	0.7564	0.7639	0.9845
0.0025	14.0	3682	0.0936	0.7637	0.7619	0.7628	0.7632	0.7730	0.9846
0.0023	15.0	3945	0.0970	0.7648	0.7625	0.7636	0.7639	0.7724	0.9845
0.0018	16.0	4208	0.0988	0.7717	0.7583	0.7649	0.7654	0.7750	0.9846
0.0017	17.0	4471	0.1000	0.7821	0.7535	0.7675	0.7678	0.7765	0.9845
0.0016	18.0	4734	0.1010	0.7648	0.7589	0.7618	0.7622	0.7713	0.9844
0.0014	19.0	4997	0.1044	0.7752	0.7529	0.7639	0.7642	0.7742	0.9844
0.0014	20.0	5260	0.1044	0.7719	0.7529	0.7623	0.7626	0.7729	0.9843

Framework versions

Transformers 4.48.2
Pytorch 2.4.1+cu121
Datasets 3.0.2
Tokenizers 0.21.0

Task: Token Classification
Library: transformers
Type: bert
License: apache-2.0
Languages: en
Created: Aug 4, 2026
Updated: Aug 4, 2026