outputs

HuyHoang1977/outputs

outputs is a token classification model from HuyHoang1977. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as cc-by-sa-4.0.

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

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At a glance

Task: Token Classification
Library: transformers
License: cc-by-sa-4.0
Model type: bert
Access: Public
Created: Oct 9, 2025
Updated: Oct 10, 2025
SHA: 94b5250b

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Base models

KoichiYasuoka/bert-base-japanese-char-extended

Model card

From the Hugging Face model README

outputs

This model is a fine-tuned version of KoichiYasuoka/bert-base-japanese-char-extended on the None dataset. It achieves the following results on the evaluation set:

Loss: 2.2432
F1: 0.1451
Precision: 0.1495
Recall: 0.1410
Accuracy: 0.1914
F1 Art: 0.2010
F1 Conj: 0.1410
F1 Dj: 0.0
F1 Dp: 0.0606
F1 Dv: 0.0
F1 Erb: 0.0
F1 Et: 0.0513
F1 Ntj: 0.0273
F1 Oun: 0.0
F1 Ron: 0.1128
F1 Ropn: 0.0102
F1 Um: 0.0400
F1 Unct: 0.6066
F1 Ux: 0.0932
F1 Ym: 0.0296
F1 : 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: 3e-05
train_batch_size: 32
eval_batch_size: 64
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 64
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: 0.06
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Precision	Recall	Accuracy	F1 Art	F1 Conj	F1 Dj	F1 Dp	F1 Erb	F1 Et	F1 Ntj	F1 Oun	F1 Ron	F1 Ropn	F1 Um	F1 Unct	F1 Ux	F1 Ym
2.3878	1.0	638	2.4024	0.1351	0.1440	0.1272	0.2013	0.1619	0.1305	0.0061	0.0106	0.0	0.0813	0.0377	0.0	0.0	0.0191	0.0567	0.5886	0.1625	0.0446
2.3651	2.0	1276	2.3474	0.1368	0.1418	0.1321	0.1769	0.1957	0.1369	0.0	0.0228	0.0	0.0250	0.0253	0.0139	0.1175	0.0	0.0205	0.5886	0.0	0.0343
2.3634	3.0	1914	2.3653	0.1380	0.1403	0.1358	0.1947	0.1914	0.1278	0.0	0.0248	0.0	0.0783	0.0780	0.0	0.0995	0.0118	0.0	0.5886	0.1129	0.0627
2.3592	4.0	2552	2.3568	0.1356	0.1365	0.1346	0.1774	0.1776	0.1375	0.0061	0.0282	0.0	0.0729	0.0283	0.0	0.0966	0.0037	0.0229	0.5886	0.1038	0.0470
2.3378	5.0	3190	2.3542	0.1386	0.1498	0.1289	0.2005	0.1914	0.1416	0.0	0.0168	0.0	0.0302	0.0335	0.0057	0.1079	0.0118	0.0161	0.5886	0.0158	0.0182
2.388	6.0	3828	2.3583	0.1413	0.1578	0.1279	0.2129	0.1672	0.1466	0.0	0.0183	0.0552	0.0586	0.0349	0.0	0.0995	0.0	0.0229	0.5886	0.1217	0.0391
2.3177	7.0	4466	2.3540	0.1389	0.1523	0.1277	0.2005	0.1672	0.1434	0.0	0.0026	0.0	0.0	0.0234	0.0	0.1126	0.0291	0.0229	0.5886	0.1179	0.0182
2.3434	8.0	5104	2.3745	0.1349	0.1418	0.1286	0.1984	0.1776	0.1341	0.0	0.0006	0.0	0.0855	0.0402	0.0057	0.1079	0.0118	0.0437	0.5886	0.1038	0.0325
2.3773	9.0	5742	2.3556	0.1386	0.1444	0.1332	0.1920	0.1914	0.1399	0.0	0.0468	0.0	0.0583	0.0377	0.0	0.0995	0.0118	0.0437	0.5886	0.0363	0.0390
2.3234	10.0	6380	2.3626	0.1416	0.1468	0.1367	0.1930	0.1776	0.1385	0.0	0.0617	0.0	0.0549	0.0377	0.0	0.1079	0.0118	0.0437	0.5886	0.0794	0.0390

Framework versions

Transformers 4.53.3
Pytorch 2.6.0+cu124
Datasets 4.1.1
Tokenizers 0.21.2

Task: Token Classification
Library: transformers
Type: bert
License: cc-by-sa-4.0
Created: Oct 9, 2025
Updated: Oct 10, 2025