Revision_09_03_PhoBert_Lexical_Meta_Q1-xlm_meta

RonTon05/Revision_09_03_PhoBert_Lexical_Meta_Q1-xlm_meta

Revision_09_03_PhoBert_Lexical_Meta_Q1-xlm_meta is a machine learning model from RonTon05. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as agpl-3.0.

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

Downloads · 30 days

Access

Public

Updated Mar 10, 2026

Parameters

412M

1.6 GB on disk

Likes

Public

Hugging Face

Repo makeup

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.safetensors1.6 GB · 100%

At a glance

Library: transformers
License: agpl-3.0
Access: Public
Created: Mar 10, 2026
Updated: Mar 10, 2026
SHA: dcb378af

Base models

phunganhsang/PhoBert_Lexical_Dataset45K

Model card

From the Hugging Face model README

Revision_09_03_PhoBert_Lexical_Meta_Q1-xlm_meta

This model is a fine-tuned version of phunganhsang/PhoBert_Lexical_Dataset45K on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.3816
Accuracy: 0.9275
F1: 0.7167

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: 32
eval_batch_size: 32
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	F1
No log	0.1748	100	0.1887	0.9497	0.7353
No log	0.3497	200	0.1815	0.9490	0.7396
No log	0.5245	300	0.2291	0.9352	0.7229
No log	0.6993	400	0.2309	0.9297	0.7135
No log	0.8741	500	0.2079	0.9410	0.7367
0.1957	1.0490	600	0.1919	0.9463	0.7384
0.1957	1.2238	700	0.2241	0.9383	0.7310
0.1957	1.3986	800	0.2204	0.9422	0.7350
0.1957	1.5734	900	0.2635	0.9310	0.7223
0.1957	1.7483	1000	0.2369	0.9362	0.7274
0.1957	1.9231	1100	0.2547	0.9332	0.7215
0.1304	2.0979	1200	0.3088	0.9255	0.7112
0.1304	2.2727	1300	0.3538	0.9165	0.7004
0.1304	2.4476	1400	0.3045	0.9332	0.7297
0.1304	2.6224	1500	0.2627	0.9424	0.7347
0.1304	2.7972	1600	0.3009	0.9290	0.7225
0.1304	2.9720	1700	0.2816	0.9379	0.7294
0.0842	3.1469	1800	0.3555	0.9273	0.7201
0.0842	3.3217	1900	0.3312	0.9287	0.7166
0.0842	3.4965	2000	0.3112	0.9318	0.7257
0.0842	3.6713	2100	0.3462	0.9301	0.7189
0.0842	3.8462	2200	0.3491	0.9293	0.7177
0.0552	4.0210	2300	0.3497	0.9348	0.7259
0.0552	4.1958	2400	0.3911	0.9182	0.7029
0.0552	4.3706	2500	0.3815	0.9238	0.7096
0.0552	4.5455	2600	0.3794	0.9273	0.7148
0.0552	4.7203	2700	0.3790	0.9268	0.7142
0.0552	4.8951	2800	0.3816	0.9275	0.7167

Framework versions

Transformers 5.3.0
Pytorch 2.9.0+cu126
Datasets 4.0.0
Tokenizers 0.22.2

Library: transformers
License: agpl-3.0
Created: Mar 10, 2026
Updated: Mar 10, 2026