web-content-sumary-cls

utthi/web-content-sumary-cls

web-content-sumary-cls is a text classification model from utthi. Use it when you need a label for a piece of text. 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

29% of all-time downloads

All-time downloads

Public

Parameters

135M

540 MB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.safetensors540 MB · 100%

At a glance

Task: Text Classification
Library: transformers
License: agpl-3.0
Model type: roberta
Access: Public
Created: May 15, 2025
Updated: May 15, 2025
SHA: 1a75c87d

Try a prompt

Base models

vinai/phobert-base-v2

Model card

From the Hugging Face model README

web-content-sumary-cls

This model is a fine-tuned version of vinai/phobert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.8715
Accuracy: 0.8524
F1: 0.8222

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: 64
eval_batch_size: 64
seed: 42
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
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	0.4545	150	0.7061	0.7950	0.6569
No log	0.9091	300	0.5687	0.8168	0.7023
0.8303	1.3636	450	0.4732	0.8494	0.7251
0.8303	1.8182	600	0.4309	0.8581	0.8113
0.4545	2.2727	750	0.4547	0.8553	0.8208
0.4545	2.7273	900	0.4450	0.8579	0.8212
0.3152	3.1818	1050	0.4625	0.8570	0.8273
0.3152	3.6364	1200	0.4719	0.8564	0.8344
0.2328	4.0909	1350	0.4873	0.8619	0.8327
0.2328	4.5455	1500	0.4879	0.8598	0.8367
0.1712	5.0	1650	0.5019	0.8517	0.8276
0.1712	5.4545	1800	0.5579	0.8604	0.8305
0.1712	5.9091	1950	0.5267	0.8570	0.8366
0.1335	6.3636	2100	0.6011	0.8579	0.8304
0.1335	6.8182	2250	0.6327	0.8539	0.8255
0.1043	7.2727	2400	0.6376	0.8473	0.8255
0.1043	7.7273	2550	0.6161	0.8530	0.8245
0.0873	8.1818	2700	0.6972	0.8555	0.8201
0.0873	8.6364	2850	0.7232	0.8505	0.8187
0.0694	9.0909	3000	0.7737	0.8483	0.8198
0.0694	9.5455	3150	0.7432	0.8520	0.8236
0.058	10.0	3300	0.7928	0.8517	0.8183
0.058	10.4545	3450	0.7736	0.8532	0.8213
0.058	10.9091	3600	0.8185	0.8549	0.8216
0.0492	11.3636	3750	0.8136	0.8545	0.8246
0.0492	11.8182	3900	0.8213	0.8483	0.8183
0.0405	12.2727	4050	0.8379	0.8541	0.8265
0.0405	12.7273	4200	0.8304	0.8562	0.8270
0.0348	13.1818	4350	0.8629	0.8545	0.8263
0.0348	13.6364	4500	0.8757	0.8534	0.8261
0.0304	14.0909	4650	0.8532	0.8536	0.8258
0.0304	14.5455	4800	0.8661	0.8524	0.8244
0.0279	15.0	4950	0.8715	0.8524	0.8222

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1

Task: Text Classification
Library: transformers
Type: roberta
License: agpl-3.0
Created: May 15, 2025
Updated: May 15, 2025