PhoBert_70KURL

gechim/PhoBert_70KURL

PhoBert_70KURL is a text classification model from gechim. Use it when you need a label for a piece of text. It is set up for transformers.

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

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At a glance

Task: Text Classification
Library: transformers
Model type: roberta
Access: Public
Created: Jun 6, 2024
Updated: Jun 6, 2024
SHA: 5a9a7fa4

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Base models

vinai/phobert-base-v2

Model card

From the Hugging Face model README

PhoBert_70KURL

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: 0.3781
Accuracy: 0.9371
F1: 0.9417

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
gradient_accumulation_steps: 2
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 2150
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	0.4651	200	0.3374	0.9302	0.9233
No log	0.9302	400	0.1954	0.9353	0.9383
No log	1.3953	600	0.1273	0.9598	0.9609
No log	1.8605	800	0.1601	0.9423	0.9461
0.3263	2.3256	1000	0.1059	0.9666	0.9675
0.3263	2.7907	1200	0.0832	0.9742	0.9746
0.3263	3.2558	1400	0.1073	0.9621	0.9637
0.3263	3.7209	1600	0.1053	0.9644	0.9658
0.1315	4.1860	1800	0.0994	0.9653	0.9663
0.1315	4.6512	2000	0.1109	0.9617	0.9633
0.1315	5.1163	2200	0.1350	0.9491	0.9523
0.1315	5.5814	2400	0.1204	0.9543	0.9567
0.0959	6.0465	2600	0.2663	0.8906	0.9034
0.0959	6.5116	2800	0.1309	0.9510	0.9538
0.0959	6.9767	3000	0.2027	0.9279	0.9340
0.0959	7.4419	3200	0.1277	0.9509	0.9535
0.0959	7.9070	3400	0.1892	0.9473	0.9507
0.0689	8.3721	3600	0.2090	0.9350	0.9396
0.0689	8.8372	3800	0.1247	0.9579	0.9596
0.0689	9.3023	4000	0.2984	0.9132	0.9216
0.0689	9.7674	4200	0.2277	0.9352	0.9401
0.0483	10.2326	4400	0.2595	0.9266	0.9328
0.0483	10.6977	4600	0.2725	0.9249	0.9313
0.0483	11.1628	4800	0.2483	0.9341	0.9391
0.0483	11.6279	5000	0.2195	0.9442	0.9478
0.0379	12.0930	5200	0.7268	0.8450	0.8672
0.0379	12.5581	5400	0.2236	0.9503	0.9530
0.0379	13.0233	5600	0.2610	0.9438	0.9474
0.0379	13.4884	5800	0.3155	0.9366	0.9412
0.0379	13.9535	6000	0.3225	0.9374	0.9418
0.0284	14.4186	6200	0.4604	0.9158	0.9238
0.0284	14.8837	6400	0.3269	0.9372	0.9417
0.0284	15.3488	6600	0.3419	0.9401	0.9442
0.0284	15.8140	6800	0.2869	0.9465	0.9497
0.0213	16.2791	7000	0.3623	0.9364	0.9410
0.0213	16.7442	7200	0.2880	0.9433	0.9466
0.0213	17.2093	7400	0.3010	0.9454	0.9484
0.0213	17.6744	7600	0.3719	0.9344	0.9391
0.0165	18.1395	7800	0.3160	0.9448	0.9481
0.0165	18.6047	8000	0.3767	0.9388	0.9431
0.0165	19.0698	8200	0.3702	0.9383	0.9426
0.0165	19.5349	8400	0.3998	0.9343	0.9393
0.0121	20.0	8600	0.3781	0.9371	0.9417

Framework versions

Transformers 4.41.2
Pytorch 2.1.2
Datasets 2.19.2
Tokenizers 0.19.1

Task: Text Classification
Library: transformers
Type: roberta
Created: Jun 6, 2024
Updated: Jun 6, 2024