PhoBert_70KURL_bo_vn

gechim/PhoBert_70KURL_bo_vn

PhoBert_70KURL_bo_vn 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: xlm-roberta
Access: Public
Created: Jun 6, 2024
Updated: Jun 6, 2024
SHA: 698cb580

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Base models

vinai/phobert-base-v2

Model card

From the Hugging Face model README

PhoBert_70KURL_bo_vn

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: 0.0050
Accuracy: 0.9980
F1: 0.9980

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	0.4818	0.8017	0.7745
No log	0.9302	400	0.2638	0.9006	0.9018
No log	1.3953	600	0.2097	0.9222	0.9232
No log	1.8605	800	0.1810	0.9335	0.9343
0.3614	2.3256	1000	0.1468	0.9476	0.9478
0.3614	2.7907	1200	0.1471	0.9451	0.9458
0.3614	3.2558	1400	0.1196	0.9569	0.9570
0.3614	3.7209	1600	0.1065	0.9617	0.9618
0.1608	4.1860	1800	0.1014	0.9641	0.9641
0.1608	4.6512	2000	0.0953	0.9666	0.9668
0.1608	5.1163	2200	0.0843	0.9695	0.9696
0.1608	5.5814	2400	0.0761	0.9729	0.9730
0.1156	6.0465	2600	0.0675	0.9786	0.9787
0.1156	6.5116	2800	0.0547	0.9819	0.9820
0.1156	6.9767	3000	0.0487	0.9843	0.9843
0.1156	7.4419	3200	0.0419	0.9864	0.9865
0.1156	7.9070	3400	0.0460	0.9840	0.9841
0.0814	8.3721	3600	0.0361	0.9884	0.9884
0.0814	8.8372	3800	0.0334	0.9896	0.9896
0.0814	9.3023	4000	0.0327	0.9885	0.9885
0.0814	9.7674	4200	0.0326	0.9890	0.9890
0.0584	10.2326	4400	0.0282	0.9911	0.9911
0.0584	10.6977	4600	0.0222	0.9930	0.9930
0.0584	11.1628	4800	0.0185	0.9942	0.9942
0.0584	11.6279	5000	0.0163	0.9951	0.9951
0.0412	12.0930	5200	0.0235	0.9921	0.9921
0.0412	12.5581	5400	0.0134	0.9956	0.9956
0.0412	13.0233	5600	0.0123	0.9960	0.9960
0.0412	13.4884	5800	0.0111	0.9963	0.9963
0.0412	13.9535	6000	0.0096	0.9968	0.9968
0.0316	14.4186	6200	0.0143	0.9953	0.9953
0.0316	14.8837	6400	0.0088	0.9971	0.9971
0.0316	15.3488	6600	0.0077	0.9973	0.9973
0.0316	15.8140	6800	0.0073	0.9975	0.9975
0.0237	16.2791	7000	0.0066	0.9977	0.9977
0.0237	16.7442	7200	0.0065	0.9977	0.9977
0.0237	17.2093	7400	0.0057	0.9978	0.9978
0.0237	17.6744	7600	0.0072	0.9976	0.9976
0.0188	18.1395	7800	0.0055	0.9979	0.9979
0.0188	18.6047	8000	0.0052	0.9981	0.9981
0.0188	19.0698	8200	0.0052	0.9980	0.9980
0.0188	19.5349	8400	0.0050	0.9980	0.9980
0.0146	20.0	8600	0.0050	0.9980	0.9980

Framework versions

Transformers 4.41.2
Pytorch 2.1.2
Datasets 2.19.2
Tokenizers 0.19.1

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