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datht/phobert-v2-UIT-VSMEC-ep20

sourceHugging Faceagpl-3.0updated 1y agoView on Hugging Face

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phobert-v2-UIT-VSMEC-ep20

This model is a fine-tuned version of vinai/phobert-base-v2 on the None dataset. It achieves the following results on the evaluation set:

—Loss: 1.7445
—Micro F1: 31.1953
—Micro Precision: 31.1953
—Micro Recall: 31.1953
—Macro F1: 6.7937
—Macro Precision: 4.4565
—Macro Recall: 14.2857

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: 0.0002
—trainbatchsize: 16
—evalbatchsize: 16
—seed: 42
—distributed_type: multi-GPU
—gradientaccumulationsteps: 4
—totaltrainbatch_size: 64
—optimizer: Use adamwtorch with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
—lrschedulertype: cosine
—lrschedulerwarmup_ratio: 0.01
—num_epochs: 20.0

Training results

Training Loss	Epoch	Step	Validation Loss	Micro F1	Micro Precision	Micro Recall	Macro F1	Macro Precision	Macro Recall
1.6508	1.0	87	1.6280	38.1924	38.1924	38.1924	13.6689	11.0672	19.7948
1.6617	2.0	174	1.6283	41.8367	41.8367	41.8367	18.9235	16.9137	23.0805
1.7109	3.0	261	1.7474	32.6531	32.6531	32.6531	10.8742	8.5221	17.8755
1.7516	4.0	348	1.7299	33.0904	33.0904	33.0904	9.5104	10.4580	15.7395
1.6395	5.0	435	1.6963	36.1516	36.1516	36.1516	12.0999	10.4591	18.0399
1.7109	6.0	522	1.6895	36.1516	36.1516	36.1516	12.1304	10.5941	18.0399
1.6211	7.0	609	1.6557	39.2128	39.2128	39.2128	14.3600	10.8292	21.3168
1.6352	8.0	696	1.6453	36.8805	36.8805	36.8805	14.3207	11.9547	21.6941
1.7352	9.0	783	1.7116	30.7580	30.7580	30.7580	11.9946	11.8060	19.2029
1.5836	10.0	870	1.6459	37.4636	37.4636	37.4636	14.4364	11.7582	21.8830
1.6883	11.0	957	1.6196	38.6297	38.6297	38.6297	14.8475	11.9901	22.4561
1.7379	12.0	1044	1.6837	38.4840	38.4840	38.4840	13.8374	10.6984	20.2799
1.6898	13.0	1131	1.6709	37.7551	37.7551	37.7551	13.4848	10.5169	19.7508
1.8129	14.0	1218	1.7437	32.0700	32.0700	32.0700	8.2124	12.2136	14.9597
1.702	15.0	1305	1.7436	31.1953	31.1953	31.1953	6.7937	4.4565	14.2857
1.773	16.0	1392	1.7405	31.1953	31.1953	31.1953	6.7937	4.4565	14.2857
1.8234	17.0	1479	1.7439	31.1953	31.1953	31.1953	6.7937	4.4565	14.2857
1.7848	18.0	1566	1.7449	31.1953	31.1953	31.1953	6.7937	4.4565	14.2857
1.8039	19.0	1653	1.7440	31.1953	31.1953	31.1953	6.7937	4.4565	14.2857
1.777	19.7723	1720	1.7445	31.1953	31.1953	31.1953	6.7937	4.4565	14.2857

Framework versions

—Transformers 4.50.0
—Pytorch 2.6.0+cu124
—Datasets 2.15.0
—Tokenizers 0.21.1