bert-30M-uncased-classification-CMC-first-intent

This model is a fine-tuned version of vietgpt/bert-30M-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0086
Accuracy: 1.0

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	28	0.9113	0.4694
No log	2.0	56	0.8776	0.4898
No log	3.0	84	0.8197	0.5714
No log	4.0	112	0.5064	0.9388
No log	5.0	140	0.2618	0.9388
No log	6.0	168	0.2210	0.9388
No log	7.0	196	0.1956	0.9592
No log	8.0	224	0.1718	0.9796
No log	9.0	252	0.1478	0.9592
No log	10.0	280	0.1190	0.9796
No log	11.0	308	0.0898	0.9796
No log	12.0	336	0.0926	0.9796
No log	13.0	364	0.0654	0.9796
No log	14.0	392	0.0635	0.9796
No log	15.0	420	0.0475	0.9796
No log	16.0	448	0.0469	0.9796
No log	17.0	476	0.0391	0.9796
0.4397	18.0	504	0.0325	1.0
0.4397	19.0	532	0.0272	1.0
0.4397	20.0	560	0.0267	1.0
0.4397	21.0	588	0.0216	1.0
0.4397	22.0	616	0.0200	1.0
0.4397	23.0	644	0.0185	1.0
0.4397	24.0	672	0.0185	1.0
0.4397	25.0	700	0.0141	1.0
0.4397	26.0	728	0.0151	1.0
0.4397	27.0	756	0.0140	1.0
0.4397	28.0	784	0.0132	1.0
0.4397	29.0	812	0.0127	1.0
0.4397	30.0	840	0.0118	1.0
0.4397	31.0	868	0.0113	1.0
0.4397	32.0	896	0.0113	1.0
0.4397	33.0	924	0.0107	1.0
0.4397	34.0	952	0.0103	1.0
0.4397	35.0	980	0.0103	1.0
0.0247	36.0	1008	0.0098	1.0
0.0247	37.0	1036	0.0097	1.0
0.0247	38.0	1064	0.0094	1.0
0.0247	39.0	1092	0.0096	1.0
0.0247	40.0	1120	0.0094	1.0
0.0247	41.0	1148	0.0095	1.0
0.0247	42.0	1176	0.0094	1.0
0.0247	43.0	1204	0.0092	1.0
0.0247	44.0	1232	0.0090	1.0
0.0247	45.0	1260	0.0090	1.0
0.0247	46.0	1288	0.0086	1.0
0.0247	47.0	1316	0.0086	1.0
0.0247	48.0	1344	0.0086	1.0
0.0247	49.0	1372	0.0086	1.0
0.0247	50.0	1400	0.0086	1.0

Framework versions

Transformers 4.37.1
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.1

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Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05

train_batch_size: 16

eval_batch_size: 16

seed: 42

optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08

lr_scheduler_type: linear

num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	28	0.9113	0.4694
No log	2.0	56	0.8776	0.4898
No log	3.0	84	0.8197	0.5714
No log	4.0	112	0.5064	0.9388
No log	5.0	140	0.2618	0.9388
No log	6.0	168	0.2210	0.9388
No log	7.0	196	0.1956	0.9592
No log	8.0	224	0.1718	0.9796
No log	9.0	252	0.1478	0.9592
No log	10.0	280	0.1190	0.9796
No log	11.0	308	0.0898	0.9796
No log	12.0	336	0.0926	0.9796
No log	13.0	364	0.0654	0.9796
No log	14.0	392	0.0635	0.9796
No log	15.0	420	0.0475	0.9796
No log	16.0	448	0.0469	0.9796
No log	17.0	476	0.0391	0.9796
0.4397	18.0	504	0.0325	1.0
0.4397	19.0	532	0.0272	1.0
0.4397	20.0	560	0.0267	1.0
0.4397	21.0	588	0.0216	1.0
0.4397	22.0	616	0.0200	1.0
0.4397	23.0	644	0.0185	1.0
0.4397	24.0	672	0.0185	1.0
0.4397	25.0	700	0.0141	1.0
0.4397	26.0	728	0.0151	1.0
0.4397	27.0	756	0.0140	1.0
0.4397	28.0	784	0.0132	1.0
0.4397	29.0	812	0.0127	1.0
0.4397	30.0	840	0.0118	1.0
0.4397	31.0	868	0.0113	1.0
0.4397	32.0	896	0.0113	1.0
0.4397	33.0	924	0.0107	1.0
0.4397	34.0	952	0.0103	1.0
0.4397	35.0	980	0.0103	1.0
0.0247	36.0	1008	0.0098	1.0
0.0247	37.0	1036	0.0097	1.0
0.0247	38.0	1064	0.0094	1.0
0.0247	39.0	1092	0.0096	1.0
0.0247	40.0	1120	0.0094	1.0
0.0247	41.0	1148	0.0095	1.0
0.0247	42.0	1176	0.0094	1.0
0.0247	43.0	1204	0.0092	1.0
0.0247	44.0	1232	0.0090	1.0
0.0247	45.0	1260	0.0090	1.0
0.0247	46.0	1288	0.0086	1.0
0.0247	47.0	1316	0.0086	1.0
0.0247	48.0	1344	0.0086	1.0
0.0247	49.0	1372	0.0086	1.0
0.0247	50.0	1400	0.0086	1.0

Framework versions

Transformers 4.37.1

Pytorch 2.1.0+cu121

Datasets 2.16.1

Tokenizers 0.15.1

thangvip/bert-30M-uncased-classification-CMC-first-intent

bert-30M-uncased-classification-CMC-first-intent

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

bert-30M-uncased-classification-CMC-first-intent

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions