bert-30M-uncased-classification-CMC-fqa-new

thangvip/bert-30M-uncased-classification-CMC-fqa-new

bert-30M-uncased-classification-CMC-fqa-new is a text classification model from thangvip. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.

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

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At a glance

Task: Text Classification
Library: transformers
License: apache-2.0
Model type: bert
Access: Public
Created: Jan 27, 2024
Updated: Jan 27, 2024
SHA: e24aa3c7

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Base models

vietgpt/bert-30M-uncased

Model card

From the Hugging Face model README

bert-30M-uncased-classification-CMC-fqa-new

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: 0.7760
Accuracy: 0.9677

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	20	3.4306	0.0323
No log	2.0	40	3.4143	0.0323
No log	3.0	60	3.4026	0.0645
No log	4.0	80	3.3888	0.2258
No log	5.0	100	3.3725	0.2581
No log	6.0	120	3.3523	0.3548
No log	7.0	140	3.3244	0.4194
No log	8.0	160	3.2797	0.3871
No log	9.0	180	3.2072	0.5161
No log	10.0	200	3.0977	0.4839
No log	11.0	220	2.9538	0.2903
No log	12.0	240	2.8136	0.2903
No log	13.0	260	2.6977	0.3871
No log	14.0	280	2.5970	0.4839
No log	15.0	300	2.5041	0.5806
No log	16.0	320	2.4092	0.5484
No log	17.0	340	2.3064	0.6774
No log	18.0	360	2.2057	0.6774
No log	19.0	380	2.0945	0.7419
No log	20.0	400	1.9827	0.7742
No log	21.0	420	1.8641	0.7742
No log	22.0	440	1.7476	0.7742
No log	23.0	460	1.6518	0.8065
No log	24.0	480	1.5613	0.8065
2.7559	25.0	500	1.4894	0.8387
2.7559	26.0	520	1.4089	0.8387
2.7559	27.0	540	1.3390	0.8065
2.7559	28.0	560	1.2802	0.8710
2.7559	29.0	580	1.2265	0.8710
2.7559	30.0	600	1.1639	0.8387
2.7559	31.0	620	1.1253	0.8710
2.7559	32.0	640	1.0845	0.9032
2.7559	33.0	660	1.0468	0.9032
2.7559	34.0	680	1.0144	0.9032
2.7559	35.0	700	0.9805	0.9355
2.7559	36.0	720	0.9564	0.9355
2.7559	37.0	740	0.9237	0.9677
2.7559	38.0	760	0.9041	0.9355
2.7559	39.0	780	0.8815	0.9677
2.7559	40.0	800	0.8668	0.9677
2.7559	41.0	820	0.8486	0.9677
2.7559	42.0	840	0.8288	0.9677
2.7559	43.0	860	0.8174	0.9677
2.7559	44.0	880	0.8058	0.9677
2.7559	45.0	900	0.7978	0.9677
2.7559	46.0	920	0.7901	0.9677
2.7559	47.0	940	0.7842	0.9677
2.7559	48.0	960	0.7798	0.9677
2.7559	49.0	980	0.7769	0.9677
1.1031	50.0	1000	0.7760	0.9677

Framework versions

Transformers 4.37.1
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.1

Task: Text Classification
Library: transformers
Type: bert
License: apache-2.0
Created: Jan 27, 2024
Updated: Jan 27, 2024