BERT_Text_classification_noisy_FULL

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

Loss: 0.5701
Accuracy: 0.8552
F1: 0.8469
Precision: 0.8499
Recall: 0.8466

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: 5e-05
train_batch_size: 16
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
2.8254	0.14	100	2.1467	0.4831	0.4014	0.4638	0.4628
1.7026	0.28	200	1.0972	0.7123	0.6713	0.6981	0.6939
1.207	0.42	300	0.9107	0.7524	0.7200	0.7407	0.7315
1.0468	0.56	400	0.7801	0.7861	0.7586	0.7829	0.7676
1.0953	0.71	500	0.8004	0.7852	0.7649	0.7778	0.7685
1.0857	0.85	600	0.8224	0.7678	0.7367	0.7775	0.7507
0.9787	0.99	700	0.6742	0.8111	0.7968	0.8069	0.7976
0.7944	1.13	800	0.6627	0.8222	0.8068	0.8162	0.8077
0.8078	1.27	900	0.6222	0.8306	0.8194	0.8260	0.8187
0.8101	1.41	1000	0.6620	0.8279	0.8158	0.8276	0.8155
0.7668	1.55	1100	0.5905	0.8439	0.8361	0.8400	0.8356
0.7133	1.69	1200	0.6085	0.8328	0.8231	0.8297	0.8225
0.7621	1.84	1300	0.6039	0.8375	0.8293	0.8368	0.8283
0.6461	1.98	1400	0.6176	0.8392	0.8298	0.8356	0.8292
0.5791	2.12	1500	0.5925	0.8524	0.8439	0.8497	0.8440
0.602	2.26	1600	0.5877	0.8473	0.8389	0.8441	0.8382
0.5459	2.4	1700	0.5888	0.8534	0.8460	0.8528	0.8459
0.569	2.54	1800	0.6118	0.8467	0.8393	0.8448	0.8387
0.5138	2.68	1900	0.5797	0.8561	0.8474	0.8520	0.8474
0.5427	2.82	2000	0.5678	0.8578	0.8498	0.8531	0.8493
0.5437	2.97	2100	0.5701	0.8552	0.8469	0.8499	0.8466

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.2

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Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05

train_batch_size: 16

eval_batch_size: 32

seed: 42

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

lr_scheduler_type: linear

lr_scheduler_warmup_steps: 100

num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
2.8254	0.14	100	2.1467	0.4831	0.4014	0.4638	0.4628
1.7026	0.28	200	1.0972	0.7123	0.6713	0.6981	0.6939
1.207	0.42	300	0.9107	0.7524	0.7200	0.7407	0.7315
1.0468	0.56	400	0.7801	0.7861	0.7586	0.7829	0.7676
1.0953	0.71	500	0.8004	0.7852	0.7649	0.7778	0.7685
1.0857	0.85	600	0.8224	0.7678	0.7367	0.7775	0.7507
0.9787	0.99	700	0.6742	0.8111	0.7968	0.8069	0.7976
0.7944	1.13	800	0.6627	0.8222	0.8068	0.8162	0.8077
0.8078	1.27	900	0.6222	0.8306	0.8194	0.8260	0.8187
0.8101	1.41	1000	0.6620	0.8279	0.8158	0.8276	0.8155
0.7668	1.55	1100	0.5905	0.8439	0.8361	0.8400	0.8356
0.7133	1.69	1200	0.6085	0.8328	0.8231	0.8297	0.8225
0.7621	1.84	1300	0.6039	0.8375	0.8293	0.8368	0.8283
0.6461	1.98	1400	0.6176	0.8392	0.8298	0.8356	0.8292
0.5791	2.12	1500	0.5925	0.8524	0.8439	0.8497	0.8440
0.602	2.26	1600	0.5877	0.8473	0.8389	0.8441	0.8382
0.5459	2.4	1700	0.5888	0.8534	0.8460	0.8528	0.8459
0.569	2.54	1800	0.6118	0.8467	0.8393	0.8448	0.8387
0.5138	2.68	1900	0.5797	0.8561	0.8474	0.8520	0.8474
0.5427	2.82	2000	0.5678	0.8578	0.8498	0.8531	0.8493
0.5437	2.97	2100	0.5701	0.8552	0.8469	0.8499	0.8466

Framework versions

Transformers 4.36.2

Pytorch 2.1.2+cu121

Datasets 2.16.1

Tokenizers 0.15.2

Noodle-bg/BERT_Text_classification_noisy_FULL

BERT_Text_classification_noisy_FULL

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

BERT_Text_classification_noisy_FULL

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions