DREsS_New_FineTuningBERT_noAug_language_fold0

MayBashendy/DREsS_New_FineTuningBERT_noAug_language_fold0

DREsS_New_FineTuningBERT_noAug_language_fold0 is a text classification model from MayBashendy. 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: Nov 3, 2024
Updated: Nov 3, 2024
SHA: bf8b12b7

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Base models

Model card

From the Hugging Face model README

DREsS_New_FineTuningBERT_noAug_language_fold0

This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4675
Qwk: 0.3889
Mse: 0.6768
Rmse: 0.8227

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: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	0.08	2	9.6096	0.0	11.6263	3.4097
No log	0.16	4	7.1971	0.0	6.0202	2.4536
No log	0.24	6	6.2377	0.0	6.0202	2.4536
No log	0.32	8	5.1034	0.0	6.0202	2.4536
No log	0.4	10	4.0220	-0.0032	5.9848	2.4464
No log	0.48	12	3.2272	-0.0004	2.4697	1.5715
No log	0.56	14	2.1971	0.0	2.4141	1.5538
No log	0.64	16	1.6102	0.0	2.4141	1.5538
No log	0.72	18	1.3149	-0.0055	2.4192	1.5554
No log	0.8	20	0.8578	0.0	0.8081	0.8989
No log	0.88	22	0.6423	0.0	0.8081	0.8989
No log	0.96	24	0.5271	0.0	0.8081	0.8989
No log	1.04	26	0.4893	0.0	0.8081	0.8989
No log	1.12	28	0.5116	0.0	0.8081	0.8989
No log	1.2	30	0.4883	0.0087	0.8030	0.8961
No log	1.28	32	0.5925	0.0930	1.0505	1.0249
No log	1.3600	34	0.6109	-0.1535	1.2374	1.1124
No log	1.44	36	0.5573	0.0	0.8081	0.8989
No log	1.52	38	0.4540	0.0942	0.7626	0.8733
No log	1.6	40	0.4678	0.3250	0.6515	0.8072
No log	1.6800	42	0.4254	0.0778	0.7727	0.8790
No log	1.76	44	0.4255	0.1807	0.7273	0.8528
No log	1.8400	46	0.4055	0.3799	0.6010	0.7752
No log	1.92	48	0.3522	0.3345	0.6212	0.7882
No log	2.0	50	0.3538	0.3770	0.6212	0.7882
No log	2.08	52	0.3881	0.3860	0.6465	0.8040
No log	2.16	54	0.3439	0.4754	0.5606	0.7487
No log	2.24	56	0.3568	0.4606	0.5808	0.7621
No log	2.32	58	0.3498	0.4897	0.5505	0.7420
No log	2.4	60	0.4371	0.4010	0.6465	0.8040
No log	2.48	62	0.4060	0.3906	0.6566	0.8103
No log	2.56	64	0.3348	0.4637	0.5657	0.7521
No log	2.64	66	0.4107	0.3634	0.6768	0.8227
No log	2.7200	68	0.4442	0.3539	0.6919	0.8318
No log	2.8	70	0.3449	0.4373	0.5758	0.7588
No log	2.88	72	0.4059	0.3656	0.6667	0.8165
No log	2.96	74	0.3371	0.4122	0.5909	0.7687
No log	3.04	76	0.3270	0.4391	0.5606	0.7487
No log	3.12	78	0.3932	0.3728	0.6616	0.8134
No log	3.2	80	0.4104	0.3884	0.6465	0.8040
No log	3.2800	82	0.3605	0.4446	0.5606	0.7487
No log	3.36	84	0.3386	0.4414	0.5606	0.7487
No log	3.44	86	0.3621	0.4146	0.5909	0.7687
No log	3.52	88	0.7310	0.2691	0.8131	0.9017
No log	3.6	90	0.8389	0.2012	0.9141	0.9561
No log	3.68	92	0.4733	0.4032	0.6414	0.8009
No log	3.76	94	0.3071	0.4789	0.5455	0.7385
No log	3.84	96	0.3039	0.4875	0.5354	0.7317
No log	3.92	98	0.4186	0.4002	0.6364	0.7977
No log	4.0	100	0.7010	0.2717	0.8131	0.9017
No log	4.08	102	0.5755	0.3104	0.7576	0.8704
No log	4.16	104	0.3067	0.4183	0.5909	0.7687
No log	4.24	106	0.2998	0.5315	0.5	0.7071
No log	4.32	108	0.3163	0.3998	0.6010	0.7752
No log	4.4	110	0.5301	0.3519	0.7121	0.8439
No log	4.48	112	0.5197	0.3413	0.7172	0.8469
No log	4.5600	114	0.3261	0.3907	0.6162	0.7850
No log	4.64	116	0.3076	0.4652	0.5606	0.7487
No log	4.72	118	0.4023	0.3824	0.6566	0.8103
No log	4.8	120	0.4175	0.3941	0.6465	0.8040
No log	4.88	122	0.3858	0.4111	0.6263	0.7914
No log	4.96	124	0.3297	0.4410	0.5960	0.7720
No log	5.04	126	0.3631	0.4078	0.6263	0.7914
No log	5.12	128	0.4388	0.4022	0.6414	0.8009
No log	5.2	130	0.3924	0.3790	0.6515	0.8072
No log	5.28	132	0.3572	0.4117	0.6162	0.7850
No log	5.36	134	0.3786	0.3996	0.6263	0.7914
No log	5.44	136	0.3809	0.3850	0.6465	0.8040
No log	5.52	138	0.5014	0.3819	0.6717	0.8196
No log	5.6	140	0.4590	0.3727	0.6717	0.8196
No log	5.68	142	0.3165	0.4073	0.5960	0.7720
No log	5.76	144	0.3044	0.4642	0.5455	0.7385
No log	5.84	146	0.3480	0.3818	0.6313	0.7946
No log	5.92	148	0.5000	0.3610	0.6919	0.8318
No log	6.0	150	0.5036	0.3739	0.6818	0.8257
No log	6.08	152	0.3760	0.4189	0.6263	0.7914
No log	6.16	154	0.3778	0.4530	0.6061	0.7785
No log	6.24	156	0.4451	0.4212	0.6515	0.8072
No log	6.32	158	0.3880	0.4755	0.5960	0.7720
No log	6.4	160	0.3675	0.4900	0.5909	0.7687
No log	6.48	162	0.3907	0.4537	0.6162	0.7850
No log	6.5600	164	0.5153	0.3840	0.6869	0.8288
No log	6.64	166	0.5261	0.3841	0.6818	0.8257
No log	6.72	168	0.3776	0.4378	0.6263	0.7914
No log	6.8	170	0.3082	0.4965	0.5758	0.7588
No log	6.88	172	0.3131	0.5	0.5657	0.7521
No log	6.96	174	0.4123	0.4248	0.6313	0.7946
No log	7.04	176	0.4961	0.4013	0.6616	0.8134
No log	7.12	178	0.4301	0.4168	0.6364	0.7977
No log	7.2	180	0.4097	0.4008	0.6515	0.8072
No log	7.28	182	0.4196	0.4052	0.6515	0.8072
No log	7.36	184	0.4523	0.4062	0.6515	0.8072
No log	7.44	186	0.4826	0.3922	0.6717	0.8196
No log	7.52	188	0.4204	0.3995	0.6566	0.8103
No log	7.6	190	0.3847	0.4393	0.6212	0.7882
No log	7.68	192	0.4546	0.4023	0.6667	0.8165
No log	7.76	194	0.5324	0.4033	0.6667	0.8165
No log	7.84	196	0.4910	0.4044	0.6667	0.8165
No log	7.92	198	0.3985	0.4514	0.6111	0.7817
No log	8.0	200	0.3686	0.4524	0.6212	0.7882
No log	8.08	202	0.4120	0.4303	0.6414	0.8009
No log	8.16	204	0.5442	0.4144	0.6566	0.8103
No log	8.24	206	0.6009	0.3549	0.7323	0.8558
No log	8.32	208	0.5373	0.4123	0.6566	0.8103
No log	8.4	210	0.4305	0.4200	0.6566	0.8103
No log	8.48	212	0.3996	0.4419	0.6263	0.7914
No log	8.56	214	0.4324	0.4145	0.6616	0.8134
No log	8.64	216	0.4591	0.4099	0.6667	0.8165
No log	8.72	218	0.4528	0.4099	0.6667	0.8165
No log	8.8	220	0.4270	0.4200	0.6566	0.8103
No log	8.88	222	0.4406	0.4131	0.6667	0.8165
No log	8.96	224	0.4662	0.3888	0.6818	0.8257
No log	9.04	226	0.4897	0.3888	0.6818	0.8257
No log	9.12	228	0.4641	0.3888	0.6818	0.8257
No log	9.2	230	0.4133	0.4358	0.6364	0.7977
No log	9.28	232	0.3904	0.4574	0.6111	0.7817
No log	9.36	234	0.3988	0.4528	0.6162	0.7850
No log	9.44	236	0.4298	0.3956	0.6768	0.8227
No log	9.52	238	0.4769	0.3798	0.6818	0.8257
No log	9.6	240	0.5046	0.3774	0.6919	0.8318
No log	9.68	242	0.5039	0.3774	0.6919	0.8318
No log	9.76	244	0.4898	0.3729	0.6919	0.8318
No log	9.84	246	0.4814	0.3671	0.6970	0.8348
No log	9.92	248	0.4707	0.3889	0.6768	0.8227
No log	10.0	250	0.4675	0.3889	0.6768	0.8227

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu118
Datasets 2.21.0
Tokenizers 0.19.1

Task: Text Classification
Library: transformers
Type: bert
License: apache-2.0
Created: Nov 3, 2024
Updated: Nov 3, 2024