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

Loss: 0.0013
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: 2
eval_batch_size: 2
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.1299	0.125	5	1.0802	0.4051
1.087	0.25	10	1.0728	0.4051
1.0963	0.375	15	1.0612	0.4051
1.0295	0.5	20	1.0474	0.4051
1.0555	0.625	25	1.0342	0.4051
1.0977	0.75	30	1.0229	0.4177
1.0578	0.875	35	1.0083	0.4051
1.0634	1.0	40	0.9898	0.4051
1.101	1.125	45	0.9580	0.4177
0.975	1.25	50	0.9324	0.5696
0.8884	1.375	55	0.9234	0.4810
0.952	1.5	60	0.9370	0.4430
0.9615	1.625	65	0.9046	0.4810
0.9397	1.75	70	0.8182	0.5823
0.8171	1.875	75	0.7518	0.7215
0.9969	2.0	80	0.7126	0.8101
0.6124	2.125	85	0.7107	0.7848
0.768	2.25	90	0.6562	0.8481
0.5737	2.375	95	0.6030	0.8481
0.5334	2.5	100	0.5290	0.8734
0.3639	2.625	105	0.5277	0.8481
1.0796	2.75	110	0.4561	0.8734
0.4922	2.875	115	0.3470	0.8987
0.6645	3.0	120	0.3213	0.9367
0.3425	3.125	125	0.2897	0.9367
0.4311	3.25	130	0.2757	0.9241
0.2699	3.375	135	0.2918	0.9241
0.5033	3.5	140	0.3021	0.8987
0.1645	3.625	145	0.2710	0.9114
0.4956	3.75	150	0.2695	0.8987
0.4773	3.875	155	0.2175	0.9241
0.1566	4.0	160	0.1788	0.9494
0.331	4.125	165	0.1332	0.9747
0.0865	4.25	170	0.1716	0.9367
0.2109	4.375	175	0.1431	0.9494
0.0673	4.5	180	0.1536	0.9367
0.3188	4.625	185	0.1312	0.9494
0.1084	4.75	190	0.0994	0.9747
0.0454	4.875	195	0.1158	0.9747
0.2056	5.0	200	0.0880	0.9873
0.0444	5.125	205	0.0947	0.9747
0.0304	5.25	210	0.1205	0.9747
0.0187	5.375	215	0.1289	0.9747
0.0205	5.5	220	0.1390	0.9747
0.022	5.625	225	0.1592	0.9620
0.0163	5.75	230	0.1652	0.9620
0.7803	5.875	235	0.0975	0.9747
0.0137	6.0	240	0.0683	0.9873
0.0155	6.125	245	0.0700	0.9873
0.4981	6.25	250	0.0743	0.9873
0.0127	6.375	255	0.0730	0.9873
0.031	6.5	260	0.0733	0.9873
0.0139	6.625	265	0.0763	0.9873
0.0104	6.75	270	0.1024	0.9620
0.0158	6.875	275	0.1015	0.9620
0.0081	7.0	280	0.1042	0.9747
0.4143	7.125	285	0.1005	0.9747
0.0104	7.25	290	0.0916	0.9747
0.009	7.375	295	0.1089	0.9620
0.0087	7.5	300	0.1176	0.9620
0.1324	7.625	305	0.0496	0.9873
0.0094	7.75	310	0.0385	0.9873
0.0078	7.875	315	0.0336	0.9873
0.0077	8.0	320	0.0342	0.9873
0.008	8.125	325	0.0450	0.9873
0.0064	8.25	330	0.0590	0.9873
0.3151	8.375	335	0.0578	0.9873
0.007	8.5	340	0.0162	0.9873
0.0073	8.625	345	0.0092	1.0
0.0063	8.75	350	0.0089	1.0
0.0064	8.875	355	0.0074	1.0
0.0088	9.0	360	0.0127	0.9873
0.0074	9.125	365	0.0260	0.9873
0.0055	9.25	370	0.0355	0.9873
0.0051	9.375	375	0.0415	0.9873
0.0042	9.5	380	0.0409	0.9873
0.0053	9.625	385	0.0408	0.9873
0.3642	9.75	390	0.0096	1.0
0.0049	9.875	395	0.0032	1.0
0.0043	10.0	400	0.0029	1.0
0.0059	10.125	405	0.0029	1.0
0.0048	10.25	410	0.0030	1.0
0.0067	10.375	415	0.0030	1.0
0.0035	10.5	420	0.0029	1.0
0.0041	10.625	425	0.0042	1.0
0.0039	10.75	430	0.0084	1.0
0.2433	10.875	435	0.0027	1.0
0.0043	11.0	440	0.0024	1.0
0.0036	11.125	445	0.0024	1.0
0.0042	11.25	450	0.0024	1.0
0.0037	11.375	455	0.0025	1.0
0.0074	11.5	460	0.0023	1.0
0.0034	11.625	465	0.0022	1.0
0.0039	11.75	470	0.0021	1.0
0.0038	11.875	475	0.0021	1.0
0.0029	12.0	480	0.0021	1.0
0.0033	12.125	485	0.0020	1.0
0.0029	12.25	490	0.0020	1.0
0.0032	12.375	495	0.0020	1.0
0.0029	12.5	500	0.0020	1.0
0.0039	12.625	505	0.0019	1.0
0.0041	12.75	510	0.0019	1.0
0.0028	12.875	515	0.0020	1.0
0.0028	13.0	520	0.0020	1.0
0.0027	13.125	525	0.0020	1.0
0.0028	13.25	530	0.0020	1.0
0.0024	13.375	535	0.0019	1.0
0.0032	13.5	540	0.0019	1.0
0.0039	13.625	545	0.0019	1.0
0.0021	13.75	550	0.0018	1.0
0.0037	13.875	555	0.0018	1.0
0.0033	14.0	560	0.0017	1.0
0.003	14.125	565	0.0017	1.0
0.0025	14.25	570	0.0016	1.0
0.0032	14.375	575	0.0016	1.0
0.0025	14.5	580	0.0016	1.0
0.0026	14.625	585	0.0016	1.0
0.0024	14.75	590	0.0016	1.0
0.003	14.875	595	0.0016	1.0
0.0028	15.0	600	0.0015	1.0
0.0025	15.125	605	0.0015	1.0
0.0024	15.25	610	0.0015	1.0
0.0026	15.375	615	0.0015	1.0
0.0042	15.5	620	0.0015	1.0
0.0024	15.625	625	0.0015	1.0
0.0025	15.75	630	0.0015	1.0
0.0024	15.875	635	0.0015	1.0
0.0029	16.0	640	0.0015	1.0
0.0027	16.125	645	0.0015	1.0
0.0022	16.25	650	0.0015	1.0
0.0024	16.375	655	0.0014	1.0
0.0025	16.5	660	0.0014	1.0
0.0023	16.625	665	0.0014	1.0
0.0024	16.75	670	0.0014	1.0
0.0024	16.875	675	0.0014	1.0
0.0021	17.0	680	0.0014	1.0
0.0022	17.125	685	0.0014	1.0
0.0025	17.25	690	0.0014	1.0
0.0022	17.375	695	0.0014	1.0
0.0023	17.5	700	0.0014	1.0
0.0023	17.625	705	0.0013	1.0
0.0036	17.75	710	0.0013	1.0
0.0022	17.875	715	0.0013	1.0
0.0024	18.0	720	0.0013	1.0
0.0023	18.125	725	0.0013	1.0
0.0019	18.25	730	0.0013	1.0
0.0021	18.375	735	0.0013	1.0
0.0023	18.5	740	0.0013	1.0
0.003	18.625	745	0.0013	1.0
0.0021	18.75	750	0.0013	1.0
0.0021	18.875	755	0.0013	1.0
0.0018	19.0	760	0.0013	1.0
0.002	19.125	765	0.0013	1.0
0.0017	19.25	770	0.0013	1.0
0.0025	19.375	775	0.0013	1.0
0.0022	19.5	780	0.0013	1.0
0.0027	19.625	785	0.0013	1.0
0.0024	19.75	790	0.0013	1.0
0.0024	19.875	795	0.0013	1.0
0.0025	20.0	800	0.0013	1.0

Framework versions

Transformers 4.41.0
Pytorch 2.2.2
Datasets 2.19.1
Tokenizers 0.19.1

ankitb10/distilbert_survey_response_classification

distilbert_survey_response_classification

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ankitb10/
distilbert_survey_response_classification