distilbert_sa_pre-training-complete

gokuls/distilbert_sa_pre-training-complete

distilbert_sa_pre-training-complete is a fill-mask model from gokuls. Use it when you need the model to fill a missing word. 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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Hugging Face

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At a glance

Task: Fill-Mask
Library: transformers
License: apache-2.0
Model type: distilbert
Access: Public
Created: Jan 28, 2023
Updated: Jan 29, 2023
SHA: f40b32ab

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Datasets

wikitext

Model card

From the Hugging Face model README

distilbert_sa_pre-training-complete

This model is a fine-tuned version of distilbert-base-uncased on the wikitext wikitext-103-raw-v1 dataset. It achieves the following results on the evaluation set:

Loss: 1.3367
Accuracy: 0.7119

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: 64
eval_batch_size: 64
seed: 10
distributed_type: multi-GPU
num_devices: 2
total_train_batch_size: 128
total_eval_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
training_steps: 300000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.8819	1.0	1787	1.6350	0.6570
1.7699	2.0	3574	1.5847	0.6664
1.7308	3.0	5361	1.5857	0.6678
1.7062	4.0	7148	1.5679	0.6684
1.6858	5.0	8935	1.5573	0.6689
1.6684	6.0	10722	1.5591	0.6687
1.6545	7.0	12509	1.5253	0.6756
1.6438	8.0	14296	1.5304	0.6748
1.631	9.0	16083	1.4976	0.6805
1.6236	10.0	17870	1.5153	0.6773
1.613	11.0	19657	1.5010	0.6786
1.6046	12.0	21444	1.5069	0.6818
1.5963	13.0	23231	1.4796	0.6805
1.5906	14.0	25018	1.4858	0.6809
1.5833	15.0	26805	1.4962	0.6806
1.5771	16.0	28592	1.4749	0.6826
1.5703	17.0	30379	1.4724	0.6874
1.5663	18.0	32166	1.4946	0.6827
1.5614	19.0	33953	1.4934	0.6798
1.5558	20.0	35740	1.4512	0.6893
1.5508	21.0	37527	1.4417	0.6921
1.5466	22.0	39314	1.4744	0.6837
1.5427	23.0	41101	1.4530	0.6904
1.5382	24.0	42888	1.4378	0.6900
1.5342	25.0	44675	1.4732	0.6820
1.5293	26.0	46462	1.4609	0.6865
1.5259	27.0	48249	1.4400	0.6942
1.5223	28.0	50036	1.4586	0.6879
1.5197	29.0	51823	1.4371	0.6906
1.5161	30.0	53610	1.4335	0.6908
1.5126	31.0	55397	1.4159	0.6978
1.5087	32.0	57184	1.4412	0.6858
1.5061	33.0	58971	1.4181	0.6958
1.5035	34.0	60758	1.4357	0.6903
1.5011	35.0	62545	1.4147	0.6914
1.4967	36.0	64332	1.4251	0.6931
1.4954	37.0	66119	1.4366	0.6910
1.4925	38.0	67906	1.4488	0.6896
1.4889	39.0	69693	1.4336	0.6907
1.4866	40.0	71480	1.4226	0.6945
1.4838	41.0	73267	1.4210	0.6951
1.4821	42.0	75054	1.4081	0.6944
1.4792	43.0	76841	1.4496	0.6895
1.4778	44.0	78628	1.4263	0.6925
1.4754	45.0	80415	1.3995	0.6982
1.4736	46.0	82202	1.3958	0.6992
1.4702	47.0	83989	1.4073	0.6989
1.4683	48.0	85776	1.3917	0.6996
1.4663	49.0	87563	1.4109	0.6956
1.4648	50.0	89350	1.4003	0.6991
1.4619	51.0	91137	1.3847	0.7040
1.4599	52.0	92924	1.4323	0.6936
1.4596	53.0	94711	1.3935	0.6986
1.4567	54.0	96498	1.4122	0.6974
1.455	55.0	98285	1.3975	0.6977
1.4533	56.0	100072	1.3907	0.7004
1.4513	57.0	101859	1.3957	0.6997
1.45	58.0	103646	1.3907	0.7001
1.4466	59.0	105433	1.4063	0.6988
1.4455	60.0	107220	1.3600	0.7036
1.4439	61.0	109007	1.3941	0.7015
1.4432	62.0	110794	1.3854	0.7016
1.4404	63.0	112581	1.4080	0.6973
1.4397	64.0	114368	1.3924	0.7011
1.4366	65.0	116155	1.3872	0.7049
1.4358	66.0	117942	1.3924	0.7010
1.4339	67.0	119729	1.3895	0.6989
1.4329	68.0	121516	1.4007	0.7002
1.4319	69.0	123303	1.3672	0.7047
1.4287	70.0	125090	1.3892	0.7021
1.4284	71.0	126877	1.3674	0.7016
1.4265	72.0	128664	1.3983	0.6962
1.4254	73.0	130451	1.3665	0.7006
1.4231	74.0	132238	1.3852	0.7003
1.4228	75.0	134025	1.4328	0.6945
1.4217	76.0	135812	1.3831	0.7031
1.4198	77.0	137599	1.3897	0.7017
1.4183	78.0	139386	1.3790	0.7027
1.417	79.0	141173	1.3697	0.7063
1.4179	80.0	142960	1.3805	0.7008
1.414	81.0	144747	1.3579	0.7051
1.4132	82.0	146534	1.3727	0.7054
1.4112	83.0	148321	1.3763	0.7014
1.412	84.0	150108	1.3806	0.7051
1.4103	85.0	151895	1.3889	0.7022
1.4082	86.0	153682	1.3886	0.6993
1.4068	87.0	155469	1.3721	0.7025
1.4068	88.0	157256	1.3589	0.7050
1.4043	89.0	159043	1.3599	0.7043
1.4042	90.0	160830	1.3675	0.7055
1.4033	91.0	162617	1.3720	0.7031
1.402	92.0	164404	1.3506	0.7067
1.4004	93.0	166191	1.3833	0.7017
1.4001	94.0	167978	1.3734	0.7021
1.398	95.0	169765	1.3792	0.7037
1.3983	96.0	171552	1.3676	0.7050
1.3966	97.0	173339	1.3888	0.7027
1.3953	98.0	175126	1.3928	0.7004
1.3934	99.0	176913	1.3824	0.7011
1.3928	100.0	178700	1.3829	0.7040
1.3919	101.0	180487	1.3534	0.7046
1.3902	102.0	182274	1.3559	0.7048
1.3896	103.0	184061	1.3682	0.7045
1.3895	104.0	185848	1.3643	0.7053
1.3884	105.0	187635	1.3854	0.7018
1.3878	106.0	189422	1.3702	0.7038
1.3865	107.0	191209	1.3530	0.7056
1.3842	108.0	192996	1.3804	0.7008
1.3836	109.0	194783	1.3508	0.7073
1.3831	110.0	196570	1.3821	0.7002
1.3828	111.0	198357	1.3552	0.7063
1.382	112.0	200144	1.3567	0.7090
1.3806	113.0	201931	1.3915	0.7022
1.3802	114.0	203718	1.3661	0.7098
1.3789	115.0	205505	1.3586	0.7071
1.3789	116.0	207292	1.3643	0.7049
1.3782	117.0	209079	1.3716	0.7037
1.3763	118.0	210866	1.3476	0.7077
1.3762	119.0	212653	1.3519	0.7067
1.3741	120.0	214440	1.3736	0.7027
1.3741	121.0	216227	1.3395	0.7073
1.3732	122.0	218014	1.3724	0.7020
1.3724	123.0	219801	1.3724	0.7025
1.3715	124.0	221588	1.3572	0.7083
1.3713	125.0	223375	1.3430	0.7101
1.369	126.0	225162	1.3457	0.7103
1.3687	127.0	226949	1.3457	0.7112
1.3698	128.0	228736	1.3649	0.7024
1.3683	129.0	230523	1.3422	0.7105
1.3668	130.0	232310	1.3531	0.7058
1.3675	131.0	234097	1.3607	0.7059
1.3661	132.0	235884	1.3553	0.7063
1.3642	133.0	237671	1.3541	0.7054
1.364	134.0	239458	1.3564	0.7080
1.3635	135.0	241245	1.3634	0.7055
1.3621	136.0	243032	1.3689	0.7043
1.3623	137.0	244819	1.3668	0.7087
1.3614	138.0	246606	1.3535	0.7061
1.3614	139.0	248393	1.3588	0.7086
1.3588	140.0	250180	1.3472	0.7091
1.3598	141.0	251967	1.3425	0.7085
1.3601	142.0	253754	1.3533	0.7099
1.3585	143.0	255541	1.3298	0.7112
1.3586	144.0	257328	1.3583	0.7036
1.3583	145.0	259115	1.3455	0.7081
1.3567	146.0	260902	1.3674	0.7045
1.3561	147.0	262689	1.3705	0.7056
1.3552	148.0	264476	1.3506	0.7080
1.3547	149.0	266263	1.3457	0.7120
1.3538	150.0	268050	1.3245	0.7100
1.3543	151.0	269837	1.3278	0.7110
1.3549	152.0	271624	1.3345	0.7103
1.3528	153.0	273411	1.3336	0.7089
1.3528	154.0	275198	1.3620	0.7075
1.3531	155.0	276985	1.3383	0.7120
1.3525	156.0	278772	1.3377	0.7091
1.3509	157.0	280559	1.3639	0.7054
1.3502	158.0	282346	1.3456	0.7091
1.351	159.0	284133	1.3359	0.7110
1.3493	160.0	285920	1.3361	0.7129
1.3497	161.0	287707	1.3350	0.7091
1.3484	162.0	289494	1.3582	0.7075
1.35	163.0	291281	1.3441	0.7100
1.3485	164.0	293068	1.3227	0.7093
1.3485	165.0	294855	1.3386	0.7118
1.3483	166.0	296642	1.3470	0.7087
1.3481	167.0	298429	1.3405	0.7087
1.3465	167.88	300000	1.3382	0.7062

Framework versions

Transformers 4.26.0
Pytorch 1.14.0a0+410ce96
Datasets 2.9.0
Tokenizers 0.13.2

Task: Fill-Mask
Library: transformers
Type: distilbert
License: apache-2.0
Created: Jan 28, 2023
Updated: Jan 29, 2023