Helsinki-shirsh-finetuned-translation-english-to-hindi

This model is a fine-tuned version of yashcode00/Helsinki-shirsh-finetuned-translation-english-to-hindi on an unknown dataset. It achieves the following results on the evaluation set:

• Train Loss: 2.0769
• Validation Loss: 2.3327
• Epoch: 499

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:

• optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
• training_precision: float32

Training results

Train Loss	Validation Loss	Epoch
2.7823	2.3889	0
2.1907	2.3327	1
2.0769	2.3327	2
2.0727	2.3327	3
2.0724	2.3327	4
2.0795	2.3327	5
2.0738	2.3327	6
2.0750	2.3327	7
2.0693	2.3327	8
2.0698	2.3327	9
2.0733	2.3327	10
2.0804	2.3327	11
2.0734	2.3327	12
2.0691	2.3327	13
2.0754	2.3327	14
2.0709	2.3327	15
2.0728	2.3327	16
2.0756	2.3327	17
2.0733	2.3327	18
2.0747	2.3327	19
2.0740	2.3327	20
2.0722	2.3327	21
2.0763	2.3327	22
2.0775	2.3327	23
2.0747	2.3327	24
2.0776	2.3327	25
2.0729	2.3327	26
2.0713	2.3327	27
2.0718	2.3327	28
2.0779	2.3327	29
2.0746	2.3327	30
2.0733	2.3327	31
2.0738	2.3327	32
2.0740	2.3327	33
2.0760	2.3327	34
2.0759	2.3327	35
2.0716	2.3327	36
2.0766	2.3327	37
2.0792	2.3327	38
2.0666	2.3327	39
2.0732	2.3327	40
2.0726	2.3327	41
2.0755	2.3327	42
2.0722	2.3327	43
2.0742	2.3327	44
2.0720	2.3327	45
2.0695	2.3327	46
2.0705	2.3327	47
2.0724	2.3327	48
2.0678	2.3327	49
2.0726	2.3327	50
2.0740	2.3327	51
2.0770	2.3327	52
2.0754	2.3327	53
2.0796	2.3327	54
2.0720	2.3327	55
2.0720	2.3327	56
2.0742	2.3327	57
2.0757	2.3327	58
2.0764	2.3327	59
2.0730	2.3327	60
2.0757	2.3327	61
2.0675	2.3327	62
2.0766	2.3327	63
2.0790	2.3327	64
2.0672	2.3327	65
2.0748	2.3327	66
2.0779	2.3327	67
2.0742	2.3327	68
2.0719	2.3327	69
2.0726	2.3327	70
2.0706	2.3327	71
2.0681	2.3327	72
2.0707	2.3327	73
2.0704	2.3327	74
2.0761	2.3327	75
2.0707	2.3327	76
2.0740	2.3327	77
2.0745	2.3327	78
2.0716	2.3327	79
2.0815	2.3327	80
2.0720	2.3327	81
2.0743	2.3327	82
2.0778	2.3327	83
2.0707	2.3327	84
2.0715	2.3327	85
2.0737	2.3327	86
2.0784	2.3327	87
2.0746	2.3327	88
2.0743	2.3327	89
2.0721	2.3327	90
2.0736	2.3327	91
2.0724	2.3327	92
2.0705	2.3327	93
2.0751	2.3327	94
2.0773	2.3327	95
2.0786	2.3327	96
2.0753	2.3327	97
2.0693	2.3327	98
2.0759	2.3327	99
2.0745	2.3327	100
2.0734	2.3327	101
2.0687	2.3327	102
2.0789	2.3327	103
2.0733	2.3327	104
2.0803	2.3327	105
2.0793	2.3327	106
2.0769	2.3327	107
2.0784	2.3327	108
2.0754	2.3327	109
2.0719	2.3327	110
2.0739	2.3327	111
2.0699	2.3327	112
2.0754	2.3327	113
2.0737	2.3327	114
2.0748	2.3327	115
2.0733	2.3327	116
2.0698	2.3327	117
2.0697	2.3327	118
2.0721	2.3327	119
2.0766	2.3327	120
2.0725	2.3327	121
2.0777	2.3327	122
2.0728	2.3327	123
2.0766	2.3327	124
2.0733	2.3327	125
2.0782	2.3327	126
2.0706	2.3327	127
2.0759	2.3327	128
2.0746	2.3327	129
2.0766	2.3327	130
2.0738	2.3327	131
2.0682	2.3327	132
2.0766	2.3327	133
2.0709	2.3327	134
2.0763	2.3327	135
2.0742	2.3327	136
2.0710	2.3327	137
2.0729	2.3327	138
2.0752	2.3327	139
2.0709	2.3327	140
2.0750	2.3327	141
2.0760	2.3327	142
2.0787	2.3327	143
2.0722	2.3327	144
2.0784	2.3327	145
2.0766	2.3327	146
2.0718	2.3327	147
2.0755	2.3327	148
2.0781	2.3327	149
2.0719	2.3327	150
2.0729	2.3327	151
2.0704	2.3327	152
2.0761	2.3327	153
2.0744	2.3327	154
2.0746	2.3327	155
2.0807	2.3327	156
2.0703	2.3327	157
2.0750	2.3327	158
2.0724	2.3327	159
2.0708	2.3327	160
2.0749	2.3327	161
2.0755	2.3327	162
2.0730	2.3327	163
2.0730	2.3327	164
2.0747	2.3327	165
2.0738	2.3327	166
2.0694	2.3327	167
2.0724	2.3327	168
2.0729	2.3327	169
2.0719	2.3327	170
2.0738	2.3327	171
2.0710	2.3327	172
2.0752	2.3327	173
2.0742	2.3327	174
2.0747	2.3327	175
2.0721	2.3327	176
2.0718	2.3327	177
2.0703	2.3327	178
2.0739	2.3327	179
2.0710	2.3327	180
2.0759	2.3327	181
2.0705	2.3327	182
2.0705	2.3327	183
2.0749	2.3327	184
2.0750	2.3327	185
2.0745	2.3327	186
2.0712	2.3327	187
2.0768	2.3327	188
2.0714	2.3327	189
2.0739	2.3327	190
2.0737	2.3327	191
2.0774	2.3327	192
2.0725	2.3327	193
2.0781	2.3327	194
2.0714	2.3327	195
2.0728	2.3327	196
2.0781	2.3327	197
2.0718	2.3327	198
2.0723	2.3327	199
2.0736	2.3327	200
2.0743	2.3327	201
2.0738	2.3327	202
2.0747	2.3327	203
2.0704	2.3327	204
2.0714	2.3327	205
2.0764	2.3327	206
2.0804	2.3327	207
2.0784	2.3327	208
2.0768	2.3327	209
2.0784	2.3327	210
2.0686	2.3327	211
2.0763	2.3327	212
2.0736	2.3327	213
2.0671	2.3327	214
2.0737	2.3327	215
2.0754	2.3327	216
2.0796	2.3327	217
2.0742	2.3327	218
2.0747	2.3327	219
2.0723	2.3327	220
2.0777	2.3327	221
2.0729	2.3327	222
2.0718	2.3327	223
2.0724	2.3327	224
2.0650	2.3327	225
2.0709	2.3327	226
2.0748	2.3327	227
2.0701	2.3327	228
2.0745	2.3327	229
2.0741	2.3327	230
2.0713	2.3327	231
2.0739	2.3327	232
2.0732	2.3327	233
2.0726	2.3327	234
2.0704	2.3327	235
2.0742	2.3327	236
2.0715	2.3327	237
2.0726	2.3327	238
2.0767	2.3327	239
2.0727	2.3327	240
2.0756	2.3327	241
2.0714	2.3327	242
2.0713	2.3327	243
2.0723	2.3327	244
2.0765	2.3327	245
2.0699	2.3327	246
2.0727	2.3327	247
2.0761	2.3327	248
2.0764	2.3327	249
2.0773	2.3327	250
2.0730	2.3327	251
2.0736	2.3327	252
2.0757	2.3327	253
2.0744	2.3327	254
2.0765	2.3327	255
2.0749	2.3327	256
2.0731	2.3327	257
2.0682	2.3327	258
2.0709	2.3327	259
2.0754	2.3327	260
2.0723	2.3327	261
2.0764	2.3327	262
2.0748	2.3327	263
2.0742	2.3327	264
2.0741	2.3327	265
2.0685	2.3327	266
2.0699	2.3327	267
2.0687	2.3327	268
2.0693	2.3327	269
2.0814	2.3327	270
2.0743	2.3327	271
2.0741	2.3327	272
2.0740	2.3327	273
2.0780	2.3327	274
2.0727	2.3327	275
2.0732	2.3327	276
2.0766	2.3327	277
2.0753	2.3327	278
2.0737	2.3327	279
2.0703	2.3327	280
2.0725	2.3327	281
2.0710	2.3327	282
2.0752	2.3327	283
2.0756	2.3327	284
2.0776	2.3327	285
2.0715	2.3327	286
2.0704	2.3327	287
2.0733	2.3327	288
2.0731	2.3327	289
2.0765	2.3327	290
2.0745	2.3327	291
2.0755	2.3327	292
2.0728	2.3327	293
2.0701	2.3327	294
2.0745	2.3327	295
2.0730	2.3327	296
2.0698	2.3327	297
2.0753	2.3327	298
2.0694	2.3327	299
2.0764	2.3327	300
2.0728	2.3327	301
2.0719	2.3327	302
2.0717	2.3327	303
2.0746	2.3327	304
2.0717	2.3327	305
2.0766	2.3327	306
2.0697	2.3327	307
2.0746	2.3327	308
2.0744	2.3327	309
2.0727	2.3327	310
2.0771	2.3327	311
2.0696	2.3327	312
2.0745	2.3327	313
2.0755	2.3327	314
2.0794	2.3327	315
2.0761	2.3327	316
2.0755	2.3327	317
2.0736	2.3327	318
2.0798	2.3327	319
2.0708	2.3327	320
2.0757	2.3327	321
2.0681	2.3327	322
2.0770	2.3327	323
2.0703	2.3327	324
2.0760	2.3327	325
2.0742	2.3327	326
2.0774	2.3327	327
2.0757	2.3327	328
2.0737	2.3327	329
2.0708	2.3327	330
2.0769	2.3327	331
2.0755	2.3327	332
2.0751	2.3327	333
2.0778	2.3327	334
2.0719	2.3327	335
2.0756	2.3327	336
2.0716	2.3327	337
2.0735	2.3327	338
2.0718	2.3327	339
2.0742	2.3327	340
2.0705	2.3327	341
2.0768	2.3327	342
2.0737	2.3327	343
2.0694	2.3327	344
2.0710	2.3327	345
2.0760	2.3327	346
2.0742	2.3327	347
2.0689	2.3327	348
2.0770	2.3327	349
2.0736	2.3327	350
2.0815	2.3327	351
2.0809	2.3327	352
2.0719	2.3327	353
2.0777	2.3327	354
2.0732	2.3327	355
2.0748	2.3327	356
2.0750	2.3327	357
2.0757	2.3327	358
2.0767	2.3327	359
2.0679	2.3327	360
2.0698	2.3327	361
2.0766	2.3327	362
2.0769	2.3327	363
2.0735	2.3327	364
2.0715	2.3327	365
2.0756	2.3327	366
2.0742	2.3327	367
2.0752	2.3327	368
2.0694	2.3327	369
2.0736	2.3327	370
2.0731	2.3327	371
2.0744	2.3327	372
2.0745	2.3327	373
2.0748	2.3327	374
2.0744	2.3327	375
2.0758	2.3327	376
2.0730	2.3327	377
2.0754	2.3327	378
2.0739	2.3327	379
2.0766	2.3327	380
2.0733	2.3327	381
2.0714	2.3327	382
2.0731	2.3327	383
2.0741	2.3327	384
2.0753	2.3327	385
2.0682	2.3327	386
2.0696	2.3327	387
2.0679	2.3327	388
2.0723	2.3327	389
2.0689	2.3327	390
2.0695	2.3327	391
2.0761	2.3327	392
2.0741	2.3327	393
2.0735	2.3327	394
2.0729	2.3327	395
2.0767	2.3327	396
2.0724	2.3327	397
2.0765	2.3327	398
2.0734	2.3327	399
2.0732	2.3327	400
2.0734	2.3327	401
2.0738	2.3327	402
2.0738	2.3327	403
2.0713	2.3327	404
2.0765	2.3327	405
2.0741	2.3327	406
2.0704	2.3327	407
2.0679	2.3327	408
2.0776	2.3327	409
2.0737	2.3327	410
2.0763	2.3327	411
2.0736	2.3327	412
2.0728	2.3327	413
2.0764	2.3327	414
2.0781	2.3327	415
2.0706	2.3327	416
2.0718	2.3327	417
2.0770	2.3327	418
2.0740	2.3327	419
2.0709	2.3327	420
2.0715	2.3327	421
2.0762	2.3327	422
2.0740	2.3327	423
2.0763	2.3327	424
2.0707	2.3327	425
2.0719	2.3327	426
2.0758	2.3327	427
2.0743	2.3327	428
2.0726	2.3327	429
2.0763	2.3327	430
2.0780	2.3327	431
2.0726	2.3327	432
2.0739	2.3327	433
2.0772	2.3327	434
2.0758	2.3327	435
2.0715	2.3327	436
2.0778	2.3327	437
2.0692	2.3327	438
2.0732	2.3327	439
2.0717	2.3327	440
2.0721	2.3327	441
2.0790	2.3327	442
2.0711	2.3327	443
2.0710	2.3327	444
2.0713	2.3327	445
2.0768	2.3327	446
2.0781	2.3327	447
2.0720	2.3327	448
2.0754	2.3327	449
2.0768	2.3327	450
2.0691	2.3327	451
2.0742	2.3327	452
2.0727	2.3327	453
2.0758	2.3327	454
2.0715	2.3327	455
2.0729	2.3327	456
2.0689	2.3327	457
2.0776	2.3327	458
2.0733	2.3327	459
2.0746	2.3327	460
2.0742	2.3327	461
2.0758	2.3327	462
2.0762	2.3327	463
2.0693	2.3327	464
2.0766	2.3327	465
2.0712	2.3327	466
2.0723	2.3327	467
2.0746	2.3327	468
2.0720	2.3327	469
2.0732	2.3327	470
2.0723	2.3327	471
2.0747	2.3327	472
2.0738	2.3327	473
2.0784	2.3327	474
2.0760	2.3327	475
2.0746	2.3327	476
2.0752	2.3327	477
2.0788	2.3327	478
2.0742	2.3327	479
2.0768	2.3327	480
2.0728	2.3327	481
2.0754	2.3327	482
2.0768	2.3327	483
2.0700	2.3327	484
2.0736	2.3327	485
2.0717	2.3327	486
2.0747	2.3327	487
2.0692	2.3327	488
2.0775	2.3327	489
2.0729	2.3327	490
2.0727	2.3327	491
2.0733	2.3327	492
2.0734	2.3327	493
2.0706	2.3327	494
2.0725	2.3327	495
2.0715	2.3327	496
2.0756	2.3327	497
2.0777	2.3327	498
2.0769	2.3327	499

Framework versions

• Transformers 4.34.1
• TensorFlow 2.12.0
• Datasets 2.1.0
• Tokenizers 0.14.1