Malvegil/prologue_creator-model

This model is a fine-tuned version of Malvegil/prologue_creator-model on an unknown dataset. It achieves the following results on the evaluation set:

• Train Loss: 0.8078
• Validation Loss: 5.6164
• Epoch: 235

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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
• training_precision: float32

Training results

Train Loss	Validation Loss	Epoch
9.6983	8.6325	0
7.5394	7.4380	1
6.8454	6.9855	2
6.5034	6.8347	3
6.3289	6.6883	4
6.1540	6.5173	5
6.0244	6.3997	6
5.9258	6.3245	7
5.8095	6.2648	8
5.7688	6.1865	9
5.6244	6.1095	10
5.5619	6.0552	11
5.4940	6.0000	12
5.3603	5.9456	13
5.2852	5.8861	14
5.2218	5.8256	15
5.1715	5.7722	16
5.0556	5.7236	17
5.0339	5.6669	18
4.8776	5.6155	19
4.9133	5.5683	20
4.8350	5.5166	21
4.6951	5.4712	22
4.7144	5.4447	23
4.6560	5.3954	24
4.6125	5.3449	25
4.5494	5.3382	26
4.3536	5.2989	27
4.3613	5.2537	28
4.3123	5.2269	29
4.2677	5.2115	30
4.2407	5.1566	31
4.1543	5.1413	32
4.0961	5.1284	33
4.0779	5.0771	34
4.0303	5.0722	35
3.9734	5.0550	36
3.9541	5.0060	37
3.9188	4.9941	38
3.8350	4.9851	39
3.8081	4.9648	40
3.7395	4.9533	41
3.7045	4.9112	42
3.6765	4.9185	43
3.5667	4.8981	44
3.5491	4.8510	45
3.5547	4.8688	46
3.5317	4.8393	47
3.4210	4.8366	48
3.4503	4.8120	49
3.4187	4.8045	50
3.3313	4.7899	51
3.2695	4.7733	52
3.2980	4.7643	53
3.2614	4.7592	54
3.2011	4.7353	55
3.1756	4.7323	56
3.1325	4.7405	57
3.1642	4.6849	58
3.0915	4.7039	59
3.0950	4.6905	60
2.9946	4.6777	61
3.0338	4.7064	62
2.9554	4.6617	63
2.9999	4.6723	64
2.9410	4.6397	65
2.9157	4.6493	66
2.8930	4.6641	67
2.8620	4.6019	68
2.8726	4.6564	69
2.8386	4.6286	70
2.8574	4.6259	71
2.8023	4.6359	72
2.7938	4.6031	73
2.7686	4.6159	74
2.7211	4.6128	75
2.6670	4.5913	76
2.6814	4.6226	77
2.6588	4.6188	78
2.6030	4.5964	79
2.6216	4.6019	80
2.5280	4.6018	81
2.5754	4.5851	82
2.5673	4.5901	83
2.5393	4.6256	84
2.4955	4.5802	85
2.4958	4.6054	86
2.5005	4.6039	87
2.4841	4.5920	88
2.4570	4.6012	89
2.4515	4.5890	90
2.4431	4.5838	91
2.3742	4.5787	92
2.3844	4.6137	93
2.3383	4.5567	94
2.3353	4.6001	95
2.3191	4.5930	96
2.3239	4.6078	97
2.2769	4.6426	98
2.3320	4.5895	99
2.2817	4.5816	100
2.2582	4.6319	101
2.1774	4.6308	102
2.2102	4.6072	103
2.1617	4.6217	104
2.1204	4.6111	105
2.1133	4.6397	106
2.1467	4.6421	107
2.1342	4.6318	108
2.1181	4.6555	109
2.0767	4.6562	110
2.0712	4.6533	111
2.0510	4.6722	112
2.0286	4.6437	113
2.0246	4.6431	114
2.0103	4.6450	115
2.0312	4.7080	116
2.0114	4.6146	117
1.9577	4.7103	118
1.9565	4.6865	119
1.9472	4.6602	120
1.9208	4.7423	121
1.8886	4.6638	122
1.9209	4.7388	123
1.8418	4.6900	124
1.8558	4.7059	125
1.8710	4.7353	126
1.8964	4.6955	127
1.8434	4.7402	128
1.8208	4.7557	129
1.8239	4.7254	130
1.8503	4.7575	131
1.7790	4.7725	132
1.7704	4.7971	133
1.7516	4.7445	134
1.7630	4.8046	135
1.7549	4.8150	136
1.7104	4.7884	137
1.6935	4.8472	138
1.6870	4.8170	139
1.6855	4.7915	140
1.6557	4.8719	141
1.6574	4.8336	142
1.5848	4.8889	143
1.6420	4.8585	144
1.6126	4.8700	145
1.5733	4.8807	146
1.5987	4.9093	147
1.5042	4.8983	148
1.5607	4.9012	149
1.5851	4.9208	150
1.5446	4.9047	151
1.5388	4.9215	152
1.5056	4.9796	153
1.5179	4.9090	154
1.4876	4.9935	155
1.4975	4.9810	156
1.4607	5.0071	157
1.5030	4.9251	158
1.4315	5.0219	159
1.4314	4.9997	160
1.4178	4.9675	161
1.4635	5.0669	162
1.4097	5.0152	163
1.4132	5.0367	164
1.3775	5.0395	165
1.4041	5.0492	166
1.3943	5.0470	167
1.3495	5.1050	168
1.3552	5.1041	169
1.3615	5.0648	170
1.3254	5.1234	171
1.3445	5.0723	172
1.3316	5.1059	173
1.3324	5.1294	174
1.2835	5.1263	175
1.2682	5.1415	176
1.2784	5.0970	177
1.2765	5.1549	178
1.2319	5.1690	179
1.2499	5.1262	180
1.1930	5.2097	181
1.1929	5.1751	182
1.2155	5.1879	183
1.1793	5.2163	184
1.2233	5.2055	185
1.1913	5.2115	186
1.1525	5.2521	187
1.1655	5.2302	188
1.1481	5.2551	189
1.1580	5.2635	190
1.1389	5.2528	191
1.1284	5.2694	192
1.1326	5.2906	193
1.1092	5.2957	194
1.0763	5.3227	195
1.0933	5.3446	196
1.0921	5.3191	197
1.0771	5.3399	198
1.0682	5.3906	199
1.0679	5.3286	200
1.0526	5.3256	201
1.0728	5.3739	202
1.0440	5.3422	203
1.0250	5.3946	204
1.0444	5.3930	205
1.0013	5.4044	206
0.9885	5.4122	207
1.0002	5.4359	208
0.9855	5.4380	209
0.9918	5.4045	210
0.9711	5.4300	211
0.9513	5.4863	212
0.9615	5.4596	213
0.9264	5.4859	214
0.9255	5.4913	215
0.9387	5.4630	216
0.9216	5.4758	217
0.9157	5.4729	218
0.8907	5.5127	219
0.9049	5.5270	220
0.8869	5.5087	221
0.8787	5.5236	222
0.8839	5.5024	223
0.8711	5.5289	224
0.8663	5.5205	225
0.8504	5.5972	226
0.8532	5.5749	227
0.8397	5.5873	228
0.8358	5.5819	229
0.8230	5.5610	230
0.8384	5.5884	231
0.8251	5.5783	232
0.8137	5.5916	233
0.8078	5.6334	234
0.8078	5.6164	235

Framework versions

• Transformers 4.41.2
• TensorFlow 2.15.0
• Datasets 2.20.0
• Tokenizers 0.19.1