lora-roberta-large-no-ed

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

Loss: 0.6852
Accuracy: 0.7581
Prec: 0.6440
Recall: 0.6039
F1: 0.6206
B Acc: 0.6039
Micro F1: 0.7581
Prec Joy: 0.7167
Recall Joy: 0.7642
F1 Joy: 0.7397
Prec Anger: 0.6203
Recall Anger: 0.6289
F1 Anger: 0.6246
Prec Disgust: 0.4767
Recall Disgust: 0.3849
F1 Disgust: 0.4259
Prec Fear: 0.6752
Recall Fear: 0.5813
F1 Fear: 0.6247
Prec Neutral: 0.8418
Recall Neutral: 0.8525
F1 Neutral: 0.8471
Prec Sadness: 0.6674
Recall Sadness: 0.6614
F1 Sadness: 0.6644
Prec Surprise: 0.5101
Recall Surprise: 0.3542
F1 Surprise: 0.4181

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: 0.001
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.05
num_epochs: 20.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Prec	Recall	F1	B Acc	Micro F1	Prec Joy	Recall Joy	F1 Joy	Prec Anger	Recall Anger	F1 Anger	Prec Disgust	Recall Disgust	F1 Disgust	Prec Fear	Recall Fear	F1 Fear	Prec Neutral	Recall Neutral	F1 Neutral	Prec Sadness	Recall Sadness	F1 Sadness	Prec Surprise	Recall Surprise	F1 Surprise
0.7938	1.0	1465	0.7589	0.7257	0.6233	0.4993	0.5433	0.4993	0.7257	0.7259	0.6828	0.7037	0.6223	0.4082	0.4930	0.5359	0.2657	0.3552	0.5925	0.5110	0.5487	0.7564	0.9097	0.8260	0.7150	0.4865	0.5790	0.4151	0.2315	0.2972
0.7546	2.0	2930	0.7482	0.7243	0.6272	0.5499	0.5735	0.5499	0.7243	0.6028	0.8315	0.6989	0.5325	0.5802	0.5553	0.5135	0.2782	0.3609	0.6619	0.5388	0.5940	0.8498	0.8045	0.8265	0.74	0.5294	0.6172	0.4902	0.2864	0.3616
0.7289	3.0	4395	0.7293	0.7321	0.6234	0.5839	0.5984	0.5839	0.7321	0.6491	0.7901	0.7127	0.6129	0.5271	0.5668	0.4413	0.4561	0.4486	0.6974	0.5198	0.5956	0.8364	0.8146	0.8254	0.6406	0.6423	0.6414	0.4862	0.3376	0.3985
0.7076	4.0	5860	0.6898	0.7466	0.6572	0.5649	0.5972	0.5649	0.7466	0.7573	0.6911	0.7227	0.5110	0.6565	0.5747	0.4868	0.3096	0.3785	0.8139	0.4802	0.6041	0.8125	0.8772	0.8436	0.6939	0.5946	0.6404	0.5253	0.3453	0.4167
0.6925	5.0	7325	0.7039	0.7403	0.6121	0.5916	0.5972	0.5916	0.7403	0.6933	0.7525	0.7217	0.5234	0.6372	0.5747	0.3630	0.4100	0.3851	0.6121	0.5798	0.5955	0.8446	0.8363	0.8404	0.7512	0.5713	0.6490	0.4973	0.3542	0.4137
0.6841	6.0	8790	0.6704	0.7516	0.6607	0.5820	0.6076	0.5820	0.7516	0.7158	0.7536	0.7342	0.6577	0.4856	0.5587	0.4195	0.5502	0.4760	0.8476	0.4641	0.5998	0.8120	0.8784	0.8439	0.6971	0.6184	0.6554	0.4756	0.3235	0.3851
0.6715	7.0	10255	0.6919	0.7412	0.6246	0.6180	0.6112	0.6180	0.7412	0.7020	0.7642	0.7318	0.5513	0.6034	0.5762	0.3682	0.5962	0.4553	0.8024	0.4817	0.6020	0.8602	0.8191	0.8391	0.6611	0.6481	0.6545	0.4267	0.4130	0.4198
0.6562	8.0	11720	0.7245	0.7325	0.5985	0.6129	0.6014	0.6129	0.7325	0.6499	0.8167	0.7238	0.5320	0.6211	0.5731	0.3779	0.4728	0.4201	0.5704	0.6047	0.5871	0.8771	0.7863	0.8292	0.7431	0.6010	0.6645	0.4391	0.3875	0.4117
0.6426	9.0	13185	0.6683	0.7510	0.6304	0.6109	0.6175	0.6109	0.7510	0.7216	0.7506	0.7358	0.5768	0.6001	0.5882	0.3908	0.4603	0.4227	0.7469	0.5359	0.6240	0.8458	0.8458	0.8458	0.6966	0.6412	0.6678	0.4347	0.4425	0.4385
0.6278	10.0	14650	0.6661	0.7545	0.6427	0.5968	0.6142	0.5968	0.7545	0.7531	0.712	0.7320	0.6346	0.5476	0.5879	0.4574	0.4268	0.4416	0.7220	0.5476	0.6228	0.8304	0.8692	0.8494	0.5931	0.7276	0.6535	0.5084	0.3465	0.4122
0.6218	11.0	16115	0.6714	0.7507	0.6478	0.5958	0.6143	0.5958	0.7507	0.6878	0.7864	0.7338	0.6796	0.4950	0.5728	0.4181	0.4916	0.4519	0.7635	0.4963	0.6016	0.8324	0.8512	0.8417	0.6816	0.6524	0.6667	0.4719	0.3977	0.4316
0.6077	12.0	17580	0.6649	0.7543	0.6216	0.6171	0.6187	0.6171	0.7543	0.7496	0.7249	0.7371	0.6055	0.6095	0.6075	0.4449	0.4142	0.4290	0.6194	0.6076	0.6135	0.8426	0.8568	0.8497	0.6894	0.6386	0.6630	0.4	0.4680	0.4313
0.5868	13.0	19045	0.6680	0.7584	0.6473	0.6026	0.6224	0.6026	0.7584	0.7192	0.7522	0.7354	0.6442	0.5658	0.6025	0.4398	0.4435	0.4417	0.7127	0.5666	0.6313	0.8293	0.8711	0.8497	0.7187	0.6174	0.6642	0.4673	0.4015	0.4319
0.5747	14.0	20510	0.6692	0.7551	0.6293	0.6049	0.6155	0.6049	0.7551	0.7114	0.7621	0.7359	0.5985	0.6167	0.6075	0.4461	0.3808	0.4108	0.6088	0.6061	0.6075	0.8444	0.8522	0.8483	0.7124	0.6222	0.6642	0.4835	0.3939	0.4341
0.5632	15.0	21975	0.6763	0.7551	0.6390	0.6104	0.6185	0.6104	0.7551	0.6978	0.7812	0.7371	0.6381	0.5774	0.6063	0.4179	0.5272	0.4662	0.6260	0.5710	0.5972	0.8432	0.8479	0.8455	0.6950	0.6460	0.6696	0.5551	0.3223	0.4078
0.546	16.0	23440	0.6880	0.7537	0.6365	0.6089	0.6205	0.6089	0.7537	0.6906	0.7878	0.7360	0.6121	0.625	0.6185	0.4564	0.3828	0.4164	0.6587	0.6076	0.6321	0.8493	0.8350	0.8421	0.6999	0.6428	0.6702	0.4885	0.3811	0.4282
0.5354	17.0	24905	0.6823	0.7545	0.6399	0.6097	0.6222	0.6097	0.7545	0.6972	0.7828	0.7375	0.6131	0.6355	0.6241	0.4916	0.3682	0.4211	0.6979	0.5783	0.6325	0.8525	0.8357	0.8440	0.6820	0.6455	0.6632	0.4447	0.4220	0.4331
0.5103	18.0	26370	0.6852	0.7581	0.6440	0.6039	0.6206	0.6039	0.7581	0.7167	0.7642	0.7397	0.6203	0.6289	0.6246	0.4767	0.3849	0.4259	0.6752	0.5813	0.6247	0.8418	0.8525	0.8471	0.6674	0.6614	0.6644	0.5101	0.3542	0.4181
0.4972	19.0	27835	0.6948	0.7535	0.6350	0.6039	0.6162	0.6039	0.7535	0.7038	0.7715	0.7361	0.5989	0.6515	0.6241	0.4658	0.3703	0.4126	0.6739	0.5871	0.6275	0.8495	0.8381	0.8438	0.6631	0.6571	0.6601	0.4902	0.3517	0.4095
0.4801	20.0	29300	0.6945	0.7549	0.6320	0.6106	0.6199	0.6106	0.7549	0.7138	0.7631	0.7376	0.6130	0.6316	0.6222	0.4396	0.4038	0.4209	0.6623	0.5886	0.6233	0.8481	0.8428	0.8455	0.6626	0.6619	0.6622	0.4846	0.3824	0.4274

Framework versions

Transformers 4.32.0.dev0
Pytorch 2.0.1
Datasets 2.12.0
Tokenizers 0.11.0

lora-roberta-large-no-ed

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

NSDT 3DConvert

UnrealSynth

DreamTexture.js