metadata

license: mit
base_model: roberta-large
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - recall
  - f1
model-index:
  - name: lora-roberta-large-no-roller
    results: []
library_name: peft

lora-roberta-large-no-roller

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

Loss: 0.6492
Accuracy: 0.7693
Prec: 0.6783
Recall: 0.6473
F1: 0.6599
B Acc: 0.6473
Micro F1: 0.7693
Prec Joy: 0.7606
Recall Joy: 0.7609
F1 Joy: 0.7607
Prec Anger: 0.6490
Recall Anger: 0.6564
F1 Anger: 0.6527
Prec Disgust: 0.4785
Recall Disgust: 0.5179
F1 Disgust: 0.4974
Prec Fear: 0.7204
Recall Fear: 0.6690
F1 Fear: 0.6938
Prec Neutral: 0.8316
Recall Neutral: 0.8715
F1 Neutral: 0.8511
Prec Sadness: 0.7426
Recall Sadness: 0.6653
F1 Sadness: 0.7018
Prec Surprise: 0.5654
Recall Surprise: 0.3903
F1 Surprise: 0.4618

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: 16
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: 15.0

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
0.7936	0.75	1159	0.7811	0.7151	0.6488	0.5609	0.5848	0.5609	0.7151	0.6199	0.8341	0.7112	0.5064	0.6239	0.5590	0.6071	0.2732	0.3768	0.8054	0.4889	0.6084	0.8561	0.7799	0.8162	0.6749	0.5951	0.6325	0.4717	0.3312	0.3891
0.7404	1.5	2318	0.7121	0.7385	0.6664	0.5694	0.6008	0.5694	0.7385	0.7692	0.7050	0.7357	0.5220	0.6354	0.5732	0.6652	0.2732	0.3873	0.7082	0.5789	0.6371	0.7929	0.8857	0.8367	0.7494	0.5369	0.6256	0.4576	0.3710	0.4097
0.713	2.25	3477	0.7058	0.7460	0.6427	0.6191	0.6269	0.6191	0.7460	0.7403	0.7506	0.7454	0.5511	0.6387	0.5917	0.4204	0.5	0.4568	0.7208	0.5766	0.6407	0.8344	0.8447	0.8395	0.7142	0.6476	0.6793	0.5178	0.3753	0.4352
0.7173	3.0	4636	0.6973	0.7482	0.6831	0.5963	0.6211	0.5963	0.7482	0.7181	0.7529	0.7351	0.7473	0.4054	0.5256	0.4491	0.575	0.5043	0.7169	0.6070	0.6574	0.7877	0.8961	0.8384	0.7280	0.6387	0.6804	0.6347	0.2989	0.4064
0.6988	3.75	5795	0.6958	0.7445	0.6635	0.6000	0.6212	0.6000	0.7445	0.6616	0.8326	0.7373	0.6363	0.5851	0.6096	0.6263	0.3321	0.4341	0.6422	0.6047	0.6229	0.8472	0.8195	0.8331	0.6973	0.6644	0.6805	0.5333	0.3613	0.4308
0.661	4.5	6954	0.6763	0.7543	0.6901	0.6081	0.6333	0.6081	0.7543	0.7145	0.7805	0.7461	0.6601	0.5769	0.6157	0.7074	0.2893	0.4106	0.7588	0.5813	0.6583	0.8369	0.8462	0.8415	0.6341	0.7364	0.6815	0.5188	0.4462	0.4798
0.6632	5.25	8113	0.6745	0.7543	0.6671	0.6177	0.6386	0.6177	0.7543	0.7283	0.7653	0.7463	0.6232	0.6109	0.6170	0.4665	0.4732	0.4699	0.7629	0.6058	0.6754	0.8178	0.8689	0.8426	0.7752	0.5902	0.6702	0.4961	0.4097	0.4488
0.6427	6.0	9272	0.6729	0.7514	0.6607	0.6306	0.6403	0.6306	0.7514	0.8023	0.6707	0.7306	0.6623	0.5827	0.6199	0.4785	0.4768	0.4776	0.7312	0.6269	0.6751	0.8013	0.8919	0.8441	0.7613	0.6124	0.6788	0.3879	0.5527	0.4559
0.6363	6.75	10431	0.6584	0.7579	0.6635	0.6367	0.6475	0.6367	0.7579	0.7349	0.7887	0.7608	0.5808	0.6818	0.6273	0.5335	0.4268	0.4742	0.7103	0.6222	0.6633	0.8489	0.8333	0.8410	0.7227	0.6764	0.6988	0.5135	0.4280	0.4669
0.6134	7.5	11590	0.6490	0.7634	0.6768	0.6351	0.6538	0.6351	0.7634	0.7710	0.7383	0.7543	0.6355	0.6258	0.6306	0.5637	0.4268	0.4858	0.7412	0.6433	0.6888	0.8244	0.8720	0.8476	0.7001	0.6889	0.6944	0.5018	0.4505	0.4748
0.6045	8.25	12749	0.6494	0.7612	0.6648	0.6525	0.6555	0.6525	0.7612	0.7566	0.7571	0.7569	0.6401	0.6085	0.6239	0.4371	0.5893	0.5019	0.7557	0.6222	0.6825	0.8329	0.8613	0.8469	0.7556	0.6498	0.6987	0.4760	0.4796	0.4778
0.6139	9.0	13908	0.6585	0.7561	0.6730	0.6388	0.6499	0.6388	0.7561	0.7070	0.8144	0.7570	0.5686	0.7264	0.6379	0.5846	0.4071	0.48	0.7307	0.6187	0.6700	0.8639	0.8125	0.8374	0.7312	0.6698	0.6991	0.5247	0.4226	0.4681
0.5942	9.75	15067	0.6422	0.7661	0.6838	0.6436	0.6605	0.6436	0.7661	0.7413	0.7812	0.7607	0.6120	0.6938	0.6503	0.5619	0.4375	0.4920	0.7531	0.6351	0.6891	0.8407	0.8505	0.8455	0.7505	0.6684	0.7071	0.5271	0.4387	0.4789
0.5798	10.5	16226	0.6553	0.7614	0.6828	0.6358	0.6495	0.6358	0.7614	0.7338	0.7856	0.7588	0.5979	0.6905	0.6409	0.6692	0.3179	0.4310	0.7168	0.6632	0.6889	0.8489	0.8389	0.8439	0.7273	0.672	0.6985	0.4854	0.4828	0.4841
0.5513	11.25	17385	0.6538	0.7612	0.6640	0.6499	0.6550	0.6499	0.7612	0.7121	0.8059	0.7561	0.6487	0.6574	0.6530	0.5348	0.4393	0.4824	0.6504	0.6877	0.6686	0.8594	0.8270	0.8429	0.7018	0.6924	0.6971	0.5410	0.4398	0.4852
0.5544	12.0	18544	0.6492	0.7693	0.6783	0.6473	0.6599	0.6473	0.7693	0.7606	0.7609	0.7607	0.6490	0.6564	0.6527	0.4785	0.5179	0.4974	0.7204	0.6690	0.6938	0.8316	0.8715	0.8511	0.7426	0.6653	0.7018	0.5654	0.3903	0.4618
0.5229	12.75	19703	0.6428	0.7701	0.6822	0.6449	0.6612	0.6449	0.7701	0.7439	0.7820	0.7625	0.6647	0.6584	0.6615	0.5302	0.4393	0.4805	0.7452	0.6327	0.6844	0.8446	0.8563	0.8504	0.6988	0.7147	0.7067	0.5478	0.4312	0.4826
0.5222	13.5	20862	0.6565	0.7656	0.6705	0.6486	0.6586	0.6486	0.7656	0.7366	0.7862	0.7606	0.6581	0.6531	0.6556	0.5108	0.4643	0.4864	0.7322	0.6269	0.6755	0.8479	0.8465	0.8472	0.7176	0.6933	0.7052	0.4899	0.4699	0.4797
0.4972	14.25	22021	0.6574	0.7672	0.6725	0.6502	0.6596	0.6502	0.7672	0.7537	0.7703	0.7619	0.6368	0.6737	0.6547	0.4859	0.4929	0.4894	0.7302	0.6363	0.68	0.8447	0.8525	0.8486	0.7057	0.7044	0.7051	0.5506	0.4215	0.4775

Framework versions

Transformers 4.32.0.dev0
Pytorch 2.0.1
Datasets 2.12.0
Tokenizers 0.11.0