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: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
3.1888	1.0	32	2.4935	0.2783	0.1689	0.1470	0.3131
2.25	2.0	64	1.6646	0.6169	0.5430	0.5416	0.5888
1.4955	3.0	96	1.2759	0.7516	0.6600	0.6688	0.6586
0.9512	4.0	128	1.0307	0.8052	0.7394	0.7513	0.7147
0.6053	5.0	160	0.9993	0.7975	0.7757	0.7724	0.7398
0.4064	6.0	192	0.9347	0.8335	0.7939	0.7988	0.7732
0.2802	7.0	224	0.9249	0.8285	0.7970	0.8013	0.7818
0.2062	8.0	256	0.9051	0.8395	0.8114	0.8189	0.7987
0.1372	9.0	288	0.9771	0.8447	0.7922	0.8079	0.7910
0.1	10.0	320	1.0232	0.8246	0.8086	0.8042	0.7974
0.0815	11.0	352	1.0103	0.8391	0.8173	0.8209	0.8024
0.0586	12.0	384	1.0424	0.8366	0.7980	0.8085	0.7932
0.0534	13.0	416	1.1246	0.8318	0.8070	0.8126	0.7969
0.0412	14.0	448	1.0816	0.8338	0.8186	0.8167	0.8028
0.0346	15.0	480	1.1178	0.8277	0.8222	0.8182	0.8037
0.0312	16.0	512	1.1570	0.8387	0.8237	0.8219	0.8037
0.0268	17.0	544	1.1548	0.8375	0.8279	0.8240	0.8028
0.0221	18.0	576	1.1514	0.8316	0.8149	0.8169	0.8005
0.0215	19.0	608	1.1698	0.8351	0.8221	0.8204	0.8037
0.0213	20.0	640	1.1691	0.8311	0.8196	0.8166	0.8015

Framework versions

Transformers 4.27.3
Pytorch 1.13.1+cu116
Datasets 2.10.1
Tokenizers 0.13.2

LICENSE

Copyright (c) 2014, Universidad Carlos III de Madrid. Todos los derechos reservados. Este software es propiedad de la Universidad Carlos III de Madrid, grupo de investigación Robots Sociales. La Universidad Carlos III de Madrid es titular en exclusiva de los derechos de propiedad intelectual de este software. Queda prohibido cualquier uso indebido o no autorizado, entre estos, a título enunciativo pero no limitativo, la reproducción, fijación, distribución, comunicación pública, ingeniería inversa y/o transformación sobre dicho software, ya sea total o parcialmente, siendo el responsable del uso indebido o no autorizado también responsable de las consecuencias legales que pudieran derivarse de sus actos.

Jsevisal/balanced-augmented-ft-roberta-gest-pred-seqeval-partialmatch

balanced-augmented-mlroberta-gest-pred-seqeval-partialmatch

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

LICENSE

Jsevisal/
balanced-augmented-ft-roberta-gest-pred-seqeval-partialmatch