metadata

license: mit
tags:
  - generated_from_trainer
model-index:
  - name: predict-perception-xlmr-cause-none
    results: []

predict-perception-xlmr-cause-none

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

Loss: 1.8639
Rmse: 1.3661
Rmse Cause::a Spontanea, priva di un agente scatenante: 1.3661
Mae: 1.0795
Mae Cause::a Spontanea, priva di un agente scatenante: 1.0795
R2: -1.7872
R2 Cause::a Spontanea, priva di un agente scatenante: -1.7872
Cos: -0.3043
Pair: 0.0
Rank: 0.5
Neighbors: 0.3501
Rsa: nan

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: 1e-05
train_batch_size: 20
eval_batch_size: 8
seed: 1996
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Rmse	Rmse Cause::a Spontanea, priva di un agente scatenante	Mae	Mae Cause::a Spontanea, priva di un agente scatenante	R2	R2 Cause::a Spontanea, priva di un agente scatenante	Cos	Rank	Neighbors	Rsa
1.0626	1.0	15	0.6787	0.8244	0.8244	0.7453	0.7453	-0.0149	-0.0149	0.0435	0.5	0.2515	nan
1.0186	2.0	30	0.6769	0.8233	0.8233	0.7457	0.7457	-0.0122	-0.0122	0.0435	0.5	0.2515	nan
1.0346	3.0	45	0.6812	0.8259	0.8259	0.7489	0.7489	-0.0187	-0.0187	0.0435	0.5	0.2515	nan
0.9481	4.0	60	1.0027	1.0020	1.0020	0.8546	0.8546	-0.4994	-0.4994	-0.3043	0.5	0.2579	nan
0.8838	5.0	75	0.9352	0.9677	0.9677	0.8463	0.8463	-0.3985	-0.3985	-0.2174	0.5	0.2966	nan
0.7971	6.0	90	0.9396	0.9700	0.9700	0.8608	0.8608	-0.4050	-0.4050	-0.2174	0.5	0.3156	nan
0.8182	7.0	105	0.9485	0.9746	0.9746	0.8509	0.8509	-0.4184	-0.4184	-0.1304	0.5	0.2788	nan
0.696	8.0	120	1.1396	1.0682	1.0682	0.9309	0.9309	-0.7041	-0.7041	-0.1304	0.5	0.2899	nan
0.6337	9.0	135	1.3064	1.1437	1.1437	0.9612	0.9612	-0.9536	-0.9536	-0.3913	0.5	0.4018	nan
0.5308	10.0	150	1.2403	1.1144	1.1144	0.9359	0.9359	-0.8547	-0.8547	-0.3913	0.5	0.4018	nan
0.5226	11.0	165	1.3433	1.1597	1.1597	0.9542	0.9542	-1.0087	-1.0087	-0.3913	0.5	0.4018	nan
0.474	12.0	180	1.5321	1.2386	1.2386	1.0340	1.0340	-1.2910	-1.2910	-0.3043	0.5	0.3205	nan
0.3899	13.0	195	1.6322	1.2784	1.2784	1.0083	1.0083	-1.4408	-1.4408	-0.3043	0.5	0.3590	nan
0.3937	14.0	210	1.7519	1.3244	1.3244	1.0540	1.0540	-1.6197	-1.6197	-0.3913	0.5	0.4018	nan
0.4128	15.0	225	1.8588	1.3643	1.3643	1.0765	1.0765	-1.7797	-1.7797	-0.3913	0.5	0.4018	nan
0.3424	16.0	240	1.7211	1.3128	1.3128	1.0217	1.0217	-1.5737	-1.5737	-0.3913	0.5	0.4018	nan
0.3307	17.0	255	1.7802	1.3351	1.3351	1.0790	1.0790	-1.6621	-1.6621	-0.3043	0.5	0.3205	nan
0.2972	18.0	270	1.5272	1.2366	1.2366	0.9945	0.9945	-1.2837	-1.2837	-0.3043	0.5	0.3501	nan
0.2862	19.0	285	1.7213	1.3128	1.3128	1.0574	1.0574	-1.5740	-1.5740	-0.3913	0.5	0.3815	nan
0.2844	20.0	300	1.8999	1.3793	1.3793	1.0930	1.0930	-1.8411	-1.8411	-0.3043	0.5	0.3501	nan
0.2404	21.0	315	1.9806	1.4082	1.4082	1.1221	1.1221	-1.9617	-1.9617	-0.3913	0.5	0.3815	nan
0.2349	22.0	330	1.8649	1.3665	1.3665	1.0953	1.0953	-1.7888	-1.7888	-0.3913	0.5	0.3815	nan
0.2323	23.0	345	1.8256	1.3520	1.3520	1.0694	1.0694	-1.7299	-1.7299	-0.3913	0.5	0.4018	nan
0.2217	24.0	360	1.9150	1.3847	1.3847	1.1017	1.1017	-1.8636	-1.8636	-0.3043	0.5	0.3501	nan
0.2262	25.0	375	1.8536	1.3624	1.3624	1.0667	1.0667	-1.7719	-1.7719	-0.3043	0.5	0.3501	nan
0.2052	26.0	390	1.7727	1.3323	1.3323	1.0475	1.0475	-1.6508	-1.6508	-0.3043	0.5	0.3501	nan
0.2121	27.0	405	1.8088	1.3458	1.3458	1.0588	1.0588	-1.7048	-1.7048	-0.3043	0.5	0.3501	nan
0.1723	28.0	420	1.8283	1.3530	1.3530	1.0628	1.0628	-1.7340	-1.7340	-0.3043	0.5	0.3501	nan
0.1932	29.0	435	1.8566	1.3635	1.3635	1.0763	1.0763	-1.7764	-1.7764	-0.3043	0.5	0.3501	nan
0.2157	30.0	450	1.8639	1.3661	1.3661	1.0795	1.0795	-1.7872	-1.7872	-0.3043	0.5	0.3501	nan

Framework versions

Transformers 4.16.2
Pytorch 1.10.2+cu113
Datasets 1.18.3
Tokenizers 0.11.0