predict-perception-xlmr-blame-concept

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: 0.9414
• Rmse: 0.7875
• Rmse Blame::a Un concetto astratto o un'emozione: 0.7875
• Mae: 0.6165
• Mae Blame::a Un concetto astratto o un'emozione: 0.6165
• R2: 0.2291
• R2 Blame::a Un concetto astratto o un'emozione: 0.2291
• Cos: 0.1304
• Pair: 0.0
• Rank: 0.5
• Neighbors: 0.3509
• 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 Blame::a Un concetto astratto o un'emozione	Mae	Mae Blame::a Un concetto astratto o un'emozione	R2	R2 Blame::a Un concetto astratto o un'emozione	Cos	Pair	Rank	Neighbors	Rsa
1.0549	1.0	15	1.2093	0.8925	0.8925	0.6659	0.6659	0.0097	0.0097	-0.3043	0.0	0.5	0.4013	nan
1.0085	2.0	30	1.2199	0.8964	0.8964	0.6494	0.6494	0.0010	0.0010	-0.1304	0.0	0.5	0.4515	nan
1.0131	3.0	45	1.1798	0.8815	0.8815	0.6412	0.6412	0.0339	0.0339	-0.2174	0.0	0.5	0.2402	nan
0.9931	4.0	60	1.1726	0.8788	0.8788	0.6370	0.6370	0.0397	0.0397	-0.1304	0.0	0.5	0.2911	nan
0.9668	5.0	75	1.1194	0.8587	0.8587	0.5925	0.5925	0.0833	0.0833	0.2174	0.0	0.5	0.3303	nan
0.8759	6.0	90	1.0776	0.8425	0.8425	0.6265	0.6265	0.1175	0.1175	0.3043	0.0	0.5	0.4190	nan
0.8787	7.0	105	1.0513	0.8321	0.8321	0.6087	0.6087	0.1391	0.1391	0.2174	0.0	0.5	0.3303	nan
0.7637	8.0	120	1.0537	0.8331	0.8331	0.6265	0.6265	0.1372	0.1372	0.2174	0.0	0.5	0.3303	nan
0.6568	9.0	135	0.9104	0.7744	0.7744	0.5887	0.5887	0.2544	0.2544	0.3043	0.0	0.5	0.3680	nan
0.6354	10.0	150	0.9055	0.7723	0.7723	0.6222	0.6222	0.2585	0.2585	0.1304	0.0	0.5	0.3987	nan
0.5107	11.0	165	1.0173	0.8186	0.8186	0.6168	0.6168	0.1669	0.1669	0.2174	0.0	0.5	0.3303	nan
0.4598	12.0	180	0.9155	0.7765	0.7765	0.6284	0.6284	0.2503	0.2503	0.1304	0.0	0.5	0.3987	nan
0.3815	13.0	195	0.9255	0.7808	0.7808	0.6140	0.6140	0.2421	0.2421	0.1304	0.0	0.5	0.3987	nan
0.3303	14.0	210	0.8506	0.7485	0.7485	0.6076	0.6076	0.3035	0.3035	0.0435	0.0	0.5	0.2862	nan
0.2799	15.0	225	1.0272	0.8226	0.8226	0.6699	0.6699	0.1588	0.1588	0.0435	0.0	0.5	0.2862	nan
0.2998	16.0	240	0.9969	0.8103	0.8103	0.6461	0.6461	0.1836	0.1836	0.0435	0.0	0.5	0.2862	nan
0.3131	17.0	255	0.9066	0.7727	0.7727	0.5849	0.5849	0.2576	0.2576	0.2174	0.0	0.5	0.3303	nan
0.2234	18.0	270	0.8741	0.7588	0.7588	0.5953	0.5953	0.2842	0.2842	0.2174	0.0	0.5	0.3303	nan
0.2481	19.0	285	1.0022	0.8125	0.8125	0.6549	0.6549	0.1793	0.1793	0.0435	0.0	0.5	0.2862	nan
0.2333	20.0	300	0.9238	0.7801	0.7801	0.6180	0.6180	0.2435	0.2435	0.0435	0.0	0.5	0.2862	nan
0.2407	21.0	315	0.9868	0.8062	0.8062	0.6457	0.6457	0.1919	0.1919	0.0435	0.0	0.5	0.2862	nan
0.2122	22.0	330	0.9514	0.7916	0.7916	0.6204	0.6204	0.2209	0.2209	0.0435	0.0	0.5	0.2862	nan
0.2162	23.0	345	0.9227	0.7796	0.7796	0.6053	0.6053	0.2444	0.2444	0.1304	0.0	0.5	0.3509	nan
0.1739	24.0	360	0.9147	0.7762	0.7762	0.5979	0.5979	0.2510	0.2510	0.1304	0.0	0.5	0.3509	nan
0.2084	25.0	375	0.9645	0.7970	0.7970	0.6296	0.6296	0.2102	0.2102	0.0435	0.0	0.5	0.2862	nan
0.1702	26.0	390	0.9587	0.7946	0.7946	0.6279	0.6279	0.2149	0.2149	0.0435	0.0	0.5	0.2862	nan
0.2146	27.0	405	0.9519	0.7918	0.7918	0.6273	0.6273	0.2205	0.2205	0.0435	0.0	0.5	0.2862	nan
0.1645	28.0	420	0.9398	0.7868	0.7868	0.6181	0.6181	0.2304	0.2304	0.0435	0.0	0.5	0.2862	nan
0.2052	29.0	435	0.9492	0.7907	0.7907	0.6228	0.6228	0.2227	0.2227	0.0435	0.0	0.5	0.2862	nan
0.147	30.0	450	0.9414	0.7875	0.7875	0.6165	0.6165	0.2291	0.2291	0.1304	0.0	0.5	0.3509	nan

Framework versions

• Transformers 4.16.2
• Pytorch 1.10.2+cu113
• Datasets 1.18.3
• Tokenizers 0.11.0