predict-perception-xlmr-blame-assassin

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: 0.4439
• Rmse: 0.9571
• Rmse Blame::a L'assassino: 0.9571
• Mae: 0.7260
• Mae Blame::a L'assassino: 0.7260
• R2: 0.6437
• R2 Blame::a L'assassino: 0.6437
• Cos: 0.7391
• Pair: 0.0
• Rank: 0.5
• Neighbors: 0.6287
• 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 L'assassino	Mae	Mae Blame::a L'assassino	R2	R2 Blame::a L'assassino	Cos	Pair	Rank	Neighbors	Rsa
1.0317	1.0	15	1.1311	1.5278	1.5278	1.3893	1.3893	0.0919	0.0919	0.5652	0.0	0.5	0.4512	nan
0.9475	2.0	30	1.0795	1.4926	1.4926	1.3387	1.3387	0.1334	0.1334	0.8261	0.0	0.5	0.6184	nan
0.9146	3.0	45	1.1092	1.5130	1.5130	1.4078	1.4078	0.1095	0.1095	0.4783	0.0	0.5	0.3116	nan
0.9539	4.0	60	1.1734	1.5561	1.5561	1.4238	1.4238	0.0580	0.0580	0.3913	0.0	0.5	0.3614	nan
0.8665	5.0	75	0.8910	1.3560	1.3560	1.2350	1.2350	0.2847	0.2847	0.5652	0.0	0.5	0.4136	nan
0.6564	6.0	90	0.8469	1.3220	1.3220	1.1570	1.1570	0.3201	0.3201	0.3913	0.0	0.5	0.3931	nan
0.5241	7.0	105	0.6429	1.1519	1.1519	0.9757	0.9757	0.4838	0.4838	0.5652	0.0	0.5	0.4222	nan
0.4589	8.0	120	0.5781	1.0923	1.0923	0.8714	0.8714	0.5359	0.5359	0.6522	0.0	0.5	0.4641	nan
0.4043	9.0	135	0.4525	0.9664	0.9664	0.8257	0.8257	0.6367	0.6367	0.5652	0.0	0.5	0.4263	nan
0.3498	10.0	150	0.4490	0.9627	0.9627	0.8272	0.8272	0.6395	0.6395	0.6522	0.0	0.5	0.5144	nan
0.3505	11.0	165	0.3721	0.8763	0.8763	0.7471	0.7471	0.7013	0.7013	0.7391	0.0	0.5	0.6287	nan
0.3426	12.0	180	0.4117	0.9218	0.9218	0.7477	0.7477	0.6695	0.6695	0.7391	0.0	0.5	0.6287	nan
0.3074	13.0	195	0.3761	0.8810	0.8810	0.7109	0.7109	0.6981	0.6981	0.7391	0.0	0.5	0.6287	nan
0.2261	14.0	210	0.3818	0.8877	0.8877	0.7042	0.7042	0.6935	0.6935	0.7391	0.0	0.5	0.6287	nan
0.2399	15.0	225	0.3893	0.8964	0.8964	0.7108	0.7108	0.6874	0.6874	0.7391	0.0	0.5	0.6287	nan
0.2014	16.0	240	0.4606	0.9750	0.9750	0.8046	0.8046	0.6302	0.6302	0.7391	0.0	0.5	0.6287	nan
0.1937	17.0	255	0.4549	0.9689	0.9689	0.7679	0.7679	0.6348	0.6348	0.7391	0.0	0.5	0.6287	nan
0.1831	18.0	270	0.4113	0.9213	0.9213	0.6746	0.6746	0.6698	0.6698	0.7391	0.0	0.5	0.6287	nan
0.1758	19.0	285	0.4154	0.9259	0.9259	0.7053	0.7053	0.6665	0.6665	0.7391	0.0	0.5	0.6287	nan
0.1577	20.0	300	0.3970	0.9051	0.9051	0.7163	0.7163	0.6813	0.6813	0.7391	0.0	0.5	0.6287	nan
0.1597	21.0	315	0.4199	0.9309	0.9309	0.7270	0.7270	0.6629	0.6629	0.7391	0.0	0.5	0.6287	nan
0.1145	22.0	330	0.4250	0.9365	0.9365	0.6971	0.6971	0.6588	0.6588	0.8261	0.0	0.5	0.6594	nan
0.1349	23.0	345	0.4168	0.9275	0.9275	0.7126	0.7126	0.6654	0.6654	0.7391	0.0	0.5	0.6287	nan
0.1481	24.0	360	0.4421	0.9552	0.9552	0.7441	0.7441	0.6451	0.6451	0.7391	0.0	0.5	0.6287	nan
0.1188	25.0	375	0.4356	0.9481	0.9481	0.7444	0.7444	0.6503	0.6503	0.7391	0.0	0.5	0.6287	nan
0.1119	26.0	390	0.4456	0.9590	0.9590	0.7139	0.7139	0.6422	0.6422	0.7391	0.0	0.5	0.6287	nan
0.1282	27.0	405	0.4456	0.9589	0.9589	0.7637	0.7637	0.6423	0.6423	0.7391	0.0	0.5	0.6287	nan
0.142	28.0	420	0.4501	0.9637	0.9637	0.7146	0.7146	0.6387	0.6387	0.8261	0.0	0.5	0.6594	nan
0.126	29.0	435	0.4442	0.9575	0.9575	0.7189	0.7189	0.6433	0.6433	0.7391	0.0	0.5	0.6287	nan
0.1308	30.0	450	0.4439	0.9571	0.9571	0.7260	0.7260	0.6437	0.6437	0.7391	0.0	0.5	0.6287	nan

Framework versions

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