predict-perception-xlmr-blame-object

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: 0.7219
• Rmse: 0.6215
• Rmse Blame::a Un oggetto: 0.6215
• Mae: 0.4130
• Mae Blame::a Un oggetto: 0.4130
• R2: 0.1200
• R2 Blame::a Un oggetto: 0.1200
• Cos: 0.3043
• Pair: 0.0
• Rank: 0.5
• Neighbors: 0.4335
• 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 oggetto	Mae	Mae Blame::a Un oggetto	R2	R2 Blame::a Un oggetto	Cos	Pair	Rank	Neighbors	Rsa
1.0279	1.0	15	0.8483	0.6737	0.6737	0.4761	0.4761	-0.0341	-0.0341	-0.3043	0.0	0.5	0.5507	nan
1.0676	2.0	30	0.7749	0.6439	0.6439	0.4291	0.4291	0.0554	0.0554	0.0435	0.0	0.5	0.2614	nan
0.9563	3.0	45	0.7765	0.6446	0.6446	0.4349	0.4349	0.0535	0.0535	-0.0435	0.0	0.5	0.4515	nan
0.9622	4.0	60	0.7443	0.6311	0.6311	0.4061	0.4061	0.0927	0.0927	0.1304	0.0	0.5	0.2933	nan
0.948	5.0	75	0.8071	0.6571	0.6571	0.3817	0.3817	0.0162	0.0162	0.3043	0.0	0.5	0.4207	nan
0.9532	6.0	90	0.8007	0.6546	0.6546	0.4585	0.4585	0.0239	0.0239	-0.0435	0.0	0.5	0.5507	nan
0.9101	7.0	105	0.7126	0.6175	0.6175	0.3649	0.3649	0.1313	0.1313	0.4783	0.0	0.5	0.6012	nan
0.8369	8.0	120	0.7194	0.6204	0.6204	0.3896	0.3896	0.1231	0.1231	0.3913	0.0	0.5	0.3494	nan
0.8062	9.0	135	0.7157	0.6188	0.6188	0.4192	0.4192	0.1275	0.1275	0.0435	0.0	0.5	0.3182	nan
0.7344	10.0	150	0.7161	0.6190	0.6190	0.3612	0.3612	0.1270	0.1270	0.3043	0.0	0.5	0.6035	nan
0.7439	11.0	165	0.5894	0.5616	0.5616	0.3723	0.3723	0.2816	0.2816	0.3043	0.0	0.5	0.3846	nan
0.6241	12.0	180	0.7087	0.6158	0.6158	0.3972	0.3972	0.1361	0.1361	0.3043	0.0	0.5	0.3846	nan
0.6123	13.0	195	0.6318	0.5814	0.5814	0.3673	0.3673	0.2298	0.2298	0.3913	0.0	0.5	0.4413	nan
0.5364	14.0	210	0.6504	0.5899	0.5899	0.3674	0.3674	0.2072	0.2072	0.3043	0.0	0.5	0.3846	nan
0.5586	15.0	225	0.7151	0.6186	0.6186	0.3850	0.3850	0.1283	0.1283	0.3043	0.0	0.5	0.4335	nan
0.5133	16.0	240	0.5572	0.5460	0.5460	0.3540	0.3540	0.3208	0.3208	0.4783	0.0	0.5	0.5314	nan
0.4193	17.0	255	0.6047	0.5688	0.5688	0.3710	0.3710	0.2629	0.2629	0.3913	0.0	0.5	0.4924	nan
0.3504	18.0	270	0.6103	0.5714	0.5714	0.3687	0.3687	0.2561	0.2561	0.3913	0.0	0.5	0.4924	nan
0.3328	19.0	285	0.6181	0.5751	0.5751	0.3915	0.3915	0.2466	0.2466	0.4783	0.0	0.5	0.5314	nan
0.3276	20.0	300	0.6334	0.5822	0.5822	0.3612	0.3612	0.2279	0.2279	0.3913	0.0	0.5	0.4924	nan
0.3271	21.0	315	0.6200	0.5760	0.5760	0.3827	0.3827	0.2442	0.2442	0.3043	0.0	0.5	0.4335	nan
0.3139	22.0	330	0.6332	0.5821	0.5821	0.3723	0.3723	0.2281	0.2281	0.3913	0.0	0.5	0.4924	nan
0.2872	23.0	345	0.6694	0.5985	0.5985	0.3966	0.3966	0.1840	0.1840	0.3913	0.0	0.5	0.4924	nan
0.3617	24.0	360	0.7022	0.6130	0.6130	0.4061	0.4061	0.1440	0.1440	0.3913	0.0	0.5	0.4924	nan
0.3227	25.0	375	0.7364	0.6277	0.6277	0.4205	0.4205	0.1024	0.1024	0.3043	0.0	0.5	0.4335	nan
0.256	26.0	390	0.6938	0.6093	0.6093	0.3833	0.3833	0.1543	0.1543	0.3913	0.0	0.5	0.4924	nan
0.2605	27.0	405	0.7221	0.6216	0.6216	0.4036	0.4036	0.1198	0.1198	0.3043	0.0	0.5	0.4335	nan
0.2558	28.0	420	0.6959	0.6102	0.6102	0.3859	0.3859	0.1518	0.1518	0.3913	0.0	0.5	0.4924	nan
0.2403	29.0	435	0.7152	0.6186	0.6186	0.4088	0.4088	0.1281	0.1281	0.3913	0.0	0.5	0.4924	nan
0.3263	30.0	450	0.7219	0.6215	0.6215	0.4130	0.4130	0.1200	0.1200	0.3043	0.0	0.5	0.4335	nan

Framework versions

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