predict-perception-xlmr-cause-human

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: 0.7632
• Rmse: 1.2675
• Rmse Cause::a Causata da un essere umano: 1.2675
• Mae: 0.9299
• Mae Cause::a Causata da un essere umano: 0.9299
• R2: 0.4188
• R2 Cause::a Causata da un essere umano: 0.4188
• Cos: 0.3913
• Pair: 0.0
• Rank: 0.5
• Neighbors: 0.4082
• 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 Causata da un essere umano	Mae	Mae Cause::a Causata da un essere umano	R2	R2 Cause::a Causata da un essere umano	Cos	Pair	Rank	Neighbors	Rsa
1.0174	1.0	15	1.3796	1.7041	1.7041	1.3614	1.3614	-0.0506	-0.0506	-0.1304	0.0	0.5	0.2971	nan
0.9534	2.0	30	1.1173	1.5336	1.5336	1.2624	1.2624	0.1491	0.1491	0.4783	0.0	0.5	0.4446	nan
0.8883	3.0	45	1.0580	1.4923	1.4923	1.2451	1.2451	0.1943	0.1943	0.5652	0.0	0.5	0.4957	nan
0.8215	4.0	60	1.0200	1.4653	1.4653	1.2087	1.2087	0.2232	0.2232	0.6522	0.0	0.5	0.5123	nan
0.744	5.0	75	1.1496	1.5556	1.5556	1.2573	1.2573	0.1245	0.1245	0.2174	0.0	0.5	0.3007	nan
0.7056	6.0	90	0.9641	1.4246	1.4246	1.1763	1.1763	0.2658	0.2658	0.4783	0.0	0.5	0.3619	nan
0.6136	7.0	105	0.8328	1.3240	1.3240	1.0948	1.0948	0.3658	0.3658	0.4783	0.0	0.5	0.3628	nan
0.5185	8.0	120	0.6890	1.2043	1.2043	1.0112	1.0112	0.4753	0.4753	0.3913	0.0	0.5	0.4082	nan
0.5029	9.0	135	1.0380	1.4782	1.4782	1.1215	1.1215	0.2095	0.2095	0.3913	0.0	0.5	0.3781	nan
0.4624	10.0	150	1.1780	1.5747	1.5747	1.2852	1.2852	0.1029	0.1029	0.3913	0.0	0.5	0.4082	nan
0.4098	11.0	165	0.8714	1.3544	1.3544	1.1388	1.1388	0.3364	0.3364	0.3913	0.0	0.5	0.4082	nan
0.348	12.0	180	0.7260	1.2362	1.2362	0.9563	0.9563	0.4471	0.4471	0.5652	0.0	0.5	0.4957	nan
0.3437	13.0	195	0.7241	1.2346	1.2346	0.8998	0.8998	0.4485	0.4485	0.6522	0.0	0.5	0.4727	nan
0.2727	14.0	210	0.9070	1.3818	1.3818	1.1145	1.1145	0.3093	0.3093	0.3913	0.0	0.5	0.4082	nan
0.2762	15.0	225	0.7280	1.2380	1.2380	0.9210	0.9210	0.4456	0.4456	0.4783	0.0	0.5	0.4446	nan
0.2396	16.0	240	0.7921	1.2912	1.2912	0.9738	0.9738	0.3968	0.3968	0.3913	0.0	0.5	0.4082	nan
0.1955	17.0	255	0.8368	1.3272	1.3272	0.9717	0.9717	0.3627	0.3627	0.3913	0.0	0.5	0.4082	nan
0.1928	18.0	270	0.7782	1.2799	1.2799	0.9615	0.9615	0.4073	0.4073	0.3043	0.0	0.5	0.3768	nan
0.1893	19.0	285	0.7594	1.2644	1.2644	0.9441	0.9441	0.4216	0.4216	0.4783	0.0	0.5	0.4446	nan
0.2111	20.0	300	0.7230	1.2336	1.2336	0.8953	0.8953	0.4494	0.4494	0.3913	0.0	0.5	0.3787	nan
0.193	21.0	315	0.7836	1.2843	1.2843	0.9577	0.9577	0.4033	0.4033	0.3043	0.0	0.5	0.3768	nan
0.1649	22.0	330	0.7248	1.2352	1.2352	0.9133	0.9133	0.4480	0.4480	0.4783	0.0	0.5	0.4446	nan
0.2182	23.0	345	0.7608	1.2655	1.2655	0.9435	0.9435	0.4206	0.4206	0.4783	0.0	0.5	0.4446	nan
0.1534	24.0	360	0.7447	1.2520	1.2520	0.9277	0.9277	0.4329	0.4329	0.4783	0.0	0.5	0.4446	nan
0.1362	25.0	375	0.7437	1.2512	1.2512	0.9236	0.9236	0.4336	0.4336	0.3913	0.0	0.5	0.4082	nan
0.1391	26.0	390	0.7301	1.2397	1.2397	0.9182	0.9182	0.4440	0.4440	0.4783	0.0	0.5	0.4446	nan
0.1679	27.0	405	0.7748	1.2770	1.2770	0.9619	0.9619	0.4100	0.4100	0.3913	0.0	0.5	0.4082	nan
0.1491	28.0	420	0.7415	1.2493	1.2493	0.9097	0.9097	0.4353	0.4353	0.3913	0.0	0.5	0.4082	nan
0.1559	29.0	435	0.7525	1.2586	1.2586	0.9189	0.9189	0.4269	0.4269	0.3913	0.0	0.5	0.4082	nan
0.1784	30.0	450	0.7632	1.2675	1.2675	0.9299	0.9299	0.4188	0.4188	0.3913	0.0	0.5	0.4082	nan

Framework versions

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