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responsibility-framing/predict-perception-bert-cause-human

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predict-perception-bert-cause-human

This model is a fine-tuned version of dbmdz/bert-base-italian-xxl-cased on an unknown dataset. It achieves the following results on the evaluation set:

—Loss: 0.7139
—Rmse: 1.2259
—Rmse Cause::a Causata da un essere umano: 1.2259
—Mae: 1.0480
—Mae Cause::a Causata da un essere umano: 1.0480
—R2: 0.4563
—R2 Cause::a Causata da un essere umano: 0.4563
—Cos: 0.4783
—Pair: 0.0
—Rank: 0.5
—Neighbors: 0.3953
—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
—trainbatchsize: 20
—evalbatchsize: 8
—seed: 1996
—optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
—lrschedulertype: 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	Rank	Neighbors	Rsa
1.0874	1.0	15	1.2615	1.6296	1.6296	1.3836	1.3836	0.0393	0.0393	0.0435	0.5	0.2935	nan
0.9577	2.0	30	1.1988	1.5886	1.5886	1.3017	1.3017	0.0870	0.0870	0.4783	0.5	0.3944	nan
0.8414	3.0	45	0.9870	1.4414	1.4414	1.1963	1.1963	0.2483	0.2483	0.3913	0.5	0.3048	nan
0.7291	4.0	60	0.9098	1.3839	1.3839	1.1297	1.1297	0.3071	0.3071	0.4783	0.5	0.3084	nan
0.5949	5.0	75	0.9207	1.3921	1.3921	1.2079	1.2079	0.2988	0.2988	0.4783	0.5	0.3084	nan
0.4938	6.0	90	0.8591	1.3448	1.3448	1.1842	1.1842	0.3458	0.3458	0.4783	0.5	0.3084	nan
0.3611	7.0	105	0.8176	1.3119	1.3119	1.1454	1.1454	0.3774	0.3774	0.5652	0.5	0.4091	nan
0.2663	8.0	120	0.6879	1.2034	1.2034	1.0300	1.0300	0.4761	0.4761	0.5652	0.5	0.4091	nan
0.1833	9.0	135	0.7704	1.2735	1.2735	1.1031	1.1031	0.4133	0.4133	0.5652	0.5	0.3152	nan
0.1704	10.0	150	0.7097	1.2222	1.2222	1.0382	1.0382	0.4596	0.4596	0.4783	0.5	0.3084	nan
0.1219	11.0	165	0.6872	1.2027	1.2027	1.0198	1.0198	0.4767	0.4767	0.4783	0.5	0.3084	nan
0.1011	12.0	180	0.7201	1.2312	1.2312	1.0466	1.0466	0.4516	0.4516	0.5652	0.5	0.3152	nan
0.0849	13.0	195	0.7267	1.2368	1.2368	1.0454	1.0454	0.4466	0.4466	0.4783	0.5	0.3953	nan
0.0818	14.0	210	0.7361	1.2448	1.2448	1.0565	1.0565	0.4394	0.4394	0.4783	0.5	0.3953	nan
0.0634	15.0	225	0.7158	1.2275	1.2275	1.0384	1.0384	0.4549	0.4549	0.3913	0.5	0.3306	nan
0.065	16.0	240	0.7394	1.2475	1.2475	1.0659	1.0659	0.4369	0.4369	0.3913	0.5	0.3306	nan
0.0541	17.0	255	0.7642	1.2683	1.2683	1.0496	1.0496	0.4181	0.4181	0.4783	0.5	0.3953	nan
0.0577	18.0	270	0.7137	1.2257	1.2257	1.0303	1.0303	0.4565	0.4565	0.4783	0.5	0.3953	nan
0.0474	19.0	285	0.7393	1.2475	1.2475	1.0447	1.0447	0.4370	0.4370	0.4783	0.5	0.3084	nan
0.0494	20.0	300	0.7157	1.2274	1.2274	1.0453	1.0453	0.4550	0.4550	0.4783	0.5	0.3084	nan
0.0434	21.0	315	0.7248	1.2352	1.2352	1.0462	1.0462	0.4480	0.4480	0.4783	0.5	0.3953	nan
0.049	22.0	330	0.7384	1.2467	1.2467	1.0613	1.0613	0.4377	0.4377	0.4783	0.5	0.3953	nan
0.0405	23.0	345	0.7420	1.2498	1.2498	1.0653	1.0653	0.4349	0.4349	0.3913	0.5	0.3306	nan
0.0398	24.0	360	0.7355	1.2442	1.2442	1.0620	1.0620	0.4399	0.4399	0.4783	0.5	0.3953	nan
0.0398	25.0	375	0.7570	1.2623	1.2623	1.0698	1.0698	0.4235	0.4235	0.3913	0.5	0.3306	nan
0.0345	26.0	390	0.7359	1.2446	1.2446	1.0610	1.0610	0.4396	0.4396	0.5652	0.5	0.3152	nan
0.0345	27.0	405	0.7417	1.2495	1.2495	1.0660	1.0660	0.4352	0.4352	0.4783	0.5	0.3953	nan
0.0386	28.0	420	0.7215	1.2323	1.2323	1.0514	1.0514	0.4506	0.4506	0.4783	0.5	0.3084	nan
0.0372	29.0	435	0.7140	1.2260	1.2260	1.0477	1.0477	0.4562	0.4562	0.5652	0.5	0.4091	nan
0.0407	30.0	450	0.7139	1.2259	1.2259	1.0480	1.0480	0.4563	0.4563	0.4783	0.5	0.3953	nan

Framework versions

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