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responsibility-framing/predict-perception-xlmr-blame-victim

sourceHugging Facemitupdated 5y agoView on Hugging Face

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predict-perception-xlmr-blame-victim

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: 1.1098
—Rmse: 0.6801
—Rmse Blame::a La vittima: 0.6801
—Mae: 0.5617
—Mae Blame::a La vittima: 0.5617
—R2: -1.5910
—R2 Blame::a La vittima: -1.5910
—Cos: -0.1304
—Pair: 0.0
—Rank: 0.5
—Neighbors: 0.3333
—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 Blame::a La vittima	Mae	Mae Blame::a La vittima	R2	R2 Blame::a La vittima	Cos	Rank	Neighbors	Rsa
1.0422	1.0	15	0.4952	0.4542	0.4542	0.4095	0.4095	-0.1560	-0.1560	-0.1304	0.5	0.2971	nan
1.0434	2.0	30	0.4851	0.4496	0.4496	0.4054	0.4054	-0.1324	-0.1324	-0.1304	0.5	0.2971	nan
1.038	3.0	45	0.4513	0.4337	0.4337	0.3885	0.3885	-0.0536	-0.0536	-0.1304	0.5	0.2971	nan
1.0151	4.0	60	0.4395	0.4280	0.4280	0.3840	0.3840	-0.0262	-0.0262	-0.1304	0.5	0.2715	nan
0.9727	5.0	75	0.4490	0.4325	0.4325	0.3811	0.3811	-0.0482	-0.0482	0.2174	0.5	0.3338	nan
0.9733	6.0	90	0.4540	0.4349	0.4349	0.3860	0.3860	-0.0598	-0.0598	-0.2174	0.5	0.3248	nan
0.9396	7.0	105	0.4501	0.4331	0.4331	0.3849	0.3849	-0.0508	-0.0508	0.0435	0.5	0.2609	nan
0.8759	8.0	120	0.4597	0.4377	0.4377	0.3849	0.3849	-0.0731	-0.0731	0.3043	0.5	0.3898	nan
0.8768	9.0	135	0.4575	0.4366	0.4366	0.3784	0.3784	-0.0680	-0.0680	0.4783	0.5	0.4615	nan
0.8312	10.0	150	0.5363	0.4727	0.4727	0.4071	0.4071	-0.2520	-0.2520	-0.0435	0.5	0.2733	nan
0.7296	11.0	165	0.5291	0.4696	0.4696	0.4057	0.4057	-0.2353	-0.2353	0.3043	0.5	0.3898	nan
0.7941	12.0	180	0.5319	0.4708	0.4708	0.4047	0.4047	-0.2417	-0.2417	0.1304	0.5	0.3381	nan
0.6486	13.0	195	0.6787	0.5318	0.5318	0.4516	0.4516	-0.5846	-0.5846	0.1304	0.5	0.3381	nan
0.6241	14.0	210	1.0146	0.6502	0.6502	0.5580	0.5580	-1.3687	-1.3687	-0.1304	0.5	0.3509	nan
0.5868	15.0	225	0.7164	0.5464	0.5464	0.4682	0.4682	-0.6725	-0.6725	-0.0435	0.5	0.3333	nan
0.5305	16.0	240	0.9064	0.6146	0.6146	0.5173	0.5173	-1.1161	-1.1161	-0.0435	0.5	0.3333	nan
0.495	17.0	255	1.3860	0.7600	0.7600	0.6433	0.6433	-2.2358	-2.2358	-0.0435	0.5	0.2935	nan
0.566	18.0	270	0.7618	0.5634	0.5634	0.4730	0.4730	-0.7785	-0.7785	0.0435	0.5	0.3225	nan
0.4305	19.0	285	0.8849	0.6072	0.6072	0.5048	0.5048	-1.0659	-1.0659	-0.0435	0.5	0.3333	nan
0.5108	20.0	300	0.7376	0.5544	0.5544	0.4716	0.4716	-0.7220	-0.7220	0.0435	0.5	0.3225	nan
0.44	21.0	315	1.1611	0.6956	0.6956	0.5921	0.5921	-1.7108	-1.7108	-0.1304	0.5	0.3333	nan
0.395	22.0	330	1.3004	0.7361	0.7361	0.6078	0.6078	-2.0360	-2.0360	-0.2174	0.5	0.3587	nan
0.3945	23.0	345	0.9376	0.6251	0.6251	0.5272	0.5272	-1.1890	-1.1890	-0.2174	0.5	0.3188	nan
0.3093	24.0	360	1.3586	0.7524	0.7524	0.6219	0.6219	-2.1719	-2.1719	-0.2174	0.5	0.3587	nan
0.2676	25.0	375	1.2200	0.7130	0.7130	0.5994	0.5994	-1.8484	-1.8484	-0.2174	0.5	0.3587	nan
0.3257	26.0	390	1.2235	0.7140	0.7140	0.5900	0.5900	-1.8564	-1.8564	-0.2174	0.5	0.3587	nan
0.4004	27.0	405	1.0978	0.6763	0.6763	0.5624	0.5624	-1.5629	-1.5629	-0.2174	0.5	0.3587	nan
0.283	28.0	420	1.1454	0.6909	0.6909	0.5697	0.5697	-1.6742	-1.6742	-0.2174	0.5	0.3587	nan
0.3326	29.0	435	1.1214	0.6836	0.6836	0.5646	0.5646	-1.6181	-1.6181	-0.1304	0.5	0.3333	nan
0.2632	30.0	450	1.1098	0.6801	0.6801	0.5617	0.5617	-1.5910	-1.5910	-0.1304	0.5	0.3333	nan

Framework versions

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