predict-perception-xlmr-focus-assassin

responsibility-framing/predict-perception-xlmr-focus-assassin

predict-perception-xlmr-focus-assassin is a text classification model from responsibility-framing. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Text Classification
Library: transformers
License: mit
Model type: xlm-roberta
Access: Public
Created: Mar 15, 2022
Updated: Mar 15, 2022
SHA: 076712f7

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Model card

From the Hugging Face model README

predict-perception-xlmr-focus-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: 0.3264
Rmse: 0.9437
Rmse Focus::a Sull'assassino: 0.9437
Mae: 0.7093
Mae Focus::a Sull'assassino: 0.7093
R2: 0.6145
R2 Focus::a Sull'assassino: 0.6145
Cos: 0.7391
Pair: 0.0
Rank: 0.5
Neighbors: 0.6131
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 Focus::a Sull'assassino	Mae	Mae Focus::a Sull'assassino	R2	R2 Focus::a Sull'assassino	Cos	Rank	Neighbors	Rsa
1.0403	1.0	15	1.1576	1.7771	1.7771	1.6028	1.6028	-0.3670	-0.3670	-0.2174	0.5	0.2379	nan
0.9818	2.0	30	0.8916	1.5596	1.5596	1.4136	1.4136	-0.0529	-0.0529	0.3913	0.5	0.3793	nan
0.9276	3.0	45	0.9277	1.5909	1.5909	1.4560	1.4560	-0.0955	-0.0955	0.3913	0.5	0.3742	nan
0.8395	4.0	60	0.7958	1.4734	1.4734	1.3032	1.3032	0.0603	0.0603	0.5652	0.5	0.4598	nan
0.7587	5.0	75	0.4647	1.1259	1.1259	0.9316	0.9316	0.4513	0.4513	0.6522	0.5	0.5087	nan
0.696	6.0	90	0.5368	1.2101	1.2101	1.0847	1.0847	0.3661	0.3661	0.7391	0.5	0.5302	nan
0.548	7.0	105	0.3110	0.9211	0.9211	0.7896	0.7896	0.6328	0.6328	0.6522	0.5	0.5261	nan
0.4371	8.0	120	0.3392	0.9619	0.9619	0.8132	0.8132	0.5995	0.5995	0.6522	0.5	0.5261	nan
0.355	9.0	135	0.3938	1.0366	1.0366	0.8153	0.8153	0.5349	0.5349	0.7391	0.5	0.6131	nan
0.2919	10.0	150	0.3484	0.9749	0.9749	0.7487	0.7487	0.5886	0.5886	0.7391	0.5	0.6131	nan
0.2595	11.0	165	0.2812	0.8759	0.8759	0.6265	0.6265	0.6679	0.6679	0.7391	0.5	0.6131	nan
0.2368	12.0	180	0.2534	0.8314	0.8314	0.6402	0.6402	0.7008	0.7008	0.7391	0.5	0.6131	nan
0.227	13.0	195	0.2878	0.8861	0.8861	0.6769	0.6769	0.6601	0.6601	0.7391	0.5	0.6131	nan
0.1979	14.0	210	0.2405	0.8100	0.8100	0.6113	0.6113	0.7160	0.7160	0.7391	0.5	0.6131	nan
0.1622	15.0	225	0.2575	0.8382	0.8382	0.6017	0.6017	0.6959	0.6959	0.8261	0.5	0.6622	nan
0.1575	16.0	240	0.2945	0.8963	0.8963	0.6741	0.6741	0.6523	0.6523	0.8261	0.5	0.6622	nan
0.1479	17.0	255	0.3563	0.9859	0.9859	0.7367	0.7367	0.5792	0.5792	0.8261	0.5	0.6622	nan
0.1269	18.0	270	0.2806	0.8750	0.8750	0.6665	0.6665	0.6686	0.6686	0.8261	0.5	0.6622	nan
0.1257	19.0	285	0.3267	0.9441	0.9441	0.6739	0.6739	0.6142	0.6142	0.8261	0.5	0.6622	nan
0.134	20.0	300	0.3780	1.0155	1.0155	0.7331	0.7331	0.5536	0.5536	0.7391	0.5	0.5302	nan
0.1171	21.0	315	0.3890	1.0301	1.0301	0.7444	0.7444	0.5406	0.5406	0.8261	0.5	0.6622	nan
0.0934	22.0	330	0.3131	0.9242	0.9242	0.6923	0.6923	0.6303	0.6303	0.8261	0.5	0.6622	nan
0.1112	23.0	345	0.2912	0.8913	0.8913	0.6610	0.6610	0.6561	0.6561	0.8261	0.5	0.6622	nan
0.1038	24.0	360	0.3109	0.9209	0.9209	0.7019	0.7019	0.6329	0.6329	0.8261	0.5	0.6622	nan
0.085	25.0	375	0.3469	0.9728	0.9728	0.7383	0.7383	0.5904	0.5904	0.8261	0.5	0.6622	nan
0.0843	26.0	390	0.3017	0.9073	0.9073	0.6848	0.6848	0.6437	0.6437	0.7391	0.5	0.6131	nan
0.093	27.0	405	0.3269	0.9443	0.9443	0.7042	0.7042	0.6140	0.6140	0.7391	0.5	0.6131	nan
0.0846	28.0	420	0.3161	0.9286	0.9286	0.6937	0.6937	0.6267	0.6267	0.7391	0.5	0.6131	nan
0.0764	29.0	435	0.3244	0.9408	0.9408	0.7079	0.7079	0.6169	0.6169	0.7391	0.5	0.6131	nan
0.0697	30.0	450	0.3264	0.9437	0.9437	0.7093	0.7093	0.6145	0.6145	0.7391	0.5	0.6131	nan

Framework versions

Transformers 4.16.2
Pytorch 1.10.2+cu113
Datasets 1.18.3
Tokenizers 0.11.0

Task: Text Classification
Library: transformers
Type: xlm-roberta
License: mit
Created: Mar 15, 2022
Updated: Mar 15, 2022