predict-perception-xlmr-focus-object

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

predict-perception-xlmr-focus-object 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. --

Downloads · 30 days

0% of all-time downloads

All-time downloads

3.8K

Public

Repo size

2.2 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin1.1 GB · 98%

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: b72d193b

Try a prompt

Model card

From the Hugging Face model README

predict-perception-xlmr-focus-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: 0.1927
Rmse: 0.5495
Rmse Focus::a Su un oggetto: 0.5495
Mae: 0.4174
Mae Focus::a Su un oggetto: 0.4174
R2: 0.5721
R2 Focus::a Su un oggetto: 0.5721
Cos: 0.5652
Pair: 0.0
Rank: 0.5
Neighbors: 0.5518
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 Su un oggetto	Mae	Mae Focus::a Su un oggetto	R2	R2 Focus::a Su un oggetto	Cos	Rank	Neighbors	Rsa
1.0316	1.0	15	0.6428	1.0035	1.0035	0.8806	0.8806	-0.4272	-0.4272	-0.4783	0.5	0.5302	nan
1.0005	2.0	30	0.4564	0.8456	0.8456	0.7078	0.7078	-0.0134	-0.0134	0.4783	0.5	0.4440	nan
0.9519	3.0	45	0.4151	0.8063	0.8063	0.6797	0.6797	0.0784	0.0784	0.1304	0.5	0.4888	nan
0.92	4.0	60	0.3982	0.7898	0.7898	0.6516	0.6516	0.1159	0.1159	0.2174	0.5	0.5036	nan
0.8454	5.0	75	0.2739	0.6550	0.6550	0.5292	0.5292	0.3919	0.3919	0.6522	0.5	0.4160	nan
0.7247	6.0	90	0.2413	0.6148	0.6148	0.5347	0.5347	0.4642	0.4642	0.4783	0.5	0.3453	nan
0.6055	7.0	105	0.3109	0.6978	0.6978	0.6115	0.6115	0.3098	0.3098	0.4783	0.5	0.4154	nan
0.5411	8.0	120	0.3932	0.7848	0.7848	0.6712	0.6712	0.1271	0.1271	0.4783	0.5	0.4154	nan
0.4784	9.0	135	0.1316	0.4540	0.4540	0.3750	0.3750	0.7079	0.7079	0.5652	0.5	0.6247	nan
0.4039	10.0	150	0.2219	0.5896	0.5896	0.4954	0.4954	0.5074	0.5074	0.5652	0.5	0.4838	nan
0.3415	11.0	165	0.1935	0.5505	0.5505	0.4443	0.4443	0.5704	0.5704	0.5652	0.5	0.6247	nan
0.3369	12.0	180	0.2118	0.5761	0.5761	0.4554	0.4554	0.5296	0.5296	0.5652	0.5	0.6247	nan
0.3083	13.0	195	0.1928	0.5496	0.5496	0.4368	0.4368	0.5718	0.5718	0.5652	0.5	0.6247	nan
0.2678	14.0	210	0.2205	0.5877	0.5877	0.4472	0.4472	0.5105	0.5105	0.5652	0.5	0.6247	nan
0.2199	15.0	225	0.2118	0.5760	0.5760	0.4689	0.4689	0.5297	0.5297	0.5652	0.5	0.6247	nan
0.2238	16.0	240	0.2461	0.6209	0.6209	0.5047	0.5047	0.4537	0.4537	0.5652	0.5	0.6247	nan
0.2233	17.0	255	0.2307	0.6011	0.6011	0.4618	0.4618	0.4879	0.4879	0.5652	0.5	0.6247	nan
0.1903	18.0	270	0.2207	0.5880	0.5880	0.4432	0.4432	0.5100	0.5100	0.6522	0.5	0.6622	nan
0.1714	19.0	285	0.2146	0.5798	0.5798	0.4368	0.4368	0.5236	0.5236	0.5652	0.5	0.6247	nan
0.1759	20.0	300	0.1745	0.5228	0.5228	0.4152	0.4152	0.6126	0.6126	0.5652	0.5	0.6247	nan
0.1505	21.0	315	0.1944	0.5519	0.5519	0.4170	0.4170	0.5684	0.5684	0.5652	0.5	0.6247	nan
0.1467	22.0	330	0.1802	0.5313	0.5313	0.3910	0.3910	0.5999	0.5999	0.6522	0.5	0.6622	nan
0.1441	23.0	345	0.2360	0.6081	0.6081	0.4755	0.4755	0.4760	0.4760	0.4783	0.5	0.4938	nan
0.1553	24.0	360	0.2129	0.5774	0.5774	0.4539	0.4539	0.5274	0.5274	0.5652	0.5	0.5518	nan
0.1163	25.0	375	0.1780	0.5281	0.5281	0.3952	0.3952	0.6048	0.6048	0.6522	0.5	0.6622	nan
0.1266	26.0	390	0.2163	0.5821	0.5821	0.4569	0.4569	0.5198	0.5198	0.5652	0.5	0.5518	nan
0.1416	27.0	405	0.1829	0.5352	0.5352	0.4082	0.4082	0.5939	0.5939	0.5652	0.5	0.5518	nan
0.1576	28.0	420	0.1930	0.5498	0.5498	0.4126	0.4126	0.5716	0.5716	0.6522	0.5	0.6622	nan
0.118	29.0	435	0.2070	0.5694	0.5694	0.4378	0.4378	0.5405	0.5405	0.5652	0.5	0.5518	nan
0.1179	30.0	450	0.1927	0.5495	0.5495	0.4174	0.4174	0.5721	0.5721	0.5652	0.5	0.5518	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