NeRUBioS_RoBERTa_base_bne_Training_Development

This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.3499
Negref Precision: 0.5449
Negref Recall: 0.5380
Negref F1: 0.5414
Neg Precision: 0.9559
Neg Recall: 0.9694
Neg F1: 0.9626
Nsco Precision: 0.8730
Nsco Recall: 0.9062
Nsco F1: 0.8893
Unc Precision: 0.8315
Unc Recall: 0.8764
Unc F1: 0.8534
Usco Precision: 0.6608
Usco Recall: 0.7383
Usco F1: 0.6974
Precision: 0.8205
Recall: 0.8453
F1: 0.8327
Accuracy: 0.9526

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: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 12

Training results

Training Loss	Epoch	Step	Validation Loss	Negref Precision	Negref Recall	Negref F1	Neg Precision	Neg Recall	Neg F1	Nsco Precision	Nsco Recall	Nsco F1	Unc Precision	Unc Recall	Unc F1	Usco Precision	Usco Recall	Usco F1	Precision	Recall	F1	Accuracy
0.1898	1.0	1729	0.1783	0.4516	0.5316	0.4884	0.9351	0.9596	0.9472	0.8079	0.8539	0.8303	0.8193	0.7529	0.7847	0.5816	0.6406	0.6097	0.7596	0.8041	0.7813	0.9452
0.1163	2.0	3458	0.1724	0.4906	0.5527	0.5198	0.9274	0.9760	0.9511	0.8252	0.9026	0.8622	0.8263	0.8263	0.8263	0.5662	0.6680	0.6129	0.7721	0.8376	0.8036	0.9485
0.0621	3.0	5187	0.1946	0.5139	0.5063	0.5101	0.9524	0.9618	0.9571	0.8542	0.8836	0.8687	0.8071	0.8726	0.8386	0.6034	0.6836	0.6410	0.7999	0.8249	0.8122	0.9480
0.0378	4.0	6916	0.2279	0.4923	0.5401	0.5151	0.9450	0.9749	0.9597	0.8568	0.8884	0.8723	0.8259	0.8610	0.8431	0.6179	0.6758	0.6455	0.7940	0.8347	0.8138	0.9490
0.0192	5.0	8645	0.2495	0.5227	0.5338	0.5282	0.9541	0.9760	0.9649	0.8256	0.8884	0.8558	0.8071	0.8726	0.8386	0.6049	0.6758	0.6384	0.7929	0.8351	0.8135	0.9508
0.0134	6.0	10374	0.2764	0.5199	0.5232	0.5216	0.9568	0.9672	0.9620	0.8687	0.8955	0.8819	0.8277	0.8533	0.8403	0.6389	0.7188	0.6765	0.8114	0.8347	0.8229	0.9514
0.0068	7.0	12103	0.2876	0.4880	0.5169	0.5020	0.9470	0.9760	0.9613	0.8593	0.8919	0.8753	0.8494	0.8494	0.8494	0.6456	0.7188	0.6802	0.8010	0.8351	0.8177	0.9508
0.0059	8.0	13832	0.2886	0.4991	0.5591	0.5274	0.9488	0.9705	0.9595	0.8601	0.8907	0.8751	0.8231	0.8803	0.8507	0.6528	0.7344	0.6912	0.7986	0.8446	0.8209	0.9516
0.0029	9.0	15561	0.3290	0.5408	0.4895	0.5138	0.9529	0.9716	0.9622	0.8653	0.9002	0.8824	0.8218	0.8726	0.8464	0.6090	0.7422	0.6690	0.8125	0.8358	0.8240	0.9505
0.0009	10.0	17290	0.3582	0.5438	0.5105	0.5267	0.9519	0.9716	0.9616	0.8757	0.9038	0.8895	0.8218	0.8726	0.8464	0.6737	0.75	0.7098	0.8227	0.8413	0.8319	0.9506
0.0012	11.0	19019	0.3516	0.5139	0.5443	0.5287	0.9539	0.9705	0.9621	0.8834	0.9086	0.8958	0.8291	0.8803	0.8539	0.6761	0.75	0.7111	0.8157	0.8489	0.8320	0.9526
0.0005	12.0	20748	0.3499	0.5449	0.5380	0.5414	0.9559	0.9694	0.9626	0.8730	0.9062	0.8893	0.8315	0.8764	0.8534	0.6608	0.7383	0.6974	0.8205	0.8453	0.8327	0.9526

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

ajtamayoh/NeRUBioS_RoBERTa_base_bne_Training_Development

NeRUBioS_RoBERTa_base_bne_Training_Development

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

为此模型做种