NeRUBioS_RoBERTa_base_bne_Training_Testing

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: 0.3389
Negref Precision: 0.5225
Negref Recall: 0.5800
Negref F1: 0.5498
Neg Precision: 0.9521
Neg Recall: 0.9642
Neg F1: 0.9581
Nsco Precision: 0.8732
Nsco Recall: 0.9062
Nsco F1: 0.8894
Unc Precision: 0.8115
Unc Recall: 0.8718
Unc F1: 0.8405
Usco Precision: 0.6862
Usco Recall: 0.7532
Usco F1: 0.7181
Precision: 0.8150
Recall: 0.8557
F1: 0.8348
Accuracy: 0.9505

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
0.1896	1.0	1729	0.1858	0.4255	0.4655	0.4446	0.9389	0.9501	0.9445	0.8327	0.8775	0.8545	0.7571	0.8231	0.7887	0.5996	0.6967	0.6445	0.7681	0.8136	0.7902	0.9407
0.1143	2.0	3458	0.1772	0.4907	0.5419	0.5150	0.9402	0.9600	0.9500	0.8400	0.8933	0.8658	0.7818	0.8359	0.8079	0.6035	0.7121	0.6533	0.7843	0.8369	0.8098	0.9441
0.0654	3.0	5187	0.1992	0.5314	0.4963	0.5133	0.9513	0.9600	0.9556	0.8648	0.8949	0.8796	0.7916	0.8667	0.8274	0.6033	0.7429	0.6659	0.8086	0.8357	0.8219	0.9459
0.0407	4.0	6916	0.2400	0.5270	0.5448	0.5357	0.9513	0.9607	0.9560	0.8554	0.8858	0.8703	0.8029	0.8462	0.8240	0.6635	0.7147	0.6881	0.8104	0.8364	0.8232	0.9456
0.0208	5.0	8645	0.2612	0.5132	0.5698	0.5400	0.9586	0.9600	0.9593	0.8726	0.8964	0.8843	0.8079	0.8410	0.8241	0.6571	0.7095	0.6823	0.8117	0.8426	0.8269	0.9472
0.0158	6.0	10374	0.2784	0.5019	0.5786	0.5375	0.9520	0.9614	0.9567	0.8669	0.9017	0.8839	0.8177	0.8282	0.8229	0.6490	0.7224	0.6837	0.8041	0.8462	0.8246	0.9485
0.0098	7.0	12103	0.3086	0.5159	0.5727	0.5428	0.9585	0.9572	0.9578	0.8743	0.8888	0.8815	0.8216	0.8385	0.8299	0.6855	0.7172	0.7010	0.8167	0.8402	0.8283	0.9489
0.0038	8.0	13832	0.3087	0.5189	0.5433	0.5308	0.9560	0.9614	0.9587	0.8810	0.8956	0.8882	0.8066	0.8769	0.8403	0.6808	0.7455	0.7117	0.8193	0.8452	0.8321	0.9482
0.0035	9.0	15561	0.3158	0.5147	0.5668	0.5395	0.9573	0.9614	0.9594	0.8820	0.8933	0.8876	0.8063	0.8538	0.8294	0.6573	0.7198	0.6871	0.8144	0.8438	0.8288	0.9501
0.0016	10.0	17290	0.3380	0.5171	0.5536	0.5348	0.9502	0.9656	0.9579	0.8635	0.9047	0.8836	0.8134	0.8718	0.8416	0.6690	0.7481	0.7063	0.8108	0.8509	0.8304	0.9491
0.0004	11.0	19019	0.3369	0.5164	0.5786	0.5457	0.9555	0.9649	0.9602	0.8759	0.9024	0.8890	0.8106	0.8667	0.8377	0.6822	0.7506	0.7148	0.8147	0.8538	0.8338	0.9502
0.0009	12.0	20748	0.3389	0.5225	0.5800	0.5498	0.9521	0.9642	0.9581	0.8732	0.9062	0.8894	0.8115	0.8718	0.8405	0.6862	0.7532	0.7181	0.8150	0.8557	0.8348	0.9505

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_Testing

NeRUBioS_RoBERTa_base_bne_Training_Testing

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

为此模型做种