NeRUBioS_xlm_RoBERTa_base_Training_Testing

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

Loss: 0.3585
Negref Precision: 0.5638
Negref Recall: 0.6035
Negref F1: 0.5830
Neg Precision: 0.9508
Neg Recall: 0.9642
Neg F1: 0.9575
Nsco Precision: 0.8692
Nsco Recall: 0.9047
Nsco F1: 0.8866
Unc Precision: 0.8005
Unc Recall: 0.8846
Unc F1: 0.8404
Usco Precision: 0.6696
Usco Recall: 0.7815
Usco F1: 0.7212
Precision: 0.8184
Recall: 0.8628
F1: 0.8400
Accuracy: 0.9482

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
0.2259	1.0	1729	0.2246	0.4076	0.4890	0.4446	0.9112	0.9515	0.9310	0.7928	0.8654	0.8275	0.7015	0.8256	0.7585	0.4629	0.6735	0.5487	0.7158	0.8122	0.7610	0.9287
0.16	2.0	3458	0.2028	0.5217	0.5301	0.5259	0.9283	0.9642	0.9459	0.8311	0.8896	0.8593	0.7812	0.8513	0.8147	0.5734	0.7532	0.6511	0.7817	0.8405	0.8100	0.9397
0.1235	3.0	5187	0.2148	0.5176	0.4963	0.5067	0.9520	0.9607	0.9563	0.8641	0.8850	0.8744	0.7684	0.8846	0.8224	0.6113	0.7481	0.6728	0.8038	0.8350	0.8191	0.9439
0.0949	4.0	6916	0.2261	0.5054	0.6211	0.5573	0.9327	0.9642	0.9482	0.8450	0.8828	0.8635	0.7976	0.8487	0.8224	0.6034	0.7352	0.6628	0.7818	0.8512	0.8150	0.9450
0.0633	5.0	8645	0.2354	0.5609	0.5947	0.5773	0.9417	0.9649	0.9532	0.8669	0.9062	0.8861	0.8062	0.8641	0.8342	0.6334	0.7506	0.6871	0.8118	0.8573	0.8340	0.9461
0.0495	6.0	10374	0.2829	0.5585	0.5962	0.5767	0.9445	0.9684	0.9563	0.8671	0.9077	0.8869	0.8116	0.8615	0.8358	0.6526	0.7532	0.6993	0.8151	0.8592	0.8366	0.9442
0.0365	7.0	12103	0.2699	0.5446	0.5830	0.5631	0.9552	0.9572	0.9562	0.8804	0.9024	0.8913	0.8080	0.8846	0.8446	0.6521	0.7661	0.7045	0.8182	0.8550	0.8362	0.9473
0.0265	8.0	13832	0.3082	0.5630	0.5580	0.5605	0.9466	0.9593	0.9529	0.8702	0.9024	0.8860	0.8038	0.8718	0.8364	0.6571	0.7635	0.7063	0.8194	0.8502	0.8345	0.9460
0.0216	9.0	15561	0.3286	0.5485	0.5977	0.5720	0.9388	0.9691	0.9537	0.8715	0.9077	0.8892	0.8085	0.8769	0.8413	0.6453	0.7763	0.7048	0.8105	0.8633	0.8361	0.9455
0.0133	10.0	17290	0.3503	0.5732	0.6094	0.5907	0.9481	0.9628	0.9554	0.8698	0.8994	0.8843	0.8137	0.8846	0.8477	0.6816	0.7815	0.7281	0.8223	0.8616	0.8415	0.9482
0.0088	11.0	19019	0.3476	0.5584	0.6182	0.5868	0.9450	0.9656	0.9552	0.8614	0.9070	0.8836	0.8080	0.8846	0.8446	0.6659	0.7789	0.7180	0.8126	0.8661	0.8385	0.9483
0.0093	12.0	20748	0.3585	0.5638	0.6035	0.5830	0.9508	0.9642	0.9575	0.8692	0.9047	0.8866	0.8005	0.8846	0.8404	0.6696	0.7815	0.7212	0.8184	0.8628	0.8400	0.9482

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

ajtamayoh/NeRUBioS_xlm_RoBERTa_base_Training_Testing

NeRUBioS_xlm_RoBERTa_base_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