NeRUBioS_xlm_RoBERTa_base_Training_Development

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: 0.3324
Negref Precision: 0.5672
Negref Recall: 0.5696
Negref F1: 0.5684
Neg Precision: 0.9480
Neg Recall: 0.9760
Neg F1: 0.9618
Nsco Precision: 0.8685
Nsco Recall: 0.9097
Nsco F1: 0.8886
Unc Precision: 0.8419
Unc Recall: 0.8842
Unc F1: 0.8625
Usco Precision: 0.6429
Usco Recall: 0.7383
Usco F1: 0.6873
Precision: 0.8190
Recall: 0.8548
F1: 0.8365
Accuracy: 0.9520

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
0.2316	1.0	1729	0.2023	0.4328	0.4958	0.4621	0.8759	0.9629	0.9173	0.7259	0.8397	0.7786	0.7059	0.8340	0.7646	0.4408	0.6836	0.5360	0.6864	0.8063	0.7415	0.9364
0.1596	2.0	3458	0.1756	0.4771	0.5274	0.5010	0.9252	0.9727	0.9484	0.8113	0.8836	0.8459	0.8036	0.8687	0.8349	0.5615	0.6953	0.6213	0.7624	0.8329	0.7961	0.9480
0.1197	3.0	5187	0.1735	0.5214	0.5401	0.5306	0.9436	0.9672	0.9553	0.8449	0.8800	0.8621	0.8094	0.8687	0.8380	0.5705	0.6953	0.6268	0.7891	0.8322	0.8101	0.9510
0.1006	4.0	6916	0.2003	0.5324	0.5717	0.5514	0.9365	0.9814	0.9584	0.8510	0.8955	0.8727	0.7965	0.8764	0.8346	0.5755	0.7148	0.6376	0.7890	0.8497	0.8182	0.9508
0.0706	5.0	8645	0.2077	0.5434	0.5675	0.5552	0.9497	0.9683	0.9589	0.8821	0.9062	0.8940	0.8285	0.8764	0.8518	0.6013	0.7188	0.6548	0.8107	0.8482	0.8290	0.9531
0.0514	6.0	10374	0.2554	0.5282	0.5527	0.5402	0.9281	0.9716	0.9493	0.8476	0.9050	0.8754	0.8433	0.8726	0.8577	0.6131	0.7305	0.6667	0.7950	0.8471	0.8202	0.9496
0.039	7.0	12103	0.2547	0.5306	0.5675	0.5484	0.9508	0.9705	0.9606	0.8672	0.9074	0.8868	0.8582	0.9112	0.8839	0.6609	0.7461	0.7009	0.8136	0.8551	0.8339	0.9525
0.0273	8.0	13832	0.2796	0.5447	0.5401	0.5424	0.9459	0.9738	0.9597	0.8615	0.9086	0.8844	0.8088	0.8494	0.8286	0.6575	0.75	0.7007	0.8115	0.8464	0.8286	0.9497
0.0214	9.0	15561	0.3079	0.5429	0.5738	0.5579	0.9391	0.9771	0.9577	0.8707	0.9121	0.8910	0.8448	0.9035	0.8731	0.6084	0.7344	0.6655	0.8066	0.8580	0.8315	0.9514
0.0136	10.0	17290	0.3172	0.5524	0.5781	0.5649	0.9459	0.9738	0.9597	0.8848	0.9121	0.8982	0.8476	0.8803	0.8636	0.6519	0.7461	0.6958	0.8201	0.8566	0.8380	0.9520
0.0098	11.0	19019	0.3312	0.5729	0.5717	0.5723	0.9501	0.9771	0.9634	0.8753	0.9086	0.8916	0.8476	0.8803	0.8636	0.6574	0.7422	0.6972	0.8251	0.8551	0.8398	0.9516
0.008	12.0	20748	0.3324	0.5672	0.5696	0.5684	0.9480	0.9760	0.9618	0.8685	0.9097	0.8886	0.8419	0.8842	0.8625	0.6429	0.7383	0.6873	0.8190	0.8548	0.8365	0.9520

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_Development

NeRUBioS_xlm_RoBERTa_base_Training_Development

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Seed this model