Negation_and_Uncertainty_Scope_Detection_mBERT_fine_tuned

This model is a fine-tuned version of bert-base-multilingual-cased on the NUBES dataset. This is a result of the PhD dissertation of Antonio Tamayo. It achieves the following results on the evaluation set:

• Loss: 0.1954
• Neg Precision: 0.9056
• Neg Recall: 0.9332
• Neg F1: 0.9192
• Nsco Precision: 0.8973
• Nsco Recall: 0.9079
• Nsco F1: 0.9026
• Unc Precision: 0.864
• Unc Recall: 0.9019
• Unc F1: 0.8825
• Usco Precision: 0.8210
• Usco Recall: 0.8650
• Usco F1: 0.8424
• Precision: 0.8886
• Recall: 0.9127
• F1: 0.9005
• Accuracy: 0.9698

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: 7

Training results

Training Loss	Epoch	Step	Validation Loss	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
0.1688	1.0	1726	0.1386	0.8885	0.9064	0.8974	0.8706	0.8697	0.8702	0.8326	0.8309	0.8318	0.7694	0.8304	0.7987	0.8621	0.8775	0.8697	0.9633
0.111	2.0	3452	0.1455	0.8803	0.9128	0.8962	0.8726	0.8924	0.8824	0.8244	0.8622	0.8429	0.7391	0.8125	0.7741	0.8547	0.8888	0.8714	0.9624
0.0678	3.0	5178	0.1621	0.9204	0.9004	0.9103	0.9051	0.8961	0.9006	0.8571	0.8643	0.8607	0.8454	0.7935	0.8187	0.9010	0.8828	0.8918	0.9679
0.043	4.0	6904	0.1590	0.9099	0.9212	0.9155	0.8885	0.9042	0.8963	0.8525	0.8810	0.8665	0.8288	0.8426	0.8356	0.8874	0.9020	0.8946	0.9696
0.0278	5.0	8630	0.1679	0.9140	0.9272	0.9206	0.8977	0.9057	0.9017	0.8656	0.8873	0.8763	0.8315	0.8594	0.8452	0.8939	0.9076	0.9007	0.9696
0.0153	6.0	10356	0.1908	0.8994	0.9359	0.9173	0.8888	0.9101	0.8993	0.8676	0.8894	0.8784	0.8122	0.8638	0.8372	0.8819	0.9137	0.8975	0.9688
0.0082	7.0	12082	0.1954	0.9056	0.9332	0.9192	0.8973	0.9079	0.9026	0.864	0.9019	0.8825	0.8210	0.8650	0.8424	0.8886	0.9127	0.9005	0.9698

Framework versions

• Transformers 4.29.2
• Pytorch 2.0.1+cu118
• Datasets 2.12.0
• Tokenizers 0.13.3