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

ajtamayoh/Negation_and_Uncertainty_Scope_Detection_mBERT_fine_tuned

Negation_and_Uncertainty_Scope_Detection_mBERT_fine_tuned

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Seed this model