Negation_Scope_Detection_SFU_Spanish_NLP-CIC-WFU_DisTEMIST_fine_tuned

This model is a fine-tuned version of ajtamayoh/NER_EHR_Spanish_model_Mulitlingual_BERT on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.3219
• Precision: 0.7403
• Recall: 0.7571
• F1: 0.7486
• Accuracy: 0.9518

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: 5e-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	Precision	Recall	F1	Accuracy
No log	1.0	72	0.2142	0.5227	0.6497	0.5793	0.9267
No log	2.0	144	0.2019	0.625	0.7062	0.6631	0.9420
No log	3.0	216	0.3089	0.6444	0.6554	0.6499	0.9432
No log	4.0	288	0.2376	0.6952	0.7345	0.7143	0.9478
No log	5.0	360	0.2876	0.7037	0.7514	0.7268	0.9538
No log	6.0	432	0.3077	0.7278	0.7401	0.7339	0.9534
0.091	7.0	504	0.3219	0.7403	0.7571	0.7486	0.9518

Framework versions

• Transformers 4.20.1
• Pytorch 1.11.0+cu113
• Datasets 2.3.2
• Tokenizers 0.12.1