final-ft__roberta-clinical-wl-es__70k-ultrasounds-ner

This model is a fine-tuned version of manucos/final-ft__roberta-clinical-wl-es__70k-ultrasounds on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.3411
• Precision: 0.8624
• Recall: 0.9007
• F1: 0.8812
• Accuracy: 0.9380

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: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	280	0.3115	0.7961	0.8444	0.8196	0.9220
0.5059	2.0	560	0.2686	0.8117	0.8815	0.8452	0.9321
0.5059	3.0	840	0.2472	0.8623	0.8815	0.8718	0.9386
0.143	4.0	1120	0.2720	0.8505	0.8933	0.8714	0.9341
0.143	5.0	1400	0.2730	0.8541	0.8933	0.8733	0.9366
0.0852	6.0	1680	0.2987	0.8665	0.9037	0.8847	0.9394
0.0852	7.0	1960	0.2896	0.8671	0.8993	0.8829	0.9374
0.0475	8.0	2240	0.3160	0.8770	0.9081	0.8923	0.9405
0.0334	9.0	2520	0.3321	0.8640	0.9037	0.8834	0.9377
0.0334	10.0	2800	0.3392	0.8696	0.8993	0.8842	0.9374
0.0248	11.0	3080	0.3368	0.8636	0.9007	0.8818	0.9394
0.0248	12.0	3360	0.3502	0.8655	0.8963	0.8806	0.9358
0.0209	13.0	3640	0.3377	0.8755	0.8963	0.8858	0.9405
0.0209	14.0	3920	0.3408	0.8624	0.9007	0.8812	0.9383
0.0175	15.0	4200	0.3411	0.8624	0.9007	0.8812	0.9380

Framework versions

• Transformers 4.41.1
• Pytorch 2.3.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1