ner-roberta-es-clinical-trials-ner

jpherrerap/ner-roberta-es-clinical-trials-ner

ner-roberta-es-clinical-trials-ner is a token classification model from jpherrerap. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as cc-by-nc-4.0.

should probably proofread and complete it, then remove this comment. --

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Datasets

jpherrerap/competencia2

Model card

From the Hugging Face model README

ner-roberta-es-clinical-trials-ner

This model is a fine-tuned version of lcampillos/roberta-es-clinical-trials-ner on the jpherrerap/competencia2 dataset. It achieves the following results on the evaluation set:

Loss: 0.2661
Body Part Precision: 0.7124
Body Part Recall: 0.8173
Body Part F1: 0.7612
Body Part Number: 197
Disease Precision: 0.7712
Disease Recall: 0.7697
Disease F1: 0.7704
Disease Number: 521
Family Member Precision: 0.8462
Family Member Recall: 0.8462
Family Member F1: 0.8462
Family Member Number: 13
Medication Precision: 0.8378
Medication Recall: 0.8378
Medication F1: 0.8378
Medication Number: 37
Procedure Precision: 0.6510
Procedure Recall: 0.7239
Procedure F1: 0.6855
Procedure Number: 134
Overall Precision: 0.7418
Overall Recall: 0.7772
Overall F1: 0.7591
Overall Accuracy: 0.9238

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: 16
eval_batch_size: 16
seed: 13
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Body Part Precision	Body Part Recall	Body Part F1	Body Part Number	Disease Precision	Disease Recall	Disease F1	Disease Number	Family Member Precision	Family Member Recall	Family Member F1	Family Member Number	Medication Precision	Medication Recall	Medication F1	Medication Number	Procedure Precision	Procedure Recall	Procedure F1	Procedure Number	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.3329	1.0	502	0.2561	0.6830	0.7766	0.7268	197	0.7718	0.7658	0.7688	521	0.9231	0.9231	0.9231	13	0.75	0.8108	0.7792	37	0.6218	0.7239	0.6690	134	0.7274	0.7661	0.7462	0.9219
0.1699	2.0	1004	0.2661	0.7124	0.8173	0.7612	197	0.7712	0.7697	0.7704	521	0.8462	0.8462	0.8462	13	0.8378	0.8378	0.8378	37	0.6510	0.7239	0.6855	134	0.7418	0.7772	0.7591	0.9238

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3

Task: Token Classification
Library: transformers
Type: roberta
License: cc-by-nc-4.0
Languages: es
Created: Jul 10, 2023
Updated: Jul 12, 2023