Fine-tunning
BETO for detection of entities in medical prescriptions. More models and detailes can be found
in our repository.
This is a fine-tuned version of
bert-clinical-scratch-wl-es from
PLN group @ CMM.
Which is a fine-tunned version
bert-base-spanish-wwm-uncased (BETO) from
DCC UChile.
This work is part of a project that aims to have entity recognition models on prescription data from Minsal (Chile Health Minsistry), for the MDS7201 course from Data Science MSc program at UChile.
We use data from a Chilean Hospital, which is not available for public use, but we do provide the files with which we trained the models.
The procedure is the following one:
We provide a
demo.
Here we introduce those funtions that are necessary in order to translate the model's output into understandable tags.
We also provide a complementary model:
beto-prescripciones-medicas-ADMIN.
This model tags the output of the current model of those tokens tagged as ADMIN.
The
demo includes such model, and the output of both is shown as an example below:
This example is also shown in
this notebook, which uses the model as a blackbox.
1training_args = TrainingArguments(
2 output_dir="./results",
3 learning_rate=2e-5,
4 per_device_train_batch_size=16,
5 per_device_eval_batch_size=16,
6 num_train_epochs=5,
7 weight_decay=0.01
8)
1training_args = TrainingArguments(
2 output_dir = "./results",
3 evaluation_strategy = "epoch",
4 learning_rate = 2e-5,
5 per_device_train_batch_size = 16,
6 per_device_eval_batch_size = 16,
7 num_train_epochs = 20,
8 weight_decay = 0.01,
9)
