👨🏻⚕️ Spanish Clinical Encoders Analysis
Collection
Models and datasets used in the paper `A comparative analysis of Spanish Clinical encoder-based models on NER and classification tasks` • 91 items • Updated • 3
How to use IIC/BETO_Galen-nubes with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("token-classification", model="IIC/BETO_Galen-nubes") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("IIC/BETO_Galen-nubes")
model = AutoModelForSequenceClassification.from_pretrained("IIC/BETO_Galen-nubes")This model is a finetuned version of BETO_Galen for the nubes dataset used in a benchmark in the paper TODO. The model has a F1 of 0.762
Please refer to the original publication for more information TODO LINK
| parameter | Value |
|---|---|
| batch size | 16 |
| learning rate | 4e-05 |
| classifier dropout | 0 |
| warmup ratio | 0 |
| warmup steps | 0 |
| weight decay | 0 |
| optimizer | AdamW |
| epochs | 10 |
| early stopping patience | 3 |
TODO