👨🏻⚕️ 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
Text Classification
Transformers
PyTorch
Safetensors
Spanish
deberta-v2
biomedical
clinical
spanish
mdeberta-v3-base
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("IIC/mdeberta-v3-base-livingner3")
model = AutoModelForSequenceClassification.from_pretrained("IIC/mdeberta-v3-base-livingner3", device_map="auto")Quick Links
mdeberta-v3-base-livingner3
This model is a finetuned version of mdeberta-v3-base for the livingner3 dataset used in a benchmark in the paper TODO. The model has a F1 of 0.153
Please refer to the original publication for more information TODO LINK
Parameters used
| parameter | Value |
|---|---|
| batch size | 64 |
| learning rate | 1e-05 |
| classifier dropout | 0.2 |
| warmup ratio | 0 |
| warmup steps | 0 |
| weight decay | 0 |
| optimizer | AdamW |
| epochs | 10 |
| early stopping patience | 3 |
BibTeX entry and citation info
TODO
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Dataset used to train IIC/mdeberta-v3-base-livingner3
Collection including IIC/mdeberta-v3-base-livingner3
Evaluation results
- f1 on livingner3test set self-reported0.153
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="IIC/mdeberta-v3-base-livingner3")