metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- disease
metrics:
- precision
- recall
- f1
- accuracy
base_model: plncmm/roberta-clinical-wl-es
model-index:
- name: spanish-disease-tagger
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: disease
type: disease
config: disease
split: train
args: disease
metrics:
- type: precision
value: 0.8385373870172556
name: Precision
- type: recall
value: 0.8711054204011951
name: Recall
- type: f1
value: 0.8545111994975926
name: F1
- type: accuracy
value: 0.9487721041951381
name: Accuracy
spanish-disease-tagger
This model is a fine-tuned version of plncmm/roberta-clinical-wl-es on the disease dataset. It achieves the following results on the evaluation set:
- Loss: 0.1786
- Precision: 0.8385
- Recall: 0.8711
- F1: 0.8545
- Accuracy: 0.9488
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.2217 | 1.0 | 502 | 0.1698 | 0.8142 | 0.8587 | 0.8359 | 0.9437 |
| 0.1203 | 2.0 | 1004 | 0.1735 | 0.8513 | 0.8528 | 0.8520 | 0.9473 |
| 0.093 | 3.0 | 1506 | 0.1786 | 0.8385 | 0.8711 | 0.8545 | 0.9488 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2