metadata
license: apache-2.0
widget:
- text: Ayer dormí la siesta durante 3 horas
- text: Recuerda tu cita con el médico el lunes a las 8 de la tarde
- text: Recuerda tomar la medicación cada noche
tags:
- LABEL-0 = NONE
- LABEL-1 = B-DATE
- LABEL-2 = I-DATE
- LABEL-3 = B-TIME
- LABEL-4 = I-TIME
- LABEL-5 = B-DURATION
- LABEL-6 = I-DURATION
- LABEL-7 = B-SET
- LABEL-8 = I-SET
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Bio-RoBERTime
results: []
Bio-RoBERTime
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-biomedical-clinical-es on the None dataset.
It achieves the following results on the E3C test set following the TempEval-3 evaluation metrics:
| E3C | Strict | Relaxed | type |
|---|---|---|---|
| RoBERTime | 0.7606 | 0.9108 | 0.8357 |
| Heideltime | 0.5945 | 0.7558 | 0.6083 |
| Annotador | 0.6006 | 0.7347 | 0.5598 |
Model description
- Developed by: Alejandro Sánchez de Castro, Juan Martínez Romo, Lourdes Araujo
This model is the result of the paper "RoBERTime: A novel model for the detection of temporal expressions in Spanish"
Cite as:
@article{sanchez2023robertime, title={RoBERTime: A novel model for the detection of temporal expressions in Spanish}, author={Sánchez-de-Castro-Fernández, Alejandro and Araujo Serna, Lourdes and Martínez Romo, Juan}, year={2023}, publisher={Sociedad Española para el Procesamiento del Lenguaje Natural} }
Intended uses & limitations
This model is prepared for the detection of temporal expressions extension in Spanish. It may work in other languages due to RoBERTa multilingual capabilities. This model does not normalize the expression value. This is considered to be a separate task.
Training and evaluation data
This model has been trained on the Spanish Timebank corpus and E3C corpus
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 72
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 24
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2