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--- |
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license: apache-2.0 |
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widget: |
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- text: "Ayer dormí la siesta durante 3 horas" |
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- text: "Recuerda tu cita con el médico el lunes a las 8 de la tarde" |
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- text: "Recuerda tomar la medicación cada noche" |
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tags: |
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- LABEL-0 = NONE |
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- LABEL-1 = B-DATE |
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- LABEL-2 = I-DATE |
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- LABEL-3 = B-TIME |
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- LABEL-4 = I-TIME |
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- LABEL-5 = B-DURATION |
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- LABEL-6 = I-DURATION |
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- LABEL-7 = B-SET |
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- LABEL-8 = I-SET |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: Bio-RoBERTime |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Bio-RoBERTime |
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This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-biomedical-clinical-es](https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-clinical-es) on the None dataset. |
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It achieves the following results on the E3C test set following the TempEval-3 evaluation metrics: |
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| E3C | Strict | Relaxed | type | |
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|------------|:-------:|--------:|-------:| |
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| RoBERTime | **0.7606** | **0.9108** | **0.8357** | |
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| Heideltime | 0.5945 | 0.7558 | 0.6083 | |
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| Annotador | 0.6006 | 0.7347 | 0.5598 | |
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## Model description |
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- **Developed by**: Alejandro Sánchez de Castro, Juan Martínez Romo, Lourdes Araujo |
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This model is the result of the paper "RoBERTime: A novel model for the detection of temporal expressions in Spanish" |
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- **Cite as**: |
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@article{sanchez2023robertime, |
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title={RoBERTime: A novel model for the detection of temporal expressions in Spanish}, |
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author={Sánchez-de-Castro-Fernández, Alejandro and Araujo Serna, Lourdes and Martínez Romo, Juan}, |
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year={2023}, |
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publisher={Sociedad Española para el Procesamiento del Lenguaje Natural} |
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} |
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## Intended uses & limitations |
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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. |
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## Training and evaluation data |
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This model has been trained on the Spanish Timebank corpus and E3C corpus |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 8e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 72 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 24 |
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### Framework versions |
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- Transformers 4.24.0 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.7.0 |
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- Tokenizers 0.13.2 |
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