transformers safetensors xlm-roberta token-classification robson-criteria-classification ner es arxiv:1910.09700 base_model:FacebookAI/xlm-roberta-base base_model:finetune:FacebookAI/xlm-roberta-base autotrain_compatible endpoints_compatible region:us
---
base_model:
- FacebookAI/xlm-roberta-base
language:
- es
library_name: transformers
tags:
- robson-criteria-classification
- ner
---

# Model Card for Model ID
The **xlm-roberta-base-robson-criteria-classification-ner-es** is a Named Entity Recognition (NER) model for the Spanish language fine-tuned from the RoBERTa base model.

## Model Details



### Model Description

In the table below, we have outlined the entities set. Most entities are based on the obstetric variables described in the Robson Implementation Manual [Robson Classification: Implementation Manual](https://www.who.int/publications/i/item/9789241513197). However, we have added nine additional entities related to the use of antibiotics, uterotonics, dose, posology, complications, obstetric hemorrhage, the outcome of delivery (whether it was a vaginal birth or a cesarean section), and the personal information within the Electronic Health Records (EHRs).


#### Clinical entities set
<table>
<thead>
<tr>
    <th>No</th>
    <th>Spanish Entity</th>
    <th>English Entity</th>
    <th>Obsetric variable</th>
</tr>
</thead>
<tbody>
<tr>
    <td>1</td><td>Parto nulípara</td><td>Nullipara labor</td><td rowspan="2">Parity</td>
</tr>
<tr>
    <td>2</td><td>Parto multípara</td><td>Multipara labor</td>
</tr>
<tr>
    <td>3</td><td>Cesárea previa (Si)</td><td>One or more Cesarean Section</td><td rowspan="2">Previous Cesarean Section</td>
</tr>
<tr>
    <td>4</td><td>Cesárea previa (No)</td><td>None Cesarean Section</td>
</tr>
<tr>
    <td>5</td><td>TDP espontáneo</td><td>Spontaneous labor</td><td rowspan="3">Onset of labour</td>
</tr>
<tr>
    <td>6</td><td>TDP inducido</td><td>Induced labor</td>
</tr>
<tr>
    <td>7</td><td>TDP No: cesárea programada</td><td>No labor, scheduled Cesarean Section</td>
</tr>
<tr>
    <td>8</td><td>Embarazo único</td><td>Singleton pregnancy</td><td rowspan="2">Number of fetuses</td>
</tr>
<tr>
    <td>9</td><td>Embarazo Múltiple</td><td>Multiple pregnancy</td>    
</tr>
<tr>
    <td>10</td><td>Edad < 37 semanas</td><td>Preterm pregnancy</td><td rowspan="2">Gestational age</td>
</tr>
<tr>
    <td>11</td><td>Edad &ge; 37 semanas</td><td>Term pregnancy</td>
</tr>
<tr>
    <td>12</td><td>Posición cefálica</td><td>Cephalic presentation</td><td rowspan="3">Fetal lie and presentation</td>
</tr>
<tr>
    <td>13</td><td>Posición podálica</td><td>Breech presentation</td>
</tr>
<tr>
    <td>14</td><td>Situación transversa</td><td>Transverse lie</td>
</tr>
<tr>
    <td>15</td><td>Antibiótico</td><td>Antibiotic</td><td></td>
</tr>
<tr>
    <td>16</td><td>Complicación</td><td>Complication</td><td></td>
</tr>
<tr>
    <td>17</td><td>Dosis</td><td>Dose</td><td></td>
</tr>
<tr>
    <td>18</td><td>Hemorragia Obstétrica </td><td> Obstetric Hemorrhage</td><td></td>
</tr>
<tr>
    <td>19</td><td>Info personal</td><td>Personal Information</td><td></td>
</tr>
<tr>
    <td>20</td><td>Posología</td><td>Posology</td><td></td>
</tr>
<tr>
    <td>21</td><td>Tipo de resolución: parto</td><td>Delivery resolution: VB</td><td></td>
</tr>
<tr>
    <td>22</td><td>Tipo de resolución: cesarea</td><td>Delivery resolution: CS</td><td></td>
</tr>
<tr>
    <td>23</td><td>Uterotónico</td><td>Uterotonic</td><td></td>
</tr>
</tbody>
</table>

This model detects entities by classifying every token according to the IOB format:

```python
['O', 'B-Antibiótico', 'I-Antibiótico', 'B-Cesárea previa (NO)', 'I-Cesárea previa (NO)', 'B-Cesárea previa (SI)', 'I-Cesárea previa (SI)', 'B-Complicación', 'I-Complicación', 'B-Dosis', 'I-Dosis', 'B-Edad < 37 semanas', 'I-Edad < 37 semanas', 'B-Edad >= 37 semanas', 'I-Edad >= 37 semanas', 'B-Embarazo múltiple', 'I-Embarazo múltiple', 'B-Embarazo único', 'I-Embarazo único', 'B-Hemorragia obstétrica', 'I-Hemorragia obstétrica', 'B-Info personal', 'I-Info personal', 'B-Parto multípara', 'I-Parto multípara', 'B-Parto nulípara', 'I-Parto nulípara', 'B-Posición cefálica', 'I-Posición cefálica', 'B-Posición podálica', 'I-Posición podálica', 'B-Posología', 'I-Posología', 'B-Situación transversa', 'I-Situación transversa', 'B-TDP No: cesárea programada', 'I-TDP No: cesárea programada', 'B-TDP espontáneo', 'I-TDP espontáneo', 'B-TDP inducido', 'I-TDP inducido', 'B-Tipo de resolución: cesárea', 'I-Tipo de resolución: cesárea', 'B-Tipo de resolución: parto', 'I-Tipo de resolución: parto', 'B-Uterotónico', 'I-Uterotónico']
```

## 🤝 Author
Created by [Orlando Ramos](https://huggingface.co/orlandxrf).  
This model is part of the organization's efforts [LATEiimas](https://huggingface.co/LATEiimas).



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