---
license: cc-by-4.0
language:
- es
pipeline_tag: token-classification
---
# Model Card for Model ID
These model aim to recognise occupation mentions (NER) in Spanish clinical notes and to whom the occupation belongs.
## Model Details
PLM Model
Learning rate
Batch size
Epochs
Max length
Optimizer
Max clip grad norm
Epsilon
PlanTL-GOB-ES/ roberta-base-biomedical-es
2e-05
8
10
510
AdamW
1
1e-08
### Model Description
PlanTL-GOB-ES/roberta-base-biomedical-es model was fine-tuned using MEDDOPROF corpus (Salvador Lima-López, Eulàlia Farré-Maduell, Antonio Miranda-Escalada, Vicent Briva-Iglesias, & Martin Krallinger. (2022). MEDDOPROF corpus: complete gold standard annotations for occupation detection in medical documents in Spanish [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7116201)
Two models were built: A model for occupation recognition (MEDDO_FINAL_ROBERTA_ner_sentencia_510_8_10_2e-05_1e-08) and a model to detect to whom the profession belongs (MEDDO_FINAL_ROBERTA_class_sentencia_510_8_10_2e-05_1e-08).
More details about this can be found in MEDDOPROF shared task:
Lima-López, S., Farré-Maduell, E., Miranda-Escalada, A., Brivá-Iglesias, V., & Krallinger, M. (2021). Nlp applied to occupational health: Meddoprof shared task at iberlef 2021 on automatic recognition, classification and normalization of professions and occupations from medical texts. Procesamiento del Lenguaje Natural, 67, 243-256.
- **Developed by:** Alfredo Madrid
- **Language(s) (NLP):** Spanish
- **License:** CC BY-SA 4.0
- **Finetuned from model [optional]:** PlanTL-GOB-ES/roberta-base-biomedical-es
### Model Sources
- **Repository:** https://huggingface.co/HCSCRheuma/Occupations
- **Paper [optional]:** Madrid García, A. (2023). Recognition of professions in medical documentation.
## Uses
**Model 1**
```
import torch
import pandas as pd
import numpy as np
from transformers import AutoTokenizer, AutoModelForTokenClassification
model = AutoModelForTokenClassification.from_pretrained("MEDDO_FINAL_ROBERTA_ner_sentencia_510_8_10_2e-05_1e-08")
tokenizer = AutoTokenizer.from_pretrained("MEDDO_FINAL_ROBERTA_ner_sentencia_510_8_10_2e-05_1e-08")
```
```
note = "El paciente trabaja en una empresa de construccion los jueves"
tokenized_sentence = tokenizer.encode(note, truncation=True)
tokenized_words_ids = tokenizer(note, truncation=True)
word_ids = tokenized_words_ids.word_ids
input_ids = torch.tensor([tokenized_sentence])
with torch.no_grad():
output = model(input_ids)
label_indices = np.argmax(output[0].to('cpu').numpy(), axis=2)
tokens = tokenizer.convert_ids_to_tokens(input_ids.numpy()[0])
label_indices
```
```
df = pd.DataFrame(zip(tokens, label_indices[0], word_ids(0)), columns=["labels", "tokens", "relation"])
df['labels'] = df['labels'].str.replace('##', '')
df['tokens'] = df['tokens'].map({0: 'B-PROFESION', 1: 'B-SITUACION_LABORAL', 2: 'I-SITUACION_LABORAL', 3: 'I-ACTIVIDAD', 4: 'I-PROFESION', 5: 'O', 6: 'B-ACTIVIDAD', 7: 'PAD'})
df = df[1:-1]
df['relation'] = df['relation'].astype('int')
df['labels'] = df.groupby('relation')['labels'].transform(lambda x: ''.join(x))
df = df.groupby('relation').first()
df
```
**Output**
| relation | labels | tokens |
|:--------:|:-------------:|:-----------:|
| 0 | ĠEl | O |
| 1 | Ġpaciente | O |
| 2 | Ġtrabaja | B-PROFESION |
| 3 | Ġen | I-PROFESION |
| 4 | Ġuna | I-PROFESION |
| 5 | Ġempresa | I-PROFESION |
| 6 | Ġde | I-PROFESION |
| 7 | Ġconstruccion | I-PROFESION |
| 8 | Ġlos | O |
| 9 | Ġjueves | O |
**Model 2**
```
import torch
import pandas as pd
import numpy as np
from transformers import AutoTokenizer, AutoModelForTokenClassification
model = AutoModelForTokenClassification.from_pretrained("MEDDO_FINAL_ROBERTA_class_sentencia_510_8_10_2e-05_1e-08")
tokenizer = AutoTokenizer.from_pretrained("MEDDO_FINAL_ROBERTA_class_sentencia_510_8_10_2e-05_1e-08")
```
```
note = "El paciente trabaja en una empresa de construccion los jueves"
tokenized_sentence = tokenizer.encode(note, truncation=True)
tokenized_words_ids = tokenizer(note, truncation=True)
word_ids = tokenized_words_ids.word_ids
input_ids = torch.tensor([tokenized_sentence])
with torch.no_grad():
output = model(input_ids)
label_indices = np.argmax(output[0].to('cpu').numpy(), axis=2)
tokens = tokenizer.convert_ids_to_tokens(input_ids.to('cpu').numpy()[0])
label_indices
```
```
df = pd.DataFrame(zip(tokens, label_indices[0], word_ids(0)), columns=["labels", "tokens", "relation"])
df['labels'] = df['labels'].str.replace('##', '')
df['tokens'] = df['tokens'].map({0: 'B-FAMILIAR', 1: 'I-PACIENTE', 2: 'I-OTROS', 3: 'B-SANITARIO', 4: 'B-PACIENTE', 5: 'I-FAMILIAR', 6: 'O', 7: 'B-OTROS', 8: 'I-SANITARIO', 9: 'PAD'}
)
df = df[1:-1]
df['relation'] = df['relation'].astype('int')
df['labels'] = df.groupby('relation')['labels'].transform(lambda x: ''.join(x))
df = df.groupby('relation').first()
df
```
**Output**
| relation | labels | tokens |
|:--------:|:-------------:|:-----------:|
| 0 | ĠEl | O |
| 1 | Ġpaciente | O |
| 2 | Ġtrabaja | B-PACIENTE |
| 3 | Ġen | I-PACIENTE |
| 4 | Ġuna | I-PACIENTE |
| 5 | Ġempresa | I-PACIENTE |
| 6 | Ġde | I-PACIENTE |
| 7 | Ġconstruccion | I-PACIENTE |
| 8 | Ġlos | O |
| 9 | Ġjueves | O |