readme: ---
  language: es
  license: gpl-3.0
  tags:
  - PyTorch
  - Transformers
  - Token Classification
  - roberta
  - roberta-base-bne
  widget:
  - text: "Fue antes de llegar a Sigüeiro, en el Camino de Santiago."
  - text: "El proyecto lo financia el Ministerio de Industria y Competitividad."
  model-index:
  - name: roberta-bne-ner-cds
    results: []
  ---
  
  # Introduction
  
  This model is a fine-tuned version of [roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) for Named-Entity Recognition, in the domain of tourism related to the Way of Saint Jacques. It recognizes four types of entities: location (LOC), organizations (ORG), person (PER) and miscellaneous (MISC).
  
  ## Usage
  
  You can use this model with Transformers *pipeline* for NER.
  
  ```python
  from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
  
  tokenizer = AutoTokenizer.from_pretrained("roberta-bne-ner-cds")
  model = AutoModelForTokenClassification.from_pretrained("roberta-bne-ner-cds")
  
  example = "Fue antes de llegar a Sigüeiro, en el Camino de Santiago. El proyecto lo financia el Ministerio de Industria y Competitividad."
  ner_pipe = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
  
  for ent in ner_pipe(example):
      print(ent)
  ```
  
  ## Dataset
  
  ToDo
  
  ## Model performance
  
  entity|precision|recall|f1
  -|-|-|-
  PER|0.965|0.924|0.944
  ORG|0.900|0.701|0.788
  LOC|0.982|0.985|0.983
  MISC|0.798|0.874|0.834
  micro avg|0.964|0.968|0.966|4265
  macro avg|0.911|0.871|0.887
  weighted avg|0.965|0.968|0.966
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 32
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3.0
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  