readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - ju-bezdek/conll2003-SK-NER
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: outputs
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: ju-bezdek/conll2003-SK-NER
        type: ju-bezdek/conll2003-SK-NER
        args: conll2003-SK-NER
      metrics:
      - name: Precision
        type: precision
        value: 0.8189727994593682
      - name: Recall
        type: recall
        value: 0.8389581169955002
      - name: F1
        type: f1
        value: 0.8288450029922203
      - name: Accuracy
        type: accuracy
        value: 0.9526157920337243
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # outputs
  
  This model is a fine-tuned version of [gerulata/slovakbert](https://huggingface.co/gerulata/slovakbert) on the [ju-bezdek/conll2003-SK-NER](https://huggingface.co/datasets/ju-bezdek/conll2003-SK-NER) dataset.
  It achieves the following results on the evaluation (validation) set:
  - Loss: 0.1752
  - Precision: 0.8190
  - Recall: 0.8390
  - F1: 0.8288
  - Accuracy: 0.9526
  
  ## Model description
  
  More information needed
  
  ## Code example
  
  ```python:
  from transformers import pipeline, AutoModel, AutoTokenizer
  from spacy import displacy
  import os
  
  
  model_path="ju-bezdek/slovakbert-conll2003-sk-ner"
  
  aggregation_strategy="max"
  ner_pipeline = pipeline(task='ner', model=model_path, aggregation_strategy=aggregation_strategy)
  
  input_sentence= "Ruský premiér Viktor Černomyrdin v piatok povedal, že prezident Boris Jeľcin , ktorý je na dovolenke mimo Moskvy , podporil mierový plán šéfa bezpečnosti Alexandra Lebedu pre Čečensko, uviedla tlačová agentúra Interfax"
  ner_ents = ner_pipeline(input_sentence)
  print(ner_ents)
  
  ent_group_labels = [ner_pipeline.model.config.id2label[i][2:] for i in ner_pipeline.model.config.id2label if i>0]
  
  options = {"ents":ent_group_labels}
  
  dicplacy_ents = [{"start":ent["start"], "end":ent["end"], "label":ent["entity_group"]} for ent in ner_ents]
  displacy.render({"text":input_sentence, "ents":dicplacy_ents}, style="ent", options=options, jupyter=True, manual=True)
  ```
  
  ### Result: 
  <div>
               <span class="tex2jax_ignore"><div class="entities" style="line-height: 2.5; direction: ltr">
         <mark class="entity" style="background: #ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;">
             Ruský
             <span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">MISC</span>
         </mark>
          premiér 
         <mark class="entity" style="background: #ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;">
             Viktor Černomyrdin
             <span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">PER</span>
         </mark>
          v piatok povedal, že prezident 
         <mark class="entity" style="background: #ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;">
             Boris Jeľcin,
             <span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">PER</span>
         </mark>
          , ktorý je na dovolenke mimo 
         <mark class="entity" style="background: #ff9561; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;">
             Moskvy
             <span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">LOC</span>
         </mark>
          , podporil mierový plán šéfa bezpečnosti 
         <mark class="entity" style="background: #ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;">
             Alexandra Lebedu
             <span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">PER</span>
         </mark>
          pre 
         <mark class="entity" style="background: #ff9561; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;">
             Čečensko,
             <span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">LOC</span>
         </mark>
          uviedla tlačová agentúra 
         <mark class="entity" style="background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;">
             Interfax
             <span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">ORG</span>
         </mark>
         </div></span>
         </div>
  
  
  
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training p