readme: ---
  license: apache-2.0
  tags:
  - token-classification
  datasets:
  - wikiann-conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  
  model-index:
  - name: distilroberta-base-ner-wikiann-conll2003-3-class
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: wikiann-conll2003
        type: wikiann-conll2003
      metrics:
        - name: Precision
          type: precision
          value: 0.9624757386241104
        - name: Recall
          type: recall
          value: 0.9667497021553124
        - name: F1
          type: f1
          value: 0.964607986167396
        - name: Accuracy
          type: accuracy
          value: 0.9913626461292995
  ---
  
  <!-- 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. -->
  
  # distilroberta-base-ner-wikiann-conll2003-3-class
  
  This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the wikiann and conll2003 dataset. It consists out of the classes of wikiann. 
  
  O (0), B-PER (1), I-PER (2), B-ORG (3), I-ORG (4) B-LOC (5), I-LOC (6).
  
  eval F1-Score: **96,25** (merged dataset)   
  test F1-Score: **92,41** (merged dataset) 
  
  
  
  ## Model Usage
  
  ```python
  from transformers import AutoTokenizer, AutoModelForTokenClassification
  from transformers import pipeline
  
  tokenizer = AutoTokenizer.from_pretrained("philschmid/distilroberta-base-ner-wikiann-conll2003-3-class")
  model = AutoModelForTokenClassification.from_pretrained("philschmid/distilroberta-base-ner-wikiann-conll2003-3-class")
  
  nlp = pipeline("ner", model=model, tokenizer=tokenizer, grouped_entities=True)
  example = "My name is Philipp and live in Germany"
  
  nlp(example)
  
  ```
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 4.9086903597787154e-05
  - train_batch_size: 32
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  It achieves the following results on the evaluation set:
  - Loss: 0.0520
  - Precision: 0.9625
  - Recall: 0.9667
  - F1: 0.9646
  - Accuracy: 0.9914
  
  It achieves the following results on the test set:
  - Loss: 0.141
  - Precision: 0.917
  - Recall: 0.9313
  - F1: 0.9241
  - Accuracy: 0.9807
  
  
  ### Framework versions
  
  - Transformers 4.6.1
  - Pytorch 1.8.1+cu101
  - Datasets 1.6.2
  - Tokenizers 0.10.3
  