readme: ---
  license: apache-2.0
  tags:
  - token-classification
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: distilroberta-base-ner-conll2003
    results:
    - task:
        type: token-classification
        name: Token Classification
      dataset:
        name: conll2003
        type: conll2003
      metrics:
      - type: precision
        value: 0.9492923423001218
        name: Precision
      - type: recall
        value: 0.9565545901020023
        name: Recall
      - type: f1
        value: 0.9529096297690173
        name: F1
      - type: accuracy
        value: 0.9883096560400111
        name: Accuracy
    - task:
        type: token-classification
        name: Token Classification
      dataset:
        name: conll2003
        type: conll2003
        config: conll2003
        split: validation
      metrics:
      - type: accuracy
        value: 0.9883249976987512
        name: Accuracy
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTEwNzFlMjk0ZDY4NTg2MGQxMDZkM2IyZjdjNDEwYmNiMWY1MWZiNzg1ZjMyZTlkYzQ0MmVmNTZkMjEyMGQ1YiIsInZlcnNpb24iOjF9.zxapWje7kbauQ5-VDNbY487JB5wkN4XqgaLwoX1cSmNfgpp-MPCjqrocxayb1kImbN8CvzOpU1aSfvRfyd5fAw
      - type: precision
        value: 0.9906910190038265
        name: Precision
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWRjMjYyOGQ2MGMwOGE1ODQyNDU1MzZiNWU4MGUzYWVlNjQ3NDhjZDRlZTE0NDlmMGJjZjliZjU2ZmFiZmZiYyIsInZlcnNpb24iOjF9.G_QY9mDkIkllmWPsgmUoVgs-R9XjfYkdJMS8hcyGM-7NXsbigUgZZnhfD0TjDak62UoEplqwSX5r0S4xKPdxBQ
      - type: recall
        value: 0.9916635820847483
        name: Recall
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODE0MDE5ZWMzNTM5MTA1NTI4YzNhNzI2NzVjODIzZWY0OWE2ODJiN2FiNmVkNGVkMTI2ODZiOGEwNTEzNzk2MCIsInZlcnNpb24iOjF9.zenVqRfs8TrKoiIu_QXQJtHyj3dEH97ZDLxUn_UJ2tdW36hpBflgKCJNBvFFkra7bS4cNRfIkwxxCUMWH1ptBg
      - type: f1
        value: 0.9911770619696786
        name: F1
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZWZjY2NiNjZlNDFiODQ3M2JkOWJjNzRlY2FmNjMwNGFkNzFmNTBkOGQ5YTcyZjUzNjAwNDAxMThiNTE5ZThiNiIsInZlcnNpb24iOjF9.c9aD9hycCS-WBaLUb8NKzIpd2LE6xfJrhg3fL9_832RiMq5gcMs9qtarP3Jbo6WbPs_WThr_v4gn7K4Ti-0-CA
      - type: loss
        value: 0.05638007074594498
        name: loss
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNGM3NTQ5ODBhMDcyNjBjMGUxMDgzYjI2NjEwNjM0MjU0MjEzMTRmODA2MjMwZWU1YTQ3OWU2YjUzNTliZTkwMSIsInZlcnNpb24iOjF9.03OwbxrdKm-vg6ia5CBYdEaSCuRbT0pLoEvwpd4NtjydVzo5wzS-pWgY6vH4PlI0ZCTBY0Po0IZSsJulWJttDg
  ---
  
  <!-- 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-conll2003
  
  This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the conll2003 dataset.
  
  eval F1-Score: **95,29** (CoNLL-03)   
  test F1-Score: **90,74** (CoNLL-03)  
  
  eval F1-Score: **95,29** (CoNLL++ / CoNLL-03 corrected)  
  test F1-Score: **92,23** (CoNLL++ / CoNLL-03 corrected)  
  
  
  ## Model Usage
  
  ```python
  from transformers import AutoTokenizer, AutoModelForTokenClassification
  from transformers import pipeline
  
  tokenizer = AutoTokenizer.from_pretrained("philschmid/distilroberta-base-ner-conll2003")
  model = AutoModelForTokenClassification.from_pretrained("philschmid/distilroberta-base-ner-conll2003")
  
  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: 4.9902376275441704e-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: 6.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  #### CoNNL2003
  
  It achieves the following results on the evaluation set:
  - Loss: 0.0583
  - Precision: 0.9493
  - Recall: 0.9566
  - F1: 0.9529
  - Accuracy: 0.9883
  
  It achieves the following results on the test set:
  - Loss: 0.2025
  - Precision: 0.8999
  - Recall: 0.915
  - F1: 0.9074
  - Accuracy: 0.9741
  
  #### CoNNL++ / CoNLL2003 corrected
  
  It achieves the following results on the evaluation set:
  - Loss: 0.0567
  - Precision: 0.9493
  - Recall: 0.9566
  - F1: 0.9529
  - Accuracy: 0.9883
  
  It achieves the following results on the test set:
  - Loss: 0.1359
  - Precision: 0.92
  - Recall: 0.9245
  - F1: 0.9223
  - Accuracy: 0.9785
  
  
  ### Framework versions
  
  - Transformers 4.6.1
  - Pytorch 1.8.1+cu101
  - Datasets 1.6.2
  - Tokenizers 0.10.2
  