readme: ---
  license: apache-2.0
  tags:
  - token-classification
  datasets:
  - wikiann
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: distilroberta-base-ner-wikiann
    results:
    - task:
        type: token-classification
        name: Token Classification
      dataset:
        name: wikiann
        type: wikiann
      metrics:
      - type: precision
        value: 0.8331921416757433
        name: Precision
      - type: recall
        value: 0.84243586083126
        name: Recall
      - type: f1
        value: 0.8377885044416501
        name: F1
      - type: accuracy
        value: 0.91930707459758
        name: Accuracy
    - task:
        type: token-classification
        name: Token Classification
      dataset:
        name: wikiann
        type: wikiann
        config: en
        split: test
      metrics:
      - type: accuracy
        value: 0.9200373733433721
        name: Accuracy
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNGFmMTNkZDYwMDllNjE5ZTVjYzYwYTQyMDFjYzNkYTkxZmVmOTNkOTFlOTU4MmM2MmFlMWQzMTcwZGViOTA3ZCIsInZlcnNpb24iOjF9.pOwPcBmA7XJdq9QgCNoCivTsu0WfsCnvRtzObDrqhFtrO2PjLNf9tmlQeahGcBGFo6yIHvhndBYwf__lN-4nBg
      - type: precision
        value: 0.9258482820953792
        name: Precision
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzFhNGJlMzk0N2JmYmU3YjAxZjJjNGFjZjZjOTJhODc3MjQyODMzYzE2Y2Y4NWQ4YThhMjg3NWI1MGRmODczMiIsInZlcnNpb24iOjF9.eVTQJqXeGY0XZaGURXBrT8sjMl7O_SxuFB4NS7C6jbpr46MMZdusvzkmndOIrGjReB2vB3sAmpcT0hydpqRkDg
      - type: recall
        value: 0.9347545055892119
        name: Recall
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiN2Y5ZGIzM2JlOWNjZGUzOWU5MGIwOTFiODM4NmU3NGQ3ZmUxYzM4ZmYxNjIwOTE0ZWFiYWJhMzk4NDg4ZjI3MSIsInZlcnNpb24iOjF9.tzl3gTEDFuj7kpGsERkQzXfh7B0Qwao31VcXKF1rSvf3ulVgXsU-vTB2oZiGr3w5AySr_80J0pIpSpvGzfhNAQ
      - type: f1
        value: 0.9302800779500893
        name: F1
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjY5MDM2ZWQ1MzJmNDFhMGFmZmQ1MzM0NmJmOTVmYTM1OWZmNzc4YWI4ZWUwMTFlMTQ5MTJmYWRhNmVmZTUyZCIsInZlcnNpb24iOjF9.zMUq4ZGLfu0eQF7lHNkaf6LByypIevygVGLpBA3jW80OUy5VeZDK7d6q0RV_N4SO5gTkLEjoDvSqLDcaw-9VBw
      - type: loss
        value: 0.3007512390613556
        name: loss
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzI5YmIxODFkN2NkYzJkZDgyZTc4MDhlMDkyMzM3NWFiZWQ1MmUzMDA1MGYyM2RlNzVlNTIwNDcwNTFmNjYwMSIsInZlcnNpb24iOjF9.D8vx5YhoNHY4CdRXEt3rL95odR2kZJ1e_c34HD28xX9YeWKIjjt4E0FSz6Xw4ufJd9UlCnQ_u4VPFTYI-RXlCQ
  ---
  
  <!-- 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
  
  This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the wikiann dataset.
  
  
  eval F1-Score: **83,78** 
  test F1-Score: **83,76** 
  
  
  ## Model Usage
  
  ```python
  from transformers import AutoTokenizer, AutoModelForTokenClassification
  from transformers import pipeline
  
  tokenizer = AutoTokenizer.from_pretrained("philschmid/distilroberta-base-ner-wikiann")
  model = AutoModelForTokenClassification.from_pretrained("philschmid/distilroberta-base-ner-wikiann")
  
  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: 0.3156
  - Precision: 0.8332
  - Recall: 0.8424
  - F1: 0.8378
  - Accuracy: 0.9193
  
  It achieves the following results on the test set:
  - Loss: 0.3023
  - Precision: 0.8301
  - Recall: 0.8452
  - F1: 0.8376
  - Accuracy: 0.92
  
  
  ### Framework versions
  
  - Transformers 4.6.1
  - Pytorch 1.8.1+cu101
  - Datasets 1.6.2
  - Tokenizers 0.10.2
  