readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  model-index:
  - name: bert-finetuned-ner
    results: []
  ---
  
  <!-- 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. -->
  
  # bert-finetuned-ner
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0814
  
  
  ## Model description
  
  bert-base-NER is a fine-tuned BERT model that is ready to use for Named Entity Recognition and achieves state-of-the-art performance for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscellaneous (MISC).
  
  Specifically, this model is a bert-base-cased model that was fine-tuned on the English version of the standard CoNLL-2003 Named Entity Recognition dataset.
  
  If you'd like to use a larger BERT-large model fine-tuned on the same dataset, a bert-large-NER version is also available.
  
  #  How to Use
  You can use this model with Transformers pipeline for NER.
  
      from transformers import AutoTokenizer, AutoModelForTokenClassification
      from transformers import pipeline
      
      tokenizer = AutoTokenizer.from_pretrained("Hatman/bert-finetuned-ner")
      model = AutoModelForTokenClassification.from_pretrained("Hatman/bert-finetuned-ner")
      
      nlp = pipeline("ner", model=model, tokenizer=tokenizer)
      example = "My name is Wolfgang and I live in Berlin"
      
      ner_results = nlp(example)
      print(ner_results)
  
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 8
  - 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
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss |
  |:-------------:|:-----:|:----:|:---------------:|
  | 0.0181        | 1.0   | 1756 | 0.1301          |
  | 0.0166        | 2.0   | 3512 | 0.0762          |
  | 0.0064        | 3.0   | 5268 | 0.0814          |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  