readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  language: en
  widget:
  - text: "My name is Scott and I live in Columbus."
  - text: "My name is Scott and I am calling from Buffalo, NY. I would like to file a complain with United Airlines."
  - text: "Apple was founded in 1976 by Steve Jobs, Steve Wozniak and Ronald Wayne."
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-large-uncased-finetuned-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conll2003
        type: conll2003
        args: conll2003
      metrics:
      - name: Precision
        type: precision
        value: 0.9504719600222099
      - name: Recall
        type: recall
        value: 0.9574896520863632
      - name: F1
        type: f1
        value: 0.9539679001337494
      - name: Accuracy
        type: accuracy
        value: 0.9885618059637473
  ---
  
  <!-- 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-large-uncased-finetuned-ner
  
  This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the conll2003 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0778
  - Precision: 0.9505
  - Recall: 0.9575
  - F1: 0.9540
  - Accuracy: 0.9886
  
  ## Model description
  
  More information needed
  
  #### Limitations and bias
  
  This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Furthermore, the model occassionally tags subword tokens as entities and post-processing of results may be necessary to handle those cases. 
  
  
  #### How to use
  
  You can use this model with Transformers *pipeline* for NER.
  
  ```python
  from transformers import pipeline
  from transformers import AutoTokenizer, AutoModelForTokenClassification
  tokenizer = AutoTokenizer.from_pretrained("Jorgeutd/bert-large-uncased-finetuned-ner")
  model = AutoModelForTokenClassification.from_pretrained("Jorgeutd/bert-large-uncased-finetuned-ner")
  nlp = pipeline("ner", model=model, tokenizer=tokenizer)
  example = "My name is Scott and I live in Ohio"
  ner_results = nlp(example)
  print(ner_results)
  ```
  
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 16
  - eval_batch_size: 64
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.1997        | 1.0   | 878  | 0.0576          | 0.9316    | 0.9257 | 0.9286 | 0.9837   |
  | 0.04          | 2.0   | 1756 | 0.0490          | 0.9400    | 0.9513 | 0.9456 | 0.9870   |
  | 0.0199        | 3.0   | 2634 | 0.0557          | 0.9436    | 0.9540 | 0.9488 | 0.9879   |
  | 0.0112        | 4.0   | 3512 | 0.0602          | 0.9443    | 0.9569 | 0.9506 | 0.9881   |
  | 0.0068        | 5.0   | 4390 | 0.0631          | 0.9451    | 0.9589 | 0.9520 | 0.9882   |
  | 0.0044        | 6.0   | 5268 | 0.0638          | 0.9510    | 0.9567 | 0.9538 | 0.9885   |
  | 0.003         | 7.0   | 6146 | 0.0722          | 0.9495    | 0.9560 | 0.9527 | 0.9885   |
  | 0.0016        | 8.0   | 7024 | 0.0762          | 0.9491    | 0.9595 | 0.9543 | 0.9887   |
  | 0.0018        | 9.0   | 7902 | 0.0769          | 0.9496    | 0.9542 | 0.9519 | 0.9883   |
  | 0.0009        | 10.0  | 8780 | 0.0778          | 0.9505    | 0.9575 | 0.9540 | 0.9886   |
  
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.8.1+cu111
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  