readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  language: en
  widget:
  - text: "My name is Scott and I live in Columbus."
  - text: "Apple was founded in 1976 by Steve Jobs, Steve Wozniak and Ronald Wayne."
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: albert-base-v2-finetuned-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conll2003
        type: conll2003
        args: conll2003
      metrics:
      - name: Precision
        type: precision
        value: 0.9252213840603477
      - name: Recall
        type: recall
        value: 0.9329732113328189
      - name: F1
        type: f1
        value: 0.9290811285541773
      - name: Accuracy
        type: accuracy
        value: 0.9848205157332728
  ---
  
  <!-- 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. -->
  
  # albert-base-v2-finetuned-ner
  
  This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the conll2003 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0626
  - Precision: 0.9252
  - Recall: 0.9330
  - F1: 0.9291
  - Accuracy: 0.9848
  
  ## Model description
  
  More information needed
  
  ## limitations
  
  #### 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/albert-base-v2-finetuned-ner")
  
  model = AutoModelForTokenClassification.from_pretrained("Jorgeutd/albert-base-v2-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 and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 64
  - eval_batch_size: 64
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 220  | 0.0863          | 0.8827    | 0.8969 | 0.8898 | 0.9773   |
  | No log        | 2.0   | 440  | 0.0652          | 0.8951    | 0.9199 | 0.9073 | 0.9809   |
  | 0.1243        | 3.0   | 660  | 0.0626          | 0.9191    | 0.9208 | 0.9200 | 0.9827   |
  | 0.1243        | 4.0   | 880  | 0.0585          | 0.9227    | 0.9281 | 0.9254 | 0.9843   |
  | 0.0299        | 5.0   | 1100 | 0.0626          | 0.9252    | 0.9330 | 0.9291 | 0.9848   |
  
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.8.1+cu111
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  