readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - tner/ontonotes5
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: distilbert-finetuned-ner-ontonotes
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: ontonotes5
        type: ontonotes5
        config: ontonotes5
        split: train
        args: ontonotes5
      metrics:
      - name: Precision
        type: precision
        value: 0.8535359959297889
      - name: Recall
        type: recall
        value: 0.8788553467356427
      - name: F1
        type: f1
        value: 0.8660106468785288
      - name: Accuracy
        type: accuracy
        value: 0.9749625470373822
  widget:
  - text: 'I am Jack. I live in California and I work at Apple '
    example_title: Example 1
  - text: 'Wow this book is amazing and costs only 4€ '
    example_title: Example 2
  ---
  
  <!-- 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. -->
  
  # distilbert-finetuned-ner-ontonotes
  
  This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the ontonotes5 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1448
  - Precision: 0.8535
  - Recall: 0.8789
  - F1: 0.8660
  - Accuracy: 0.9750
  
  ## Model description
  
  Token classification experiment, NER, on business topics.
  
  ## Intended uses & limitations
  
  The model can be used on token classification, in particular NER. It is fine tuned on business domain.
  
  ## Training and evaluation data
  
  The dataset used is [ontonotes5](https://huggingface.co/datasets/tner/ontonotes5)
  
  ## Training procedure
  
  ### 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: 6
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0937        | 1.0   | 7491  | 0.0998          | 0.8367    | 0.8587 | 0.8475 | 0.9731   |
  | 0.0572        | 2.0   | 14982 | 0.1084          | 0.8338    | 0.8759 | 0.8543 | 0.9737   |
  | 0.0403        | 3.0   | 22473 | 0.1145          | 0.8521    | 0.8707 | 0.8613 | 0.9748   |
  | 0.0265        | 4.0   | 29964 | 0.1222          | 0.8535    | 0.8815 | 0.8672 | 0.9752   |
  | 0.0148        | 5.0   | 37455 | 0.1365          | 0.8536    | 0.8770 | 0.8651 | 0.9747   |
  | 0.0111        | 6.0   | 44946 | 0.1448          | 0.8535    | 0.8789 | 0.8660 | 0.9750   |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.1
  - Tokenizers 0.12.1
  