readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - few_nerd
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: Cybonto-distilbert-base-uncased-finetuned-ner-v0.1
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: few_nerd
        type: few_nerd
        args: supervised
      metrics:
      - name: Precision
        type: precision
        value: 0.7377633209417596
      - name: Recall
        type: recall
        value: 0.7817648386368765
      - name: F1
        type: f1
        value: 0.7591269959856158
      - name: Accuracy
        type: accuracy
        value: 0.9383331648547562
  ---
  
  <!-- 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. -->
  
  # Cybonto-distilbert-base-uncased-finetuned-ner-v0.1
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the few_nerd dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1930
  - Precision: 0.7378
  - Recall: 0.7818
  - F1: 0.7591
  - Accuracy: 0.9383
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## 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: 36
  - eval_batch_size: 36
  - 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 | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.2001        | 1.0   | 3661  | 0.1954          | 0.7244    | 0.7750 | 0.7488 | 0.9360   |
  | 0.1717        | 2.0   | 7322  | 0.1898          | 0.7392    | 0.7767 | 0.7575 | 0.9384   |
  | 0.1485        | 3.0   | 10983 | 0.1930          | 0.7378    | 0.7818 | 0.7591 | 0.9383   |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  