readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: small-vanilla-target-conll2003
    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. -->
  
  # small-vanilla-target-conll2003
  
  This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1128
  - Precision: 0.8947
  - Recall: 0.9165
  - F1: 0.9055
  - Accuracy: 0.9782
  
  ## 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: 3e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: constant
  - training_steps: 5000
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.217         | 1.14  | 500  | 0.0895          | 0.8414    | 0.8846 | 0.8624 | 0.9725   |
  | 0.0723        | 2.28  | 1000 | 0.0808          | 0.8853    | 0.8975 | 0.8914 | 0.9762   |
  | 0.0427        | 3.42  | 1500 | 0.0807          | 0.8886    | 0.9118 | 0.9001 | 0.9778   |
  | 0.0274        | 4.56  | 2000 | 0.0816          | 0.8983    | 0.9142 | 0.9062 | 0.9790   |
  | 0.0186        | 5.69  | 2500 | 0.0910          | 0.8990    | 0.9201 | 0.9094 | 0.9788   |
  | 0.0132        | 6.83  | 3000 | 0.0933          | 0.8978    | 0.9180 | 0.9078 | 0.9789   |
  | 0.0097        | 7.97  | 3500 | 0.0954          | 0.9025    | 0.9211 | 0.9117 | 0.9798   |
  | 0.0076        | 9.11  | 4000 | 0.1003          | 0.9007    | 0.9221 | 0.9113 | 0.9794   |
  | 0.0058        | 10.25 | 4500 | 0.1037          | 0.9032    | 0.9222 | 0.9126 | 0.9798   |
  | 0.005         | 11.39 | 5000 | 0.1128          | 0.8947    | 0.9165 | 0.9055 | 0.9782   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.1.dev0
  - Tokenizers 0.13.2
  