readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - keyword_pubmed_dataset
  metrics:
  - accuracy
  model-index:
  - name: kw_pubmed_1000_0.0003
    results:
    - task:
        name: Masked Language Modeling
        type: fill-mask
      dataset:
        name: keyword_pubmed_dataset
        type: keyword_pubmed_dataset
        args: sentence
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.33938523162661094
  ---
  
  <!-- 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. -->
  
  # kw_pubmed_1000_0.0003
  
  This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the keyword_pubmed_dataset dataset.
  It achieves the following results on the evaluation set:
  - Loss: 4.7086
  - Accuracy: 0.3394
  
  ## 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: 0.0003
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - gradient_accumulation_steps: 250
  - total_train_batch_size: 8000
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | No log        | 0.09  | 4    | 4.3723          | 0.3436   |
  | 6.0386        | 0.17  | 8    | 4.2113          | 0.3442   |
  | 3.7573        | 0.26  | 12   | 4.2079          | 0.3634   |
  | 2.9944        | 0.35  | 16   | 4.3370          | 0.3513   |
  | 2.7048        | 0.44  | 20   | 4.8594          | 0.3067   |
  | 2.7048        | 0.52  | 24   | 4.4929          | 0.3383   |
  | 2.9458        | 0.61  | 28   | 4.5146          | 0.3408   |
  | 2.3783        | 0.7   | 32   | 4.5680          | 0.3430   |
  | 2.2485        | 0.78  | 36   | 4.5095          | 0.3477   |
  | 2.1701        | 0.87  | 40   | 4.4971          | 0.3449   |
  | 2.1701        | 0.96  | 44   | 4.7051          | 0.3321   |
  | 2.0861        | 1.07  | 48   | 4.7615          | 0.3310   |
  | 2.4168        | 1.15  | 52   | 4.7086          | 0.3394   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  