readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - enoriega/keyword_pubmed
  metrics:
  - accuracy
  model-index:
  - name: kw_pubmed_vanilla_sentence_10000_0.0003_2
    results:
    - task:
        name: Masked Language Modeling
        type: fill-mask
      dataset:
        name: enoriega/keyword_pubmed sentence
        type: enoriega/keyword_pubmed
        args: sentence
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.6767448105720579
  ---
  
  <!-- 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_vanilla_sentence_10000_0.0003_2
  
  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 enoriega/keyword_pubmed sentence dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.5883
  - Accuracy: 0.6767
  
  ## 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: 16
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 500
  - total_train_batch_size: 8000
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3.0
  
  ### Training results
  
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  