readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - jnlpba
  widget:
  - text: "The widespread circular form of DNA molecules inside cells creates very serious topological problems during replication. Due to the helical structure of the double helix the parental strands of circular DNA form a link of very high order, and yet they have to be unlinked before the cell division."
  - text: "It consists of 25 exons encoding a 1,278-amino acid glycoprotein that is composed of 13 transmembrane domains"
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: pubmedbert-finetuned-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: jnlpba
        type: jnlpba
        config: jnlpba
        split: train
        args: jnlpba
      metrics:
      - name: Precision
        type: precision
        value: 0.6877153861747415
      - name: Recall
        type: recall
        value: 0.7833063957515586
      - name: F1
        type: f1
        value: 0.7324050086355786
      - name: Accuracy
        type: accuracy
        value: 0.926729986431479
  ---
  
  <!-- 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. -->
  
  # pubmedbert-finetuned-ner
  
  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 jnlpba dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3766
  - Precision: 0.6877
  - Recall: 0.7833
  - F1: 0.7324
  - Accuracy: 0.9267
  
  ## 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: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.1607        | 1.0   | 2319  | 0.2241          | 0.6853    | 0.7835 | 0.7311 | 0.9302   |
  | 0.112         | 2.0   | 4638  | 0.2620          | 0.6753    | 0.7929 | 0.7294 | 0.9276   |
  | 0.0785        | 3.0   | 6957  | 0.3014          | 0.6948    | 0.7731 | 0.7319 | 0.9268   |
  | 0.055         | 4.0   | 9276  | 0.3526          | 0.6898    | 0.7801 | 0.7322 | 0.9268   |
  | 0.0418        | 5.0   | 11595 | 0.3766          | 0.6877    | 0.7833 | 0.7324 | 0.9267   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  