readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: biomedical-ner-all-finetuned-ner
    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. -->
  
  # biomedical-ner-all-finetuned-ner
  
  This model is a fine-tuned version of [d4data/biomedical-ner-all](https://huggingface.co/d4data/biomedical-ner-all) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0020
  - Precision: 1.0
  - Recall: 1.0
  - F1: 1.0
  - Accuracy: 1.0
  
  ## 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: 100
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 4    | 0.1905          | 0.7660    | 0.8480 | 0.8049 | 0.9447   |
  | No log        | 2.0   | 8    | 0.1689          | 0.7619    | 0.8565 | 0.8065 | 0.9482   |
  | No log        | 3.0   | 12   | 0.1535          | 0.7950    | 0.8801 | 0.8354 | 0.9552   |
  | No log        | 4.0   | 16   | 0.1337          | 0.816     | 0.8737 | 0.8438 | 0.9598   |
  | No log        | 5.0   | 20   | 0.1206          | 0.8191    | 0.9015 | 0.8583 | 0.9636   |
  | No log        | 6.0   | 24   | 0.1047          | 0.8285    | 0.9101 | 0.8673 | 0.9689   |
  | No log        | 7.0   | 28   | 0.0943          | 0.8509    | 0.9165 | 0.8825 | 0.9703   |
  | No log        | 8.0   | 32   | 0.0848          | 0.8495    | 0.9186 | 0.8827 | 0.9734   |
  | No log        | 9.0   | 36   | 0.0756          | 0.8732    | 0.9293 | 0.9004 | 0.9780   |
  | No log        | 10.0  | 40   | 0.0671          | 0.8884    | 0.9379 | 0.9125 | 0.9825   |
  | No log        | 11.0  | 44   | 0.0625          | 0.8912    | 0.9293 | 0.9099 | 0.9836   |
  | No log        | 12.0  | 48   | 0.0548          | 0.9048    | 0.9358 | 0.92   | 0.9857   |
  | No log        | 13.0  | 52   | 0.0492          | 0.9129    | 0.9422 | 0.9273 | 0.9881   |
  | No log        | 14.0  | 56   | 0.0466          | 0.9187    | 0.9443 | 0.9314 | 0.9885   |
  | No log        | 15.0  | 60   | 0.0400          | 0.9308    | 0.9507 | 0.9407 | 0.9902   |
  | No log        | 16.0  | 64   | 0.0369          | 0.9414    | 0.9636 | 0.9524 | 0.9920   |
  | No log        | 17.0  | 68   | 0.0342          | 0.9356    | 0.9636 | 0.9494 | 0.9920   |
  | No log        | 18.0  | 72   | 0.0300          | 0.9514    | 0.9636 | 0.9574 | 0.9937   |
  | No log        | 19.0  | 76   | 0.0284          | 0.9558    | 0.9722 | 0.9639 | 0.9941   |
  | No log        | 20.0  | 80   | 0.0270          | 0.9580    | 0.9764 | 0.9671 | 0.9944   |
  | No log        | 21.0  | 84   | 0.0234          | 0.9621    | 0.9786 | 0.9703 | 0.9948   |
  | No log        | 22.0  | 88   | 0.0215          | 0.9662    | 0.9807 | 0.9734 | 0.9955   |
  | No log        | 23.0  | 92   | 0.0199          | 0.9662    | 0.9807 | 0.9734 | 0.9955   |
  | No log        | 24.0  | 96   | 0.0179          | 0.9725    | 0.9829 | 0.9776 | 0.9955   |
  | No log        | 25.0  | 100  | 0.0164          | 0.9851    | 0.9893 | 0.9872 | 0.9965   |
  | No log        | 26.0  | 104  | 0.0158          | 0.9851    | 0.9914 | 0.9883 | 0.9969   |
  | No log        | 27.0  | 108  | 0.0145          | 0.9809    | 0.9914 | 0.9862 | 0.9979   |
  | No log        | 28.0  | 112  | 0.0130          | 0.9809    | 0.9914 | 0.9862 | 0.9983   |
  | No log        | 29.0  | 116  | 0.0122          | 0.9809    | 0.9914 | 0.9862 | 0.9983   |
  | No log        | 30.0  | 120  | 0.0114          | 0.9872    | 0.9936 | 0.9904 | 0.9986   |
  | No log        | 31.0  | 124  | 0.0104          | 0.9872    | 0.9936 | 0.9904 | 0.9990   |
  | No log        | 32.0  | 128  | 0.0098          | 0.9830    | 0.9914 | 0.9872 | 0.9986   |
  | No log        | 33.0  | 132  | 0.0094          | 0.9830    | 0.9914 | 0.9872 | 0.9986   |
  | No log        | 34.0  | 136  | 0.0091          | 0.9915    | 0.9957 | 0.9936 | 0.9993   |
  | No log        | 35.0  | 140  | 0.0085          | 0.9915    | 0.9957 | 0.9936 | 0.9990   |
  | No log        | 36.0  | 144  | 0.0080          | 0.9915    | 0.9957 | 0.9936 | 0.9990   |
  | No log        | 37.0  | 148  | 0.0073          | 0.9915    | 0.9957 | 0.9936 | 0.9993   |
  | No log        | 38.0  | 152  | 0.0069          | 0.9915    | 0.9957 | 0.9936 | 0.9993   |
  | No log        | 39.0  | 156  | 0.0067          | 0.9915    | 0.9957 | 0.9936 | 0.9993   |
  | No log        | 40.0  | 160  | 0.0063          | 0.9915    | 0.9957 | 0.9936 | 0.9