readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: biomedical-ner-all-finetuned-ner-v2
    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-v2
  
  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: 0.0003
  - 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: 0.0002
  - 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    | 0.7203          | 0.4673    | 0.5727 | 0.5146 | 0.8272   |
  | No log        | 2.0   | 8    | 0.4702          | 0.6870    | 0.6855 | 0.6862 | 0.8833   |
  | No log        | 3.0   | 12   | 0.3434          | 0.7544    | 0.7462 | 0.7503 | 0.9014   |
  | No log        | 4.0   | 16   | 0.2434          | 0.7683    | 0.8200 | 0.7933 | 0.9304   |
  | No log        | 5.0   | 20   | 0.1858          | 0.7939    | 0.8438 | 0.8181 | 0.9402   |
  | No log        | 6.0   | 24   | 0.1638          | 0.8174    | 0.8547 | 0.8356 | 0.9475   |
  | No log        | 7.0   | 28   | 0.1094          | 0.8650    | 0.8894 | 0.8770 | 0.9616   |
  | No log        | 8.0   | 32   | 0.0714          | 0.8989    | 0.9262 | 0.9124 | 0.9779   |
  | No log        | 9.0   | 36   | 0.0491          | 0.9275    | 0.9436 | 0.9355 | 0.9841   |
  | No log        | 10.0  | 40   | 0.0295          | 0.9421    | 0.9523 | 0.9471 | 0.9895   |
  | No log        | 11.0  | 44   | 0.0222          | 0.9741    | 0.9783 | 0.9762 | 0.9946   |
  | No log        | 12.0  | 48   | 0.0158          | 0.9870    | 0.9892 | 0.9881 | 0.9975   |
  | No log        | 13.0  | 52   | 0.0102          | 0.9913    | 0.9913 | 0.9913 | 0.9978   |
  | No log        | 14.0  | 56   | 0.0076          | 0.9957    | 0.9935 | 0.9946 | 0.9993   |
  | No log        | 15.0  | 60   | 0.0057          | 0.9978    | 0.9978 | 0.9978 | 0.9996   |
  | No log        | 16.0  | 64   | 0.0099          | 0.9935    | 0.9935 | 0.9935 | 0.9978   |
  | No log        | 17.0  | 68   | 0.0027          | 1.0       | 1.0    | 1.0    | 1.0      |
  | No log        | 18.0  | 72   | 0.0045          | 0.9957    | 0.9978 | 0.9967 | 0.9993   |
  | No log        | 19.0  | 76   | 0.0035          | 0.9957    | 1.0    | 0.9978 | 0.9993   |
  | No log        | 20.0  | 80   | 0.0021          | 0.9957    | 0.9978 | 0.9967 | 0.9996   |
  | No log        | 21.0  | 84   | 0.0034          | 0.9957    | 0.9978 | 0.9967 | 0.9996   |
  | No log        | 22.0  | 88   | 0.0021          | 1.0       | 0.9978 | 0.9989 | 0.9996   |
  | No log        | 23.0  | 92   | 0.0014          | 1.0       | 1.0    | 1.0    | 1.0      |
  | No log        | 24.0  | 96   | 0.0014          | 1.0       | 1.0    | 1.0    | 1.0      |
  | No log        | 25.0  | 100  | 0.0015          | 1.0       | 1.0    | 1.0    | 1.0      |
  | No log        | 26.0  | 104  | 0.0012          | 1.0       | 1.0    | 1.0    | 1.0      |
  | No log        | 27.0  | 108  | 0.0010          | 1.0       | 1.0    | 1.0    | 1.0      |
  | No log        | 28.0  | 112  | 0.0009          | 1.0       | 1.0    | 1.0    | 1.0      |
  | No log        | 29.0  | 116  | 0.0009          | 1.0       | 1.0    | 1.0    | 1.0      |
  | No log        | 30.0  | 120  | 0.0033          | 0.9978    | 1.0    | 0.9989 | 0.9996   |
  | No log        | 31.0  | 124  | 0.0054          | 0.9957    | 0.9957 | 0.9957 | 0.9993   |
  | No log        | 32.0  | 128  | 0.0031          | 0.9978    | 1.0    | 0.9989 | 0.9996   |
  | No log        | 33.0  | 132  | 0.0025          | 0.9978    | 1.0    | 0.9989 | 0.9996   |
  | No log        | 34.0  | 136  | 0.0016          | 0.9978    | 1.0    | 0.9989 | 0.9996   |
  | No log        | 35.0  | 140  | 0.0019          | 0.9957    | 0.9957 | 0.9957 | 0.9993   |
  | No log        | 36.0  | 144  | 0.0022          | 0.9978    | 1.0    | 0.9989 | 0.9996   |
  | No log        | 37.0  | 148  | 0.0022          | 0.9978    | 1.0    | 0.9989 | 0.9996   |
  | No log        | 38.0  | 152  | 0.0021          | 0.9978    | 1.0    | 0.9989 | 0.9996   |
  | No log        | 39.0  | 156  | 0.0017          | 0.9978    | 1.0    | 0.9989 | 0.9996   |
  | No log        | 40.0  | 160  | 0.0015          | 0.9978    | 1.0    | 0.998