readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: biobert-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. -->
  
  # biobert-finetuned-ner
  
  This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0280
  - Precision: 0.9316
  - Recall: 0.9303
  - F1: 0.9310
  - Accuracy: 0.9953
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 8
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0311        | 1.0   | 6070  | 0.0278          | 0.8892    | 0.9098 | 0.8994 | 0.9939   |
  | 0.0213        | 2.0   | 12140 | 0.0330          | 0.9133    | 0.9071 | 0.9102 | 0.9934   |
  | 0.0132        | 3.0   | 18210 | 0.0224          | 0.9283    | 0.9194 | 0.9238 | 0.9952   |
  | 0.0078        | 4.0   | 24280 | 0.0243          | 0.9231    | 0.9180 | 0.9205 | 0.9949   |
  | 0.0068        | 5.0   | 30350 | 0.0222          | 0.9253    | 0.9303 | 0.9278 | 0.9957   |
  | 0.0035        | 6.0   | 36420 | 0.0245          | 0.9243    | 0.9344 | 0.9293 | 0.9955   |
  | 0.0039        | 7.0   | 42490 | 0.0282          | 0.9283    | 0.9372 | 0.9327 | 0.9954   |
  | 0.0008        | 8.0   | 48560 | 0.0280          | 0.9316    | 0.9303 | 0.9310 | 0.9953   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  