readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-finetuned-ADEs_model_1
    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. -->
  
  # bert-finetuned-ADEs_model_1
  
  This model is a fine-tuned version of [jsylee/scibert_scivocab_uncased-finetuned-ner](https://huggingface.co/jsylee/scibert_scivocab_uncased-finetuned-ner) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1938
  - Precision: 0.6759
  - Recall: 0.6710
  - F1: 0.6735
  - Accuracy: 0.9132
  
  ## 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: 5e-07
  - 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: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.1987        | 1.0   | 640  | 0.1989          | 0.6618    | 0.6692 | 0.6655 | 0.9116   |
  | 0.1954        | 2.0   | 1280 | 0.1953          | 0.6710    | 0.6532 | 0.6620 | 0.9132   |
  | 0.1934        | 3.0   | 1920 | 0.1961          | 0.6586    | 0.6823 | 0.6702 | 0.9120   |
  | 0.1879        | 4.0   | 2560 | 0.1940          | 0.6727    | 0.6718 | 0.6722 | 0.9133   |
  | 0.1897        | 5.0   | 3200 | 0.1938          | 0.6759    | 0.6710 | 0.6735 | 0.9132   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0+cu113
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  