readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - plo_dunfiltered_config
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: scibert_scivocab_uncased-finetuned-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: plo_dunfiltered_config
        type: plo_dunfiltered_config
        args: PLODunfiltered
      metrics:
      - name: Precision
        type: precision
        value: 0.964925429790286
      - name: Recall
        type: recall
        value: 0.9612323892385586
      - name: F1
        type: f1
        value: 0.9630753691636831
      - name: Accuracy
        type: accuracy
        value: 0.9593916827485913
  ---
  
  <!-- 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. -->
  
  # scibert_scivocab_uncased-finetuned-ner
  
  This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the plo_dunfiltered_config dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1390
  - Precision: 0.9649
  - Recall: 0.9612
  - F1: 0.9631
  - Accuracy: 0.9594
  
  ## 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: 1e-05
  - train_batch_size: 32
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 11
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.1176        | 1.4   | 5000  | 0.1243          | 0.9570    | 0.9511 | 0.9540 | 0.9502   |
  | 0.0973        | 2.81  | 10000 | 0.1129          | 0.9609    | 0.9572 | 0.9590 | 0.9553   |
  | 0.0721        | 4.21  | 15000 | 0.1198          | 0.9645    | 0.9585 | 0.9615 | 0.9578   |
  | 0.0634        | 5.62  | 20000 | 0.1259          | 0.9649    | 0.9589 | 0.9619 | 0.9582   |
  | 0.0572        | 7.02  | 25000 | 0.1321          | 0.9653    | 0.9609 | 0.9631 | 0.9594   |
  | 0.0472        | 8.43  | 30000 | 0.1390          | 0.9649    | 0.9612 | 0.9631 | 0.9594   |
  | 0.0434        | 9.83  | 35000 | 0.1442          | 0.9656    | 0.9613 | 0.9634 | 0.9598   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.1+cu111
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  