readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - conllpp
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: roberta-large-md-conllpp-v3
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conllpp
        type: conllpp
        config: conllpp
        split: train
        args: conllpp
      metrics:
      - name: Precision
        type: precision
        value: 0.9979509811647884
      - name: Recall
        type: recall
        value: 0.9950755690599051
      - name: F1
        type: f1
        value: 0.9965112008813808
      - name: Accuracy
        type: accuracy
        value: 0.9942715623990525
  ---
  
  <!-- 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. -->
  
  # roberta-large-md-conllpp-v3
  
  This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the conllpp dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0564
  - Precision: 0.9980
  - Recall: 0.9951
  - F1: 0.9965
  - Accuracy: 0.9943
  
  ## 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: 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: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0945        | 1.0   | 878  | 0.0317          | 0.9975    | 0.9950 | 0.9963 | 0.9939   |
  | 0.0175        | 2.0   | 1756 | 0.0483          | 0.9980    | 0.9953 | 0.9967 | 0.9946   |
  | 0.0105        | 3.0   | 2634 | 0.0505          | 0.9982    | 0.9941 | 0.9961 | 0.9937   |
  | 0.0056        | 4.0   | 3512 | 0.0574          | 0.9982    | 0.9939 | 0.9961 | 0.9935   |
  | 0.0022        | 5.0   | 4390 | 0.0564          | 0.9980    | 0.9951 | 0.9965 | 0.9943   |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.12.1
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  