readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - mlner2021
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: mlner-mlwptok-muril
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: mlner2021
        type: mlner2021
        args: MLNER2021
      metrics:
      - name: Precision
        type: precision
        value: 0.0
      - name: Recall
        type: recall
        value: 0.0
      - name: F1
        type: f1
        value: 0.0
      - name: Accuracy
        type: accuracy
        value: 0.8112759262826688
  ---
  
  <!-- 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. -->
  
  # mlner-mlwptok-muril
  
  This model is a fine-tuned version of [google/muril-base-cased](https://huggingface.co/google/muril-base-cased) on the mlner2021 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.8331
  - Precision: 0.0
  - Recall: 0.0
  - F1: 0.0
  - Accuracy: 0.8113
  
  ## 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: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1  | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:---:|:--------:|
  | 1.447         | 1.0   | 1389  | 0.9396          | 0.0       | 0.0    | 0.0 | 0.8113   |
  | 0.898         | 2.0   | 2778  | 0.8883          | 0.0       | 0.0    | 0.0 | 0.8113   |
  | 0.859         | 3.0   | 4167  | 0.8721          | 0.0       | 0.0    | 0.0 | 0.8113   |
  | 0.8302        | 4.0   | 5556  | 0.8666          | 0.0       | 0.0    | 0.0 | 0.8113   |
  | 0.8165        | 5.0   | 6945  | 0.8403          | 0.0       | 0.0    | 0.0 | 0.8113   |
  | 0.8143        | 6.0   | 8334  | 0.8376          | 0.0       | 0.0    | 0.0 | 0.8113   |
  | 0.8034        | 7.0   | 9723  | 0.8393          | 0.0       | 0.0    | 0.0 | 0.8113   |
  | 0.7766        | 8.0   | 11112 | 0.8383          | 0.0       | 0.0    | 0.0 | 0.8113   |
  | 0.794         | 9.0   | 12501 | 0.8346          | 0.0       | 0.0    | 0.0 | 0.8113   |
  | 0.7858        | 10.0  | 13890 | 0.8331          | 0.0       | 0.0    | 0.0 | 0.8113   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  