readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - wikiann
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: mbert-finetuned-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: wikiann
        type: wikiann
        args: lv
      metrics:
      - name: Precision
        type: precision
        value: 0.9304986338797814
      - name: Recall
        type: recall
        value: 0.9375430144528561
      - name: F1
        type: f1
        value: 0.9340075419952005
      - name: Accuracy
        type: accuracy
        value: 0.9699674740348558
  ---
  
  <!-- 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. -->
  
  # mbert-finetuned-ner
  
  This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the wikiann dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1264
  - Precision: 0.9305
  - Recall: 0.9375
  - F1: 0.9340
  - Accuracy: 0.9700
  
  ## 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: 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: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.301         | 1.0   | 625  | 0.1756          | 0.8843    | 0.9067 | 0.8953 | 0.9500   |
  | 0.1259        | 2.0   | 1250 | 0.1248          | 0.9285    | 0.9335 | 0.9310 | 0.9688   |
  | 0.0895        | 3.0   | 1875 | 0.1264          | 0.9305    | 0.9375 | 0.9340 | 0.9700   |
  
  
  ### Framework versions
  
  - Transformers 4.19.4
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  