readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-base-multilingual-cased-finetuned-multilingual-ner
    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-base-multilingual-cased-finetuned-multilingual-ner
  
  This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2352
  - Precision: 0.8109
  - Recall: 0.8332
  - F1: 0.8219
  - Accuracy: 0.9264
  
  ## 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: 32
  - eval_batch_size: 32
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.7301        | 0.16  | 100  | 0.3827          | 0.6189    | 0.7009 | 0.6573 | 0.8734   |
  | 0.3841        | 0.32  | 200  | 0.3195          | 0.7057    | 0.7511 | 0.7277 | 0.8922   |
  | 0.3451        | 0.48  | 300  | 0.2862          | 0.7094    | 0.7750 | 0.7407 | 0.8952   |
  | 0.3187        | 0.65  | 400  | 0.2735          | 0.7372    | 0.7802 | 0.7581 | 0.9019   |
  | 0.3058        | 0.81  | 500  | 0.2533          | 0.7536    | 0.8015 | 0.7768 | 0.9052   |
  | 0.2918        | 0.97  | 600  | 0.2458          | 0.7587    | 0.8085 | 0.7828 | 0.9126   |
  | 0.2425        | 1.13  | 700  | 0.2379          | 0.7742    | 0.7976 | 0.7857 | 0.9150   |
  | 0.2387        | 1.29  | 800  | 0.2300          | 0.7772    | 0.8108 | 0.7936 | 0.9165   |
  | 0.2125        | 1.45  | 900  | 0.2387          | 0.7900    | 0.8130 | 0.8014 | 0.9180   |
  | 0.2026        | 1.62  | 1000 | 0.2317          | 0.7877    | 0.8152 | 0.8012 | 0.9186   |
  | 0.1963        | 1.78  | 1100 | 0.2326          | 0.7842    | 0.8269 | 0.8049 | 0.9220   |
  | 0.2052        | 1.94  | 1200 | 0.2247          | 0.7924    | 0.8234 | 0.8076 | 0.9212   |
  | 0.1868        | 2.1   | 1300 | 0.2410          | 0.7903    | 0.8282 | 0.8088 | 0.9204   |
  | 0.1556        | 2.26  | 1400 | 0.2428          | 0.8064    | 0.8317 | 0.8189 | 0.9256   |
  | 0.153         | 2.42  | 1500 | 0.2316          | 0.8017    | 0.8282 | 0.8147 | 0.9238   |
  | 0.1484        | 2.58  | 1600 | 0.2379          | 0.8054    | 0.8338 | 0.8194 | 0.9258   |
  | 0.137         | 2.75  | 1700 | 0.2331          | 0.8101    | 0.8324 | 0.8211 | 0.9270   |
  | 0.1638        | 2.91  | 1800 | 0.2352          | 0.8109    | 0.8332 | 0.8219 | 0.9264   |
  
  
  ### Framework versions
  
  - Transformers 4.21.0
  - Pytorch 1.12.0+cu102
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  