readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - wikiann
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-base-multilingual-cased-finetuned-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: wikiann
        type: wikiann
        config: en
        split: train
        args: en
      metrics:
      - name: Precision
        type: precision
        value: 0.8326536254925002
      - name: Recall
        type: recall
        value: 0.8515481408171921
      - name: F1
        type: f1
        value: 0.8419948974242476
      - name: Accuracy
        type: accuracy
        value: 0.934650342703884
  ---
  
  <!-- 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-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: 0.2299
  - Precision: 0.8327
  - Recall: 0.8515
  - F1: 0.8420
  - Accuracy: 0.9347
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.6617        | 0.16  | 100  | 0.3490          | 0.7259    | 0.7730 | 0.7487 | 0.8983   |
  | 0.341         | 0.32  | 200  | 0.2942          | 0.7665    | 0.8052 | 0.7854 | 0.9121   |
  | 0.3052        | 0.48  | 300  | 0.2821          | 0.7694    | 0.8021 | 0.7854 | 0.9152   |
  | 0.2938        | 0.64  | 400  | 0.2700          | 0.7897    | 0.8122 | 0.8008 | 0.9206   |
  | 0.2685        | 0.8   | 500  | 0.2482          | 0.7901    | 0.8253 | 0.8073 | 0.9242   |
  | 0.2622        | 0.96  | 600  | 0.2478          | 0.7989    | 0.8298 | 0.8141 | 0.9250   |
  | 0.2154        | 1.12  | 700  | 0.2456          | 0.8126    | 0.8365 | 0.8244 | 0.9273   |
  | 0.2046        | 1.28  | 800  | 0.2429          | 0.8079    | 0.8335 | 0.8205 | 0.9270   |
  | 0.2114        | 1.44  | 900  | 0.2377          | 0.8125    | 0.8415 | 0.8268 | 0.9300   |
  | 0.2111        | 1.6   | 1000 | 0.2381          | 0.8231    | 0.8397 | 0.8313 | 0.9309   |
  | 0.1934        | 1.76  | 1100 | 0.2349          | 0.8179    | 0.8485 | 0.8329 | 0.9308   |
  | 0.1972        | 1.92  | 1200 | 0.2293          | 0.8287    | 0.8446 | 0.8366 | 0.9332   |
  | 0.1858        | 2.08  | 1300 | 0.2366          | 0.8280    | 0.8463 | 0.8371 | 0.9327   |
  | 0.1506        | 2.24  | 1400 | 0.2392          | 0.8255    | 0.8505 | 0.8378 | 0.9332   |
  | 0.1508        | 2.4   | 1500 | 0.2346          | 0.8266    | 0.8465 | 0.8364 | 0.9334   |
  | 0.1674        | 2.56  | 1600 | 0.2329          | 0.8249    | 0.8487 | 0.8366 | 0.9329   |
  | 0.1584        | 2.72  | 1700 | 0.2309          | 0.8316    | 0.8508 | 0.8411 | 0.9341   |
  | 0.154         | 2.88  | 1800 | 0.2299          | 0.8327    | 0.8515 | 0.8420 | 0.9347   |
  
  
  ### Framework versions
  
  - Transformers 4.21.0
  - Pytorch 1.12.0+cu102
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  