readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - wikiann
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  language:
  - sk
  inference: false
  model-index:
  - name: fernet-sk-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: wikiann sk
        type: wikiann
        args: sk
      metrics:
      - name: Precision
        type: precision
        value: 0.9359821760118826
      - name: Recall
        type: recall
        value: 0.9472378804960541
      - name: F1
        type: f1
        value: 0.9415763914830033
      - name: Accuracy
        type: accuracy
        value: 0.9789063466534702
  ---
  
  # Named Entity Recognition based on FERNET-CC_sk
  
  This model is a fine-tuned version of [fav-kky/FERNET-CC_sk](https://huggingface.co/fav-kky/FERNET-CC_sk) on the Slovak wikiann dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1763
  - Precision: 0.9360
  - Recall: 0.9472
  - F1: 0.9416
  - Accuracy: 0.9789
  
  ## Intended uses & limitation
  Supported classes: LOCATION, PERSON, ORGANIZATION
  
  ```
  from transformers import pipeline
  
  ner_pipeline = pipeline(task='ner', model='crabz/slovakbert-ner')
  input_sentence = "Minister financií a líder mandátovo najsilnejšieho hnutia OĽaNO Igor Matovič upozorňuje, že následky tretej vlny budú na Slovensku veľmi veľké."
  classifications = ner_pipeline(input_sentence)
  ``` 
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 24
  - eval_batch_size: 24
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10.0
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.1259        | 1.0   | 834  | 0.1095          | 0.8963    | 0.9182 | 0.9071 | 0.9697   |
  | 0.071         | 2.0   | 1668 | 0.0974          | 0.9270    | 0.9357 | 0.9313 | 0.9762   |
  | 0.0323        | 3.0   | 2502 | 0.1259          | 0.9257    | 0.9330 | 0.9293 | 0.9745   |
  | 0.0175        | 4.0   | 3336 | 0.1347          | 0.9241    | 0.9360 | 0.9300 | 0.9756   |
  | 0.0156        | 5.0   | 4170 | 0.1407          | 0.9337    | 0.9404 | 0.9370 | 0.9780   |
  | 0.0062        | 6.0   | 5004 | 0.1522          | 0.9267    | 0.9410 | 0.9338 | 0.9774   |
  | 0.0055        | 7.0   | 5838 | 0.1559          | 0.9322    | 0.9429 | 0.9375 | 0.9780   |
  | 0.0024        | 8.0   | 6672 | 0.1733          | 0.9321    | 0.9438 | 0.9379 | 0.9779   |
  | 0.0009        | 9.0   | 7506 | 0.1765          | 0.9347    | 0.9468 | 0.9407 | 0.9784   |
  | 0.0002        | 10.0  | 8340 | 0.1763          | 0.9360    | 0.9472 | 0.9416 | 0.9789   |
  
  
  ### Framework versions
  
  - Transformers 4.14.0.dev0
  - Pytorch 1.10.0
  - Datasets 1.16.1
  - Tokenizers 0.10.3
  