readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - recall
  - precision
  - f1
  model-index:
  - name: NL_BERT_michelin_finetuned
    results: []
  widget:
  - text: "Wat een geweldige ervaring. Wij gebruikte de lunch bij de Librije. 10 gangen met in overleg hierbij gekozen wijnen. Alles klopt. De aandacht, de timing, prachtige gerechtjes. En wat een smaaksensaties! Bediening met humor. Altijd daar wanneer je ze nodig hebt, maar nooit overdreven aanwezig."
    example_title: "Michelin restaurant"  
  - text: "Mooie locatie, aardige medewerkers. Maaltijdsalade helaas teleurstellend, zeer kleine portie voor 13,80. Jammer."
    example_title: "Mooie locatie, matig eten"  
  ---
  
  <!-- 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. -->
  
  # NL_BERT_michelin_finetuned
  
  This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on a [Dutch restaurant reviews dataset](https://huggingface.co/datasets/cmotions/NL_restaurant_reviews). Provide Dutch review text to the API on the right and receive a score that indicates whether this restaurant is eligible for a Michelin star ;)
  It achieves the following results on the evaluation set:
  - Loss: 0.0637
  - Accuracy: 0.9836
  - Recall: 0.5486
  - Precision: 0.7914
  - F1: 0.6480
  - Mse: 0.0164
  
  ## 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: 5e-05
  - train_batch_size: 32
  - eval_batch_size: 128
  - 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 | Accuracy | Recall | Precision | F1     | Mse    |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
  | 0.1043        | 1.0   | 3647  | 0.0961          | 0.9792   | 0.3566 | 0.7606    | 0.4856 | 0.0208 |
  | 0.0799        | 2.0   | 7294  | 0.0797          | 0.9803   | 0.4364 | 0.7415    | 0.5495 | 0.0197 |
  | 0.0589        | 3.0   | 10941 | 0.0637          | 0.9836   | 0.5486 | 0.7914    | 0.6480 | 0.0164 |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0+cu113
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  