metadata

tags:
  - generated_from_trainer
datasets: cmotions/NL_restaurant_reviews
metrics:
  - accuracy
  - recall
  - precision
  - f1
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
model-index:
  - name: NL_BERT_michelin_finetuned
    results: []

NL_BERT_michelin_finetuned

This model is a fine-tuned version of GroNLP/bert-base-dutch-cased on a Dutch restaurant reviews dataset. 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