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--- |
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tags: |
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- generated_from_trainer |
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datasets: cmotions/NL_restaurant_reviews |
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metrics: |
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- accuracy |
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- recall |
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- precision |
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- f1 |
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widget: |
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- text: Wat een geweldige ervaring. Wij gebruikte de lunch bij de Librije. 10 gangen |
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met in overleg hierbij gekozen wijnen. Alles klopt. De aandacht, de timing, prachtige |
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gerechtjes. En wat een smaaksensaties! Bediening met humor. Altijd daar wanneer |
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je ze nodig hebt, maar nooit overdreven aanwezig. |
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example_title: Michelin restaurant |
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- text: Mooie locatie, aardige medewerkers. Maaltijdsalade helaas teleurstellend, |
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zeer kleine portie voor 13,80. Jammer. |
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example_title: Mooie locatie, matig eten |
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base_model: GroNLP/bert-base-dutch-cased |
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model-index: |
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- name: NL_BERT_michelin_finetuned |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# NL_BERT_michelin_finetuned |
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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 ;) |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0637 |
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- Accuracy: 0.9836 |
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- Recall: 0.5486 |
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- Precision: 0.7914 |
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- F1: 0.6480 |
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- Mse: 0.0164 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 128 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 | Mse | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:| |
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| 0.1043 | 1.0 | 3647 | 0.0961 | 0.9792 | 0.3566 | 0.7606 | 0.4856 | 0.0208 | |
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| 0.0799 | 2.0 | 7294 | 0.0797 | 0.9803 | 0.4364 | 0.7415 | 0.5495 | 0.0197 | |
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| 0.0589 | 3.0 | 10941 | 0.0637 | 0.9836 | 0.5486 | 0.7914 | 0.6480 | 0.0164 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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