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# 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

{"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": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer dataset:cmotions/NL_restaurant_reviews autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-cmotions/NL_restaurant_reviews #autotrain_compatible #endpoints_compatible #region-us 

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



### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1



### 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 ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-cmotions/NL_restaurant_reviews #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-cmotions/NL_restaurant_reviews #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1