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# VaccinChatSentenceClassifierDutch_fromBERTjeDIAL

This model is a fine-tuned version of [Jeska/BertjeWDialDataQA20k](https://huggingface.co/Jeska/BertjeWDialDataQA20k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8355
- Accuracy: 0.6322

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.4418        | 1.0   | 1457 | 2.3866          | 0.5406   |
| 1.7742        | 2.0   | 2914 | 1.9365          | 0.6069   |
| 1.1313        | 3.0   | 4371 | 1.8355          | 0.6322   |


### Framework versions

- Transformers 4.13.0.dev0
- Pytorch 1.10.0
- Datasets 1.16.1
- Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "VaccinChatSentenceClassifierDutch_fromBERTjeDIAL", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

VaccinChatSentenceClassifierDutch\_fromBERTjeDIAL
=================================================


This model is a fine-tuned version of Jeska/BertjeWDialDataQA20k on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 1.8355
* Accuracy: 0.6322


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: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0


### Training results



### Framework versions


* Transformers 4.13.0.dev0
* Pytorch 1.10.0
* Datasets 1.16.1
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0 ### Training results ### Framework versions


* Transformers 4.13.0.dev0
* Pytorch 1.10.0
* Datasets 1.16.1
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0 ### Training results ### Framework versions


* Transformers 4.13.0.dev0
* Pytorch 1.10.0
* Datasets 1.16.1
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0### Training results### Framework versions


* Transformers 4.13.0.dev0
* Pytorch 1.10.0
* Datasets 1.16.1
* Tokenizers 0.10.3