BertjeWDialDataALLQonly – AI Model by Jeska | AlphaNeural AI

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BertjeWDialDataALLQonly

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BertjeWDialDataALLQonly

This model is a fine-tuned version of GroNLP/bert-base-dutch-cased on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.9438

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: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15.0

Training results

Training Loss	Epoch	Step	Validation Loss
2.2122	1.0	871	2.0469
2.0961	2.0	1742	2.0117
2.0628	3.0	2613	2.0040
2.0173	4.0	3484	1.9901
1.9772	5.0	4355	1.9711
1.9455	6.0	5226	1.9785
1.917	7.0	6097	1.9380
1.8933	8.0	6968	1.9651
1.8708	9.0	7839	1.9915
1.862	10.0	8710	1.9310
1.8545	11.0	9581	1.9422
1.8231	12.0	10452	1.9310
1.8141	13.0	11323	1.9362
1.7939	14.0	12194	1.9334
1.8035	15.0	13065	1.9197

Framework versions

Transformers 4.13.0.dev0
Pytorch 1.10.0
Datasets 1.16.1
Tokenizers 0.10.3