Instructions to use Jeska/BertjeWDialData with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeska/BertjeWDialData with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Jeska/BertjeWDialData")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Jeska/BertjeWDialData") model = AutoModelForMaskedLM.from_pretrained("Jeska/BertjeWDialData") - Notebooks
- Google Colab
- Kaggle
BertjeWDialData
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: 2.2608
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: 1.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 297 | 2.2419 |
Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
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