Instructions to use dunlp/GWW with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dunlp/GWW with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dunlp/GWW")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dunlp/GWW") model = AutoModelForMaskedLM.from_pretrained("dunlp/GWW", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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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 an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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### Framework versions
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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 an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.7144
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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| 4.1537 | 1.0 | 50 | 3.2586 |
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| 3.3257 | 2.0 | 100 | 3.0064 |
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| 3.1152 | 3.0 | 150 | 2.8890 |
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| 2.9988 | 4.0 | 200 | 2.8521 |
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| 2.9437 | 5.0 | 250 | 2.7453 |
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### Framework versions
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