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")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dunlp/GWW") model = AutoModelForMaskedLM.from_pretrained("dunlp/GWW") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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# GWW
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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
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It achieves the following results on the evaluation set:
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- Loss: 3.0275
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## Intended uses & limitations
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## Training and evaluation data
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# GWW
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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 Dutch civiel works dataset, which contains several contracts.
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It achieves the following results on the evaluation set:
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- Loss: 3.0275
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## Intended uses & limitations
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This model can be used for civiel works domain specific tasks.
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## Training and evaluation data
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