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  développement, applicable à l'ensemble de son territoire.
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  ---
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- # Legal-CamemBERT
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- * Legal-CamemBERT is a [CamemBERT](https://huggingface.co/camembert-base)-based model further pre-trained on [23,000+ statutory articles](https://huggingface.co/datasets/maastrichtlawtech/bsard) from the Belgian legislation.
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  * We chose the following training set-up: 50k training steps (200 epochs) with batches of 32 sequences of length 512 with an initial learning rate of 5e-5.
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  * Training was performed on one Tesla V100 GPU with 32 GB using the [code](https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py) provided by Hugging Face.
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  ```python
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  from transformers import AutoTokenizer, AutoModel
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- tokenizer = AutoTokenizer.from_pretrained("maastrichtlawtech/legal-camembert")
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- model = AutoModel.from_pretrained("maastrichtlawtech/legal-camembert")
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  ```
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  ### About Us
 
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  développement, applicable à l'ensemble de son territoire.
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  ---
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+ # Legal-CamemBERT-Base
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+ * Legal-CamemBERT-Base is a [CamemBERT-Base](https://huggingface.co/camembert-base) model further pre-trained on [23,000+ legislative articles](https://huggingface.co/datasets/maastrichtlawtech/bsard) from the Belgian legislation.
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  * We chose the following training set-up: 50k training steps (200 epochs) with batches of 32 sequences of length 512 with an initial learning rate of 5e-5.
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  * Training was performed on one Tesla V100 GPU with 32 GB using the [code](https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py) provided by Hugging Face.
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  ```python
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  from transformers import AutoTokenizer, AutoModel
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+ tokenizer = AutoTokenizer.from_pretrained("maastrichtlawtech/legal-camembert-base")
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+ model = AutoModel.from_pretrained("maastrichtlawtech/legal-camembert-base")
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  ```
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  ### About Us