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@@ -33,7 +33,7 @@ Some of the statistics of the corpus:
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  The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original [GPT-2](http://www.persagen.com/files/misc/radford2019language.pdf) model with a vocabulary size of 50,262 tokens. The GPT2-large-bne pre-training consists of an autoregressive language model training that follows the approach of the GPT-2. The training lasted a total of 10 days with 32 computing nodes each one with 4 NVIDIA V100 GPUs of 16GB VRAM.
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  ## Evaluation and results
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- For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish).
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  ## Citing
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  Check out our paper for all the details: https://arxiv.org/abs/2107.07253
 
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  The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original [GPT-2](http://www.persagen.com/files/misc/radford2019language.pdf) model with a vocabulary size of 50,262 tokens. The GPT2-large-bne pre-training consists of an autoregressive language model training that follows the approach of the GPT-2. The training lasted a total of 10 days with 32 computing nodes each one with 4 NVIDIA V100 GPUs of 16GB VRAM.
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  ## Evaluation and results
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+ For evaluation details visit our [GitHub repository](https://github.com/PlanTL-GOB-ES/lm-spanish).
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  ## Citing
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  Check out our paper for all the details: https://arxiv.org/abs/2107.07253