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A Computing Infrastructure
We run the experiments on two machines: the first one is equipped with two NVIDIA RTX 2080TI and has 64GB of RAM. The other one is equipped with four GPUs, NVIDIA GTX 1080TI, and has 32GB of RAM.
Interested readers can replicate our experiments by using the code we release online at https://github.com/MilaNLProc/honest.
In our experiments, we consider state-of-the-art BERT and GPT-2 models available in the HuggingFace repository (Wolf et al., 2020). Whenever possible we use the uncased version.
For the completion of the language-specific BERT and GPT-3 models we make use of the code API exposed by the HuggingFace team.5
The two following lists report the models we have considered in this paper. Here we list the language-specific BERT models:
- Italian BERT XXL
5https://huggingface.co/transformers/ main_classeses/pipelines.html
https://huggingface.co/dbmdz/bert-base-italian-xxl-uncased
GilBERTo
- UmBERTo8
- BERT (Devlin et al., 2019)
RoBERTa (Liu et al., 2019)
- DistilBERT (Sanh et al., 2019)
- French
- CamemBERT (Martin et al., 2020)
- FlauBERT (Le et al., 2020)
- Spanish
- BETO (Canete et al., 2020)
- Portuguese
- BERTimbau
- Romanian
- RomanianBERT<sup>10</sup>
And this is the list of the language-specific GPT-2 models:
- GPT-2 (IT): GePpeTto (De Mattei et al., 2020)
- GPT-2 (EN): GPT-2 (Radford et al., 2019)
GPT-2 (FR): BelGPT-2 $^{11}$
- GPT-2 (PT): GPorTuguese<sup>12</sup>