<body> <span class="vertical-text" style="background-color:lightgreen;border-radius: 3px;padding: 3px;"> </span> <br> <span class="vertical-text" style="background-color:orange;border-radius: 3px;padding: 3px;"> </span> <br> <span class="vertical-text" style="background-color:lightblue;border-radius: 3px;padding: 3px;"> Model: DeBERTa</span> <br> <span class="vertical-text" style="background-color:tomato;border-radius: 3px;padding: 3px;"> Lang: IT</span> <br> <span class="vertical-text" style="background-color:lightgrey;border-radius: 3px;padding: 3px;"> </span> <br> <span class="vertical-text" style="background-color:#CF9FFF;border-radius: 3px;padding: 3px;"> </span> </body>
<h3>Model description</h3>
This is a <b>DeBERTa</b> <b>[1]</b> model for the <b>Italian</b> language, obtained using <b>mDeBERTa</b> (mdeberta-v3-base) as a starting point and focusing it on the Italian language by modifying the embedding layer (as in <b>[2]</b>, computing document-level frequencies over the <b>Wikipedia</b> dataset)
The resulting model has 124M parameters, a vocabulary of 50.256 tokens, and a size of ~500 MB.
<h3>Quick usage</h3>
from transformers import DebertaV2TokenizerFast, DebertaV2Model
tokenizer = DebertaV2TokenizerFast.from_pretrained("osiria/deberta-base-italian")
model = DebertaV2Model.from_pretrained("osiria/deberta-base-italian")
<h3>References</h3>
[1] https://arxiv.org/abs/2111.09543
[2] https://arxiv.org/abs/2010.05609
<h3>License</h3>
The model is released under <b>MIT</b> license
