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  license: apache-2.0
 
 
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  license: apache-2.0
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+ language:
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+ - it
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  ---
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+ --------------------------------------------------------------------------------------------------
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+
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+ <body>
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+ <span class="vertical-text" style="background-color:lightgreen;border-radius: 3px;padding: 3px;"> </span>
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+ <br>
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+ <span class="vertical-text" style="background-color:orange;border-radius: 3px;padding: 3px;">  </span>
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+ <br>
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+ <span class="vertical-text" style="background-color:lightblue;border-radius: 3px;padding: 3px;">    Model: DistilBERT</span>
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+ <br>
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+ <span class="vertical-text" style="background-color:tomato;border-radius: 3px;padding: 3px;">    Lang: IT</span>
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+ <br>
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+ <span class="vertical-text" style="background-color:lightgrey;border-radius: 3px;padding: 3px;">  </span>
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+ <br>
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+ <span class="vertical-text" style="background-color:#CF9FFF;border-radius: 3px;padding: 3px;"> </span>
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+ </body>
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+
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+ --------------------------------------------------------------------------------------------------
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+ <h3>Model description</h3>
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+ This is a <b>DistilBERT</b> <b>[1]</b> model for the <b>Italian</b> language, obtained using the multilingual <b>DistilBERT</b> ([distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased)) as a starting point and focusing it on the Italian language by modifying the embedding layer
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+ (as in <b>[2]</b>, computing document-level frequencies over the <b>Wikipedia</b> dataset)
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+ The resulting model has 67M parameters, a vocabulary of 30.785 tokens, and a size of ~270 MB.
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+ <h3>Quick usage</h3>
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+ ```python
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+ from transformers import BertTokenizerFast, DistilBertModel
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+ tokenizer = DistilBertTokenizerFast.from_pretrained("osiria/distilbert-base-italian-cased")
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+ model = DistilBertModel.from_pretrained("osiria/distilbert-base-italian-cased")
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+ ```
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+ <h3>References</h3>
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+ [1] https://arxiv.org/abs/1910.01108
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+ [2] https://arxiv.org/abs/2010.05609
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+ <h3>License</h3>
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+ The model is released under <b>Apache-2.0</b> license