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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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<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: BERT</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;"> Type: Uncased</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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<h3>Model description</h3>
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This is an uncased <b>BERT</b> <b>[1]</b> model for the <b>Italian</b> language, obtained using the uncased <b>mBERT</b> ([bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased)) 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 110M parameters, a vocabulary of 30.785 tokens, and a size of ~430 MB.
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<h3>Quick usage</h3>
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```python
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from transformers import BertTokenizerFast, BertModel
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tokenizer = BertTokenizerFast.from_pretrained("osiria/bert-base-italian-uncased")
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model = BertModel.from_pretrained("osiria/bert-base-italian-uncased")
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```
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<h3>References</h3>
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[1] https://arxiv.org/abs/1810.04805
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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
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