bert-tweet-base-italian-uncased

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<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: BERT-TWEET</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>

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<h3>Model description</h3>

This is a <b>BERT</b> <b>[1]</b> uncased model for the <b>Italian</b> language, obtained using <b>TwHIN-BERT</b> <b>[2]</b> (twhin-bert-base) as a starting point and focusing it on the Italian language by modifying the embedding layer (as in <b>[3]</b>, computing document-level frequencies over the <b>Wikipedia</b> dataset)

The resulting model has 110M parameters, a vocabulary of 30.520 tokens, and a size of ~440 MB.

<h3>Quick usage</h3>

from transformers import BertTokenizerFast, BertModel

tokenizer = BertTokenizerFast.from_pretrained("osiria/bert-tweet-base-italian-uncased")
model = BertModel.from_pretrained("osiria/bert-tweet-base-italian-uncased")

Here you can find the find the model already fine-tuned on Sentiment Analysis: https://huggingface.co/osiria/bert-tweet-italian-uncased-sentiment

<h3>References</h3>

[1] https://arxiv.org/abs/1810.04805

[2] https://arxiv.org/abs/2209.07562

[3] https://arxiv.org/abs/2010.05609

<h3>Limitations</h3>

This model was trained on tweets, so it's mainly suitable for general-purpose social media text processing, involving short texts written in a social network style. It might show limitations when it comes to longer and more structured text, or domain-specific text.

<h3>License</h3>

The model is released under <b>Apache-2.0</b> license