
--------------------------------------------------------------------------------------------------

<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>

--------------------------------------------------------------------------------------------------

<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](https://huggingface.co/Twitter/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>

```python
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


{"language": ["it"], "license": "apache-2.0", "widget": [{"text": "una fantastica [MASK] di #calcio! grande prestazione del mister e della squadra", "example_title": "Example 1"}, {"text": "il governo [MASK] dovrebbe fare politica, non soltanto propaganda! #vergogna", "example_title": "Example 2"}, {"text": "che serata da sogno sul #redcarpet! grazie a tutti gli attori e registi del [MASK] italiano #oscar #awards", "example_title": "Example 3"}]}
transformers pytorch safetensors bert fill-mask it arxiv:1810.04805 arxiv:2209.07562 arxiv:2010.05609 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #fill-mask #it #arxiv-1810.04805 #arxiv-2209.07562 #arxiv-2010.05609 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


--------------------------------------------------------------------------------------------------

<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>

--------------------------------------------------------------------------------------------------

<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>



Here you can find the find the model already fine-tuned on Sentiment Analysis: URL

<h3>References</h3>

[1] URL

[2] URL

[3] URL

<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


TAGS
#transformers #pytorch #safetensors #bert #fill-mask #it #arxiv-1810.04805 #arxiv-2209.07562 #arxiv-2010.05609 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
