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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: RoBERTa</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>RoBERTa</b> <b>[1]</b> model for the <b>Italian</b> language, obtained using <b>XLM-RoBERTa</b> <b>[2]</b> ([xlm-roberta-base](https://huggingface.co/xlm-roberta-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 125M parameters, a vocabulary of 50.670 tokens, and a size of ~500 MB.

<h3>Quick usage</h3>

```python
from transformers import RobertaTokenizerFast, RobertaModel

tokenizer = RobertaTokenizerFast.from_pretrained("osiria/roberta-base-italian")
model = RobertaModel.from_pretrained("osiria/roberta-base-italian")
```

<h3>References</h3>

[1] https://arxiv.org/abs/1907.11692

[2] https://arxiv.org/abs/1911.02116

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

<h3>License</h3>

The model is released under <b>MIT</b> license

{"language": ["it"], "license": "mit", "widget": [{"text": "Milano \u00e8 una <mask> italiana", "example_title": "Example 1"}, {"text": "Leopardi \u00e8 stato uno dei pi\u00f9 grandi <mask> del classicismo italiano", "example_title": "Example 2"}, {"text": "L'Italia \u00e8 uno <mask> dell'Unione Europea", "example_title": "Example 3"}]}
transformers pytorch safetensors roberta fill-mask it arxiv:1907.11692 arxiv:1911.02116 arxiv:2010.05609 license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #it #arxiv-1907.11692 #arxiv-1911.02116 #arxiv-2010.05609 #license-mit #autotrain_compatible #endpoints_compatible #region-us 

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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: RoBERTa</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>RoBERTa</b> <b>[1]</b> model for the <b>Italian</b> language, obtained using <b>XLM-RoBERTa</b> <b>[2]</b> (xlm-roberta-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 125M parameters, a vocabulary of 50.670 tokens, and a size of ~500 MB.

<h3>Quick usage</h3>



<h3>References</h3>

[1] URL

[2] URL

[3] URL

<h3>License</h3>

The model is released under <b>MIT</b> license

TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #it #arxiv-1907.11692 #arxiv-1911.02116 #arxiv-2010.05609 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
