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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: DeBERTa</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>DeBERTa</b> <b>[1]</b> model for the <b>Italian</b> language, obtained using <b>mDeBERTa</b> ([mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base)) as a starting point and focusing it on the Italian language by modifying the embedding layer 
(as in <b>[2]</b>, computing document-level frequencies over the <b>Wikipedia</b> dataset)

The resulting model has 124M parameters, a vocabulary of 50.256 tokens, and a size of ~500 MB.

<h3>Quick usage</h3>

```python
from transformers import DebertaV2TokenizerFast, DebertaV2Model

tokenizer = DebertaV2TokenizerFast.from_pretrained("osiria/deberta-base-italian")
model = DebertaV2Model.from_pretrained("osiria/deberta-base-italian")
```

<h3>References</h3>

[1] https://arxiv.org/abs/2111.09543

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

<h3>License</h3>

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

{"language": ["it"], "license": "mit"}
transformers pytorch safetensors deberta-v2 feature-extraction it arxiv:2111.09543 arxiv:2010.05609 license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #deberta-v2 #feature-extraction #it #arxiv-2111.09543 #arxiv-2010.05609 #license-mit #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: DeBERTa</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>DeBERTa</b> <b>[1]</b> model for the <b>Italian</b> language, obtained using <b>mDeBERTa</b> (mdeberta-v3-base) as a starting point and focusing it on the Italian language by modifying the embedding layer 
(as in <b>[2]</b>, computing document-level frequencies over the <b>Wikipedia</b> dataset)

The resulting model has 124M parameters, a vocabulary of 50.256 tokens, and a size of ~500 MB.

<h3>Quick usage</h3>



<h3>References</h3>

[1] URL

[2] URL

<h3>License</h3>

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

TAGS
#transformers #pytorch #safetensors #deberta-v2 #feature-extraction #it #arxiv-2111.09543 #arxiv-2010.05609 #license-mit #endpoints_compatible #region-us 
