File size: 2,970 Bytes
f39fbe2 8b5edef f39fbe2 8b5edef 4346807 8b5edef |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
---
license: mit
language:
- it
widget:
- text: "Milano è una <mask> italiana"
example_title: "Example 1"
- text: "Leopardi è stato uno dei più grandi <mask> del classicismo italiano"
example_title: "Example 2"
- text: "L'Italia è uno <mask> dell'Unione Europea"
example_title: "Example 3"
---
--------------------------------------------------------------------------------------------------
<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 Large</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 Large</b> <b>[1]</b> model for the <b>Italian</b> language, obtained using <b>XLM-RoBERTa-Large</b> <b>[2]</b> ([xlm-roberta-large](https://huggingface.co/xlm-roberta-large)) 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 356M parameters, a vocabulary of 50.670 tokens, and a size of ~1.42 GB.
<h3>Quick usage</h3>
```python
from transformers import RobertaTokenizerFast, RobertaForMaskedLM
from transformers import pipeline
tokenizer = RobertaTokenizerFast.from_pretrained("osiria/roberta-large-italian")
model = RobertaForMaskedLM.from_pretrained("osiria/roberta-large-italian")
pipe = pipeline("fill-mask", model=model, tokenizer=tokenizer)
pipe("Milano è una <mask> italiana")
[{'score': 0.9284337759017944,
'token': 7786,
'token_str': 'città',
'sequence': 'Milano è una città italiana'},
{'score': 0.03296631574630737,
'token': 26960,
'token_str': 'capitale',
'sequence': 'Milano è una capitale italiana'},
{'score': 0.015821034088730812,
'token': 8043,
'token_str': 'provincia',
'sequence': 'Milano è una provincia italiana'},
{'score': 0.007335659582167864,
'token': 18841,
'token_str': 'regione',
'sequence': 'Milano è una regione italiana'},
{'score': 0.006183209829032421,
'token': 50152,
'token_str': 'cittadina',
'sequence': 'Milano è una cittadina italiana'}]
```
<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
|