|
--- |
|
license: apache-2.0 |
|
language: |
|
- it |
|
--- |
|
-------------------------------------------------------------------------------------------------- |
|
|
|
<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: DistilUSE</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>Universal Sentence Encoder</b> <b>[1]</b> model for the <b>Italian</b> language, obtained using <b>mDistilUSE</b> ([distiluse-base-multilingual-cased-v1](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1)) 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 67M parameters, a vocabulary of 30.785 tokens, and a size of ~270 MB. |
|
|
|
It can be used to encode Italian texts and compute similarities between them. |
|
|
|
<h3>Quick usage</h3> |
|
|
|
```python |
|
from transformers import AutoTokenizer, AutoModel |
|
import numpy as np |
|
|
|
tokenizer = AutoTokenizer.from_pretrained("osiria/distiluse-base-italian") |
|
model = AutoModel.from_pretrained("osiria/distiluse-base-italian") |
|
|
|
text1 = "Alessandro Manzoni è stato uno scrittore italiano" |
|
text2 = "Giacomo Leopardi è stato un poeta italiano" |
|
|
|
vec1 = model(tokenizer.encode(text1, return_tensors = "pt")).last_hidden_state[0,0,:].cpu().detach().numpy() |
|
vec2 = model(tokenizer.encode(text2, return_tensors = "pt")).last_hidden_state[0,0,:].cpu().detach().numpy() |
|
|
|
cosine_similarity = np.dot(vec1, vec2)/(np.linalg.norm(vec1)*np.linalg.norm(vec2)) |
|
print("COSINE SIMILARITY:", cosine_similarity) |
|
|
|
# COSINE SIMILARITY: 0.734292 |
|
``` |
|
|
|
<h3>References</h3> |
|
|
|
[1] https://arxiv.org/abs/1907.04307 |
|
|
|
[2] https://arxiv.org/abs/2010.05609 |
|
|
|
<h3>License</h3> |
|
|
|
The model is released under <b>Apache-2.0</b> license |
|
|
