Downloads · 30 days
0
word2vec-light-uncased-it
osiria/word2vec-light-uncased-it
word2vec-light-uncased-it is a machine learning model from osiria. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
--------------------------------------------------------------------------------------------------
Access
Public
Updated May 7, 2023
Repo size
245 MB
Likes
4
Public
Repo makeup
Click a slice to open those files.
.npy224 MB · 92%
At a glance
- License
- apache-2.0
- Access
- Public
- Created
- May 7, 2023
- Updated
- May 7, 2023
- SHA
- 295f5178
Datasets
Model card
From the Hugging Face model README
<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: Word2Vec</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 model is a <b>lightweight</b> and uncased version of <b>Word2Vec</b> <b>[1]</b> for the <b>italian</b> language. It's implemented in Gensim and it provides embeddings for 560.509 uncased italian words in a 100-dimensional vector space, resulting in a total model size of about 245 MB.
<h3>Training procedure</h3>The model was trained on the italian split of the Wikipedia dataset (about 3.7GB, lowercased and pre-processed) for 10 epochs, using a window size of 5 and including words with a minimum count of 10, with an initial learning rate of 2.5e-3
<h3>Quick usage</h3>Download the files in a local folder called "word2vec-light-uncased-it", then run:
from gensim.models import KeyedVectors
model = KeyedVectors.load("./word2vec-light-uncased-it/word2vec.wordvectors", mmap='r')
model.most_similar("poesia", topn=5)
Expected output:
[('letteratura', 0.8193784356117249),
('poetica', 0.8115736246109009),
('narrativa', 0.7729100584983826),
('drammaturgia', 0.7576397061347961),
('prosa', 0.7552034854888916)]
This lightweight model is trained on Wikipedia, so it's particularly suitable for natively digital text from the world wide web, written in a correct and fluent form (like wikis, web pages, news, etc.).
However, it may show limitations when it comes to chaotic text, containing errors and slang expressions (like social media posts) or when it comes to domain-specific text (like medical, financial or legal content).
<h3>References</h3>[1] https://arxiv.org/abs/1301.3781
<h3>License</h3>The model is released under <b>Apache-2.0</b> license
- License
- apache-2.0
- Languages
- it
- Created
- May 7, 2023
- Updated
- May 7, 2023