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Question: spacy Arabic word2vector
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spacy Arabic word2vector

Yes, spaCy does offer pre-trained word vectors for Arabic as part of its language models. The default Arabic language model in spaCy is called “ar_core_news_md”, and it includes pre-trained word vectors that were trained on a large corpus of Arabic text.

To use the pre-trained word vectors, you can load the Arabic language model in spaCy and then access the word vectors for individual tokens in a text. Here’s an example:

makefile
import spacy

nlp = spacy.load("ar_core_news_md")

doc = nlp("أهلاً بالعالم")

# Access the vector for the first token in the text
vector = doc[0].vector

print(vector)

This will output a 300-dimensional vector that represents the word “أهلاً”. You can access the word vectors for other tokens in the text by indexing into the doc object with different integer values.

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Github - Bakrianoo/Aravec: Aravec Is A Pre-Trained Distributed Word Representation (Word Embedding) Open Source Project Which Aims To Provide The Arabic Nlp Research Community With Free To Use And Powerful Word Embedding — Github – Bakrianoo/Aravec: Aravec Is A Pre-Trained Distributed Word Representation (Word Embedding) Open Source Project Which Aims To Provide The Arabic Nlp Research Community With Free To Use And Powerful Word Embedding

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