emoji2vec | Example code - use word embeddings | Icon library
kandi X-RAY | emoji2vec Summary
kandi X-RAY | emoji2vec Summary
A demo project to play word embedding and emoji with twitter data. Let's see how we can make a smarter emoji predictor. Believe it or not, we have 1800+ emoji from standard Unicode. How to find one from them? Traditionally, we search key words of emoji description. We will use word embedding to find the best match with a context. The results reflects real users habit from social media. Now you are guided with most knowledgeable emoji master :).
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Top functions reviewed by kandi - BETA
- Compress word2vec
- Compute k - means clustering
- Check if the word is an emoji
- Draw matplotlib matplotlib
- Returns a tuple containing the words and their emoji
- Find the most similar emoji words
- Predict most similar entities
- Predict the most similar similarity
- Returns the indices of the most similar elements in w t
- Train a word2vec model
- Predict the most similar words
- Draws the clusters in the dataset
- Test the similarity between two words
emoji2vec Key Features
emoji2vec Examples and Code Snippets
Community Discussions
Trending Discussions on emoji2vec
QUESTION
We have a flask
application where we need to load a pretrained model located at the path '/root/apps/mlapi/resources/emoji2vec.bin'
using gensim
. While running the code I am getting below error
ANSWER
Answered 2019-Dec-28 at 18:24You are running into a File Permission error. This means the unix user that is attempting to access your file does not have the required permissions to do so.
It appears you are storing your pretrained model file at
/root/apps/mlapi/resources/emoji2vec.bin
This looks like a location that requires root or sudo privileges to access. To verify this run
QUESTION
I successfully followed deeplearning4j.org tutorial on Word2Vec, so I am able to load already trained model or train a new one based on some raw text (more specifically, I am using GoogleNews-vectors-negative300
and Emoji2Vec
pre-trained model).
However, I would like to combine these two above models for the following reason: Having a sentence (for example, a comment from Instagram or Twitter, which consists of emoji), I want to identify the emoji in the sentence and then map it to the word it is related to. In order to do that, I was planning to iterate over all the words in the sentence and calculate the closeness (how near the emoji and the word are located in the vector space).
I found the code how to uptrain the already existing model. However, it is mentioned that new words are not added in this case and only weights for the existing words will be updated based on a new text corpus.
I would appreciate any help or ideas on the problem I have. Thanks in advance!
...ANSWER
Answered 2018-Mar-24 at 18:15Combining two models trained from different corpuses is not a simple, supported operation in the word2vec libraries with which I'm most familiar.
In particular, even if the same word appears in both corpuses, and even in similar contexts, the randomization that's used by this algorithm during initialization and training, and extra randomization injected by multithreaded training, mean that word may appear in wildly different places. It's only the relative distances/orientation with respect to other words that should be roughly similar – not the specific coordinates/rotations.
So to merge two models requires translating one's coordinates to the other. That in itself will typically involve learning-a-projection from one space to the other, then moving unique words from a source space to the surviving space. I don't know if DL4J has a built-in routine for this; the Python gensim
library has a TranslationMatrix
example class in recent versions which can do this, as motivated by the use of word-vectors for language-to-language translations.
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Install emoji2vec
You can use emoji2vec like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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