Questions tagged [fasttext]
The fasttext tag has no usage guidance.
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How do I continue train fasttext pretrained model? [closed]
I use native fasttext library for message classification. I want to train the model on new data in case the dataset changes without using the old data, keeping the previous result. In my project user ...
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DBSCAN getting one huge cluster with noisy points
I'm currently trying to cluster customer service email answers (NLP).
When I use DBSCAN with TF-IDF embeddings + Annoy indexes, I get good clusters.
But, when I use DBSCAN with FastText embeddings + ...
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Should I use Pad Sequence when using Word Vectors?
I have an unbalanced text data set. I want to use word vectors to embed words. When I use pad sequence? Before or after the word vector? I tried it, after the word vector I used pad sequence but my ...
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Training fasttext on your own corpus
I want to train fasttext on my own corpus. However, I have a small question before continuing. Do I need each sentences as a different item in corpus or can I have many sentences as one item?
For ...
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How can I use Ensemble learning of two models with different features as an input?
I have a fake news detection problem and it predicts the binary labels "1"&"0" by vectorizing the 'tweet' column, I use three different models for detection but I want to use ...
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Gensim fast text get vocab or word index
Trying to use gensim's fasttext, testing the sample code from gensim with a small change of replacing the arguement to ...
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Data Set and guidance for Occupations/ Roles classification problem
I am working on a project where I need to find similar roles -- for example, Software Engineer, Soft. Engineer , Software Eng ( all should be marked similar)
Currently, I have tried using the Standard ...
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Genesis most_similar find synonym only (not antonyms)
Is there a way to let model.wv.most_similar in gensim return positive-meaning words only (i.e. that shows synonyms but not antonyms)?
For example, if I do:
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When are subword ngrams trained in fasttext? (Enriching Word Vectors with Subword Information)
when is the training for subword ngrams done? is it done simultaneously as when the word representation are trained? or is it done at the end, after word representations are created?
fasttext ...
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Pre-trained models for finding similar word n-grams
Are there any pre-trained models for finding similar word n-grams, where n>1?
FastText, for instance, seems to work only on unigrams:
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Initializing weights that are a pointwise product of multiple variables
In two-layer perceptrons that slide across words of text, such as word2vec and fastText, hidden layer heights may be a product of two random variables such as positional embeddings and word embeddings ...
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Keep word2vexc/fasttext model loaded in memory without using API
I have to use Fasttext model to return word embeddings. In test I was calling it through API. Since there are too many words to compute embeddings, API call seems to be expensive. I would like to use ...
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Explain FastText model using SHAP values
I have trained fastText model and some fully connected network build on its embeddings. I figured out how to use Lime on it: complete example can be found in Natural Language Processing Is Fun Part 3: ...
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Extracting vectors of FastText own model to use it on a NN
I have trained my own model of fasttext using the pretrained model of English available on their website with the next code:
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FastText Model Explained
I was reading the FastText paper and I have a few questions about the model used for classification. Since I am not from NLP background, some I am unfamiliar with the jargon.
In the figure, what ...
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Removing duplicate records before training
I am currently working on a project classifying text into classes. The specific problem is classifying job titles into various industry codes. For example "McDonalds Employee" might get classified to ...
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