I am using the SentenceTransformers library (here: https://pypi.org/project/sentence-transformers/#pretrained-models) for creating embeddings of sentences using the pre-trained model bert-base-nli-...

Does Embedding make similar words closer to each other? And do I just need to give to it all the sentences? Or it is just a lookup table and I need to code the model?

In the dataset I have 5000000 rows, I would like to add a column called 'embeddings' to my dataset. dataset = dataset.add_column('embeddings', embeddings) The variable embeddings is a numpy memmap ...

I trained my unsupervised model using fasttext.train_unsupervised() function in python. I want to save it as vec file since I will use this file for pretrainedVectors parameter in fasttext.train_su...

Is it possible to use Google BERT for calculating similarity between two textual documents? As I understand BERT's input is supposed to be a limited size sentences. Some works use BERT for similari...

I have trained a Word2Vec model using Gensim 3.8.0. Later I tried to use the pretrained model using Gensim 4.0.o on GCP. I used the following code: model = KeyedVectors.load_word2vec_format(wv_path...

Given a sentence of the type 'Roberta is a heavily optimized version of BERT.', I need to get the embeddings for each of the words in this sentence with RoBERTa. I have tried to look at the sample ...

I want to store a large n-dimensional vector (e.g. an embedding vector) in SQL Server as a piece of metadata associated with another row. In this example, it will be a 384-dimensional vector, for e...

I am a brand new user of Chroma database (and the associate python libraries). When I call get on a collection, embeddings is always none, even if embeddings are explicitly set/defined when adding ...

For ElMo, FastText and Word2Vec, I'm averaging the word embeddings within a sentence and using HDBSCAN/KMeans clustering to group similar sentences. A good example of the implementation can be see...

I am trying to use CLIP to calculate the similarities between strings. (I know that CLIP is usually used with text and images but it should work with only strings as well.) I provide a list of simp...

tf.nn.embedding_lookup(params, ids, partition_strategy='mod', name=None) I cannot understand the duty of this function. Is it like a lookup table? Which means to return the parameters correspondi...

I would like to create a minibatch by encoding multiple sentences using transform.BertTokenizer. It seems working for a single sentence. How to make it work for several sentences? from transformers...

In the paper describing BERT, there is this paragraph about WordPiece Embeddings. We use WordPiece embeddings (Wu et al., 2016) with a 30,000 token vocabulary. The first token of every sequen...

I have downloaded the data with wget !wget http://nlp.stanford.edu/data/glove.6B.zip - ‘glove.6B.zip’ saved [862182613/862182613] It is saved as zip and I would like to use glove.6B.300d.txt fi...

In LDA model generates different topics everytime i train on the same corpus , by setting the np.random.seed(0), the LDA model will always be initialized and trained in exactly the same way. Is i...

I am using faiss indexflatIP to store vectors related to some words. I also use another list to store words (the vector of the nth element in the list is nth vector in faiss index). I have two ques...

I have roughly 2 million sentences that I want to turn into vectors using Facebook AI's RoBERTa-large,fine-tuned on NLI and STSB for sentence similarity (using the awesome sentence-transformers pac...

In this page, it is said that: [...] skip-gram inverts contexts and targets, and tries to predict each context word from its target word [...] However, looking at the training dataset it prod...

I have seen that NLP models such as BERT utilize WordPiece for tokenization. In WordPiece, we split the tokens like playing to play and ##ing. It is mentioned that it covers a wider spectrum of Out...

I've only seen a few questions that ask this, and none of them have an answer yet, so I thought I might as well try. I've been using gensim's word2vec model to create some vectors. I exported them ...

I was interesting in how to get the similarity of word embedding in different sentences from BERT model (actually, that means words have different meanings in different scenarios). For example: sen...

I know the meaning and methods of word embedding(skip-gram, CBOW) completely. And I know, that Google has a word2vector API that by getting the word can produce the vector. but my problem is this:...

I am using spaCy as part of a topic modelling solution and I have a situation where I need to map a derived word vector to the "closest" or "most similar" word in a vocabulary of word vectors. I s...

I need to create a 'search engine' experience : from a short query (few words), I need to find the relevant documents in a corpus of thousands documents. After analyzing few approaches, I got very...