I have a csv file, it has 2 columns: class and text_data. I first extract biGram and TriGrams, then tried to use SVM on my data for classification. But it shows "TypeError: sequence item 0: ex...

What's the right way to return a limited number of layers using the longformer API? Unlike this case in basic BERT, it's not clear to me from the return type how to get only the last N layers. So, ...

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

How do I load a pre-trained word-embedding into a Keras Embedding layer? I downloaded the glove.6B.50d.txt (glove.6B.zip file from https://nlp.stanford.edu/projects/glove/) and I'm not sure how t...

Assuming that I want to update a pre-trained word-embedding matrix during training, is there a way to update only a subset of the word embedding matrix? I have looked into the Tensorflow API page ...

I have a large pretrained Word2Vec model in gensim from which I want to use the pretrained word vectors for an embedding layer in my Keras model. The problem is that the embedding size is enormou...

I am trying use an embeddings module from tensorflow hub as servable. I am new to tensorflow. Currently, I am using Universal Sentence Encoder embeddings as a lookup to convert sentences to embeddi...

Does anyone know how to load a tsv file with embeddings generated from StarSpace into Gensim? Gensim documentation seems to use Word2Vec a lot and I couldn't find a pertinent answer. Thanks, Amul...

I am using gensim library for loading pre-trained word vectors from GoogleNews dataset. this dataset contains 3000000 word vectors each of 300 dimensions. when I want to load GoogleNews dataset, I ...

I would like to use state-of-the-art LM T5 to get sentence embedding vector. I found this repository https://github.com/UKPLab/sentence-transformers As I know, in BERT I should take the first token...

What is the difference between word2vec and glove? Are both the ways to train a word embedding? if yes then how can we use both?

I am trying to do document embedding using BERT. The code I use is a combination of two sources. I use BERT Document Classification Tutorial with Code, and BERT Word Embeddings Tutorial. Below is t...

Are there any latest pre-trained multilingual word embeddings (multiple languages are jointly mapped to a same vector space)? I have looked at the following but they don't fit my needs: FastText...

I have trained fasttext model with Gensim over the corpus of very short sentences (up to 10 words). I know that my test set includes words that are not in my train corpus, i.e some of the words in ...

I am using BERT Word Embeddings for sentence classification task with 3 labels. I am using Google Colab for coding. My problem is, since I will have to execute the embedding part every time I resta...

I have sentences that I vectorize using sentence_vector() method of BiobertEmbedding python module (https://pypi.org/project/biobert-embedding/). For some group of sentences I have no problem but f...

I'm coming from Keras to PyTorch. I would like to create a PyTorch Embedding layer (a matrix of size V x D, where V is over vocabulary word indices and D is the embedding vector dimension) with Glo...

So I'm trying to build a word embedding model but I keep getting this error. During training, the accuracy does not change and the val_loss remains "nan" The raw shape of the data is x.shape, y.s...

I'm loading a language model from torch hub (CamemBERT a French RoBERTa-based model) and using it do embed some french sentences: import torch camembert = torch.hub.load('pytorch/fairseq', 'camem...

I've been reading some NLP with Deep Learning papers and found Fine-tuning seems to be a simple but yet confusing concept. There's been the same question asked here but still not quite clear....

I am working on a recurrent language model. To learn word embeddings that can be used to initialize my language model, I am using gensim's word2vec model. After training, the word2vec model holds ...

I thought mask_zero=True will output 0's when the input value is 0, so the following layers could skip computation or something. How does mask_zero works? Example: data_in = np.array([ [1, 2,...

I am trying to get textual representation(or the closest word) of given word embedding using BERT. Basically I am trying to get similar functionality as in gensim: >>> your_word_vector = ...

I have seen both terms used while reading papers about BERT and ELMo so I wonder if there is a difference between them.

I've been following Towards Data Science's tutorial about word2vec and skip-gram models, but I stumbled upon a problem that I cannot solve, despite searching about it for hours and trying a lot of ...