So as you know, we can extract BERT features of word in a sentence. My question is, can we also extract word features that are not included in a sentence? For example, bert features of single words such as "dog", "human", etc.
Extracting word features from BERT model
897 Views Asked by Kadaj13 At
1
There are 1 best solutions below
Related Questions in WORD-EMBEDDING
- Learning word-embeddings from characters using already learned word embedding
- To create different embedding layers in keras
- Use LSTM tutorial code to predict next word in a sentence?
- Why Word2Vec's most_similar() function is giving senseless results on training?
- How Word Mover's Distance (WMD) uses word2vec embedding space?
- Word Mover's distance calculation between word pairs of two documents
- Need of context while using Word2Vec
- finetuning tensorflow seq2seq model
- How to store Bag of Words or Embeddings in a Database
- Fine tuning of Bert word embeddings
- problem saving pre-trained fasttext vectors in "word2vec" format with _save_word2vec_format()
- How do I train word embeddings within a large block of custom text using BERT?
- The last layers of longformer for document embeddings
- text2vec word embeddings : compound some tokens but not all
- Word2Vec- does the word embedding change?
Related Questions in BERT-LANGUAGE-MODEL
- Are special tokens [CLS] [SEP] absolutely necessary while fine tuning BERT?
- BERT NER Python
- Fine tuning of Bert word embeddings
- how to predict a masked word in a given sentence
- Batch size keeps on changin, throwing `Pytorch Value Error Expected: input batch size does not match target batch size`
- Huggingface BERT SequenceClassification - ValueError: too many values to unpack (expected 2)
- How do I train word embeddings within a large block of custom text using BERT?
- what's the difference between "self-attention mechanism" and "full-connection" layer?
- Convert dtype('<U13309') to string in python
- Can I add a layer of meta data in a text classification model?
- My checkpoint albert files does not change when training
- BERT zero layer fixed word embeddings
- Tensorflow input for a series of (1, 512) tensors
- Microsoft LayoutLM model error with huggingface
- BERT model classification with many classes
Related Questions in LATENT-SEMANTIC-ANALYSIS
- Using the lsa package in R - Error in Ops.simple_triplet_matrix(m, 1) : Incompatible dimensions
- choose the proper clustering method for Latent Semantic Analysis
- Extracting word features from BERT model
- In Latent Semantic Analysis, how do you recombine the decomposed matrices after truncating the singular values?
- LSA Similarity interface
- How Sklearn Latent Dirichlet Allocation really Works?
- AttributeError: 'int' object has no attribute 'toarray'
- How do i retain numbers while preprocessing data using gensim in python?
- probabilistic latent semantic analysis R
- LSA - Feature selection
- Which formula of tf-idf does the LSA model of gensim use?
- Unsupervised commands classification
- How Latent Semantic Analysis Handle Semantics
- R Supervised Latent Dirichlet Allocation Package
- Finding Semantic Coherence between sentences in a text
Trending Questions
- UIImageView Frame Doesn't Reflect Constraints
- Is it possible to use adb commands to click on a view by finding its ID?
- How to create a new web character symbol recognizable by html/javascript?
- Why isn't my CSS3 animation smooth in Google Chrome (but very smooth on other browsers)?
- Heap Gives Page Fault
- Connect ffmpeg to Visual Studio 2008
- Both Object- and ValueAnimator jumps when Duration is set above API LvL 24
- How to avoid default initialization of objects in std::vector?
- second argument of the command line arguments in a format other than char** argv or char* argv[]
- How to improve efficiency of algorithm which generates next lexicographic permutation?
- Navigating to the another actvity app getting crash in android
- How to read the particular message format in android and store in sqlite database?
- Resetting inventory status after order is cancelled
- Efficiently compute powers of X in SSE/AVX
- Insert into an external database using ajax and php : POST 500 (Internal Server Error)
Popular Questions
- How do I undo the most recent local commits in Git?
- How can I remove a specific item from an array in JavaScript?
- How do I delete a Git branch locally and remotely?
- Find all files containing a specific text (string) on Linux?
- How do I revert a Git repository to a previous commit?
- How do I create an HTML button that acts like a link?
- How do I check out a remote Git branch?
- How do I force "git pull" to overwrite local files?
- How do I list all files of a directory?
- How to check whether a string contains a substring in JavaScript?
- How do I redirect to another webpage?
- How can I iterate over rows in a Pandas DataFrame?
- How do I convert a String to an int in Java?
- Does Python have a string 'contains' substring method?
- How do I check if a string contains a specific word?
The very first layer of BERT is a static embeddings table, so you can use it as any other embeddings table and embeddings for words (or more frequently subwords) that BERT uses input to the first self-attentive layer. The static embeddings are only comparable with each other, not with the standard contextual embeddings. If need them comparable embeddings, you can try passing single-word sentences to BERT, but note that this will be an embeddings of a single-word sentenece, not the word in general.
However, BERT is a sentence-level model that is supposed to get embeddings of words in context. It is not designed for static word embeddings, and methods specifically designed for static word embeddings (such as FastText) would certainly get better results.