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
900 Views Asked by Kadaj13 At
1
There are 1 best solutions below
Related Questions in WORD-EMBEDDING
- I am unable to perform the vector embeddings with the help of pinecone and python
- How I can search a string in a JSON file with an word-embedding list and return the nearest occurrences?
- the key did not present in Word2vec
- Subsampling when training word embeddings
- Enhancing BERT+CRF NER Model with keyphrase list
- Text Embedding result based on Priority
- Why is it possible to use OpenAI Embeddings together with Anthropic Claude Model?
- Set sample points for each cluster in kmeans using Python
- is there any way to retrieve the embeddings store in a langchain VectorStore?
- BERTopic document visualization same color for a list of topics
- How can I build an embedding encoder with FastAPI
- How can I get the embedding of a document in langchain?
- Mapping embeddings to labels in PyTorch/Huggingface
- How do I fix the output of one layer so it is compatible to another layer?
- Efficient many-to-many embedding comparisons
Related Questions in BERT-LANGUAGE-MODEL
- The training accuracy and the validation accuracy curves are almost parallel to each other. Is the model overfitting?
- Give Bert an input and ask him to predict. In this input, can Bert apply the first word prediction result to all subsequent predictions?
- how to create robust scraper for specific website without updating code after develop?
- Why are SST-2 and CoLA commonly used datasets for debiasing?
- Is BertForSequenceClassification using the CLS vector?
- How to add noise to the intermediate layer of huggingface bert model?
- Bert Istantiation TypeError: 'NoneType' object is not callable Tensorflow
- tensorflow bert 'tuple' object has no attribute problem
- Data structure in Autotrain for bert-base-uncased
- How to calculate cosine similarity with bert over 1000 random example
- the key did not present in Word2vec
- ResourceExhaustedError In Tensorflow BERT Classifier
- Enhancing BERT+CRF NER Model with keyphrase list
- Merging 6 ONNX Models into One for Unity Barracuda
- What's the exact input size in MultiHead-Attention of BERT?
Related Questions in LATENT-SEMANTIC-ANALYSIS
- Tensor Decomposition and Label-Weight Assignment in Python
- How do i retain numbers while preprocessing data using gensim in python?
- AttributeError: 'int' object has no attribute 'toarray'
- How Sklearn Latent Dirichlet Allocation really Works?
- Extracting word features from BERT model
- nltk latent semantic analysis copies the first topics over and over
- Unsupervised commands classification
- Is it possible to set the initial topic assignments for scikit-learn LDA?
- Which formula of tf-idf does the LSA model of gensim use?
- Topic Modelling: LDA , word frequency in each topic and Wordcloud
- Latent Semantic Indexation with gensim
- Latent Semantic Analysis and Stemming
- Latent text analysis (lsa package) using whole documents in R
- Semantic Similarity between Sentences in a Text
- 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 # Hahtags
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.