KeyBERT is a minimal and easy-to-use keyword extraction technique that leverages BERT embeddings to create keywords and keyphrases that are most similar to a document.
About the Project¶
Although there are already many methods available for keyword generation (e.g., Rake, YAKE!, TF-IDF, etc.) I wanted to create a very basic, but powerful method for extracting keywords and keyphrases. This is where KeyBERT comes in! Which uses BERT-embeddings and simple cosine similarity to find the sub-phrases in a document that are the most similar to the document itself.
First, document embeddings are extracted with BERT to get a document-level representation. Then, word embeddings are extracted for N-gram words/phrases. Finally, we use cosine similarity to find the words/phrases that are the most similar to the document. The most similar words could then be identified as the words that best describe the entire document.
KeyBERT is by no means unique and is created as a quick and easy method
for creating keywords and keyphrases. Although there are many great
papers and solutions out there that use BERT-embeddings
), I could not find a BERT-based solution that did not have to be trained from scratch and
could be used for beginners (correct me if I'm wrong!).
Thus, the goal was a
pip install keybert and at most 3 lines of code in usage.
Installation can be done using pypi:
pip install keybert
You may want to install more depending on the transformers and language backends that you will be using. The possible installations are:
pip install keybert[flair] pip install keybert[gensim] pip install keybert[spacy] pip install keybert[use]
To install all backends:
pip install keybert[all]
The most minimal example can be seen below for the extraction of keywords:
from keybert import KeyBERT doc = """ Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a 'reasonable' way (see inductive bias). """ kw_model = KeyBERT() keywords = kw_model.extract_keywords(doc)
You can set
keyphrase_ngram_range to set the length of the resulting keywords/keyphrases:
>>> kw_model.extract_keywords(doc, keyphrase_ngram_range=(1, 1), stop_words=None) [('learning', 0.4604), ('algorithm', 0.4556), ('training', 0.4487), ('class', 0.4086), ('mapping', 0.3700)]
To extract keyphrases, simply set
keyphrase_ngram_range to (1, 2) or higher depending on the number
of words you would like in the resulting keyphrases:
>>> kw_model.extract_keywords(doc, keyphrase_ngram_range=(1, 2), stop_words=None) [('learning algorithm', 0.6978), ('machine learning', 0.6305), ('supervised learning', 0.5985), ('algorithm analyzes', 0.5860), ('learning function', 0.5850)]