postgres-retrofit | create database-specific text value embeddings | Topic Modeling library
kandi X-RAY | postgres-retrofit Summary
kandi X-RAY | postgres-retrofit Summary
Tools to create database-specific text value embeddings from word embedding datasets
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Top functions reviewed by kandi - BETA
- Main entry point for vector query
- Get a configuration object from a file
- Extracts the vectors that are present in the group
- Create an edge list from the given configuration file
- Write term vectors to a file
- Return all table column names in the database
- Get all the terms in the table
- Create index on a table
- Return a config object based on argv
- R Creates a matrix with the presence matrix
- Return a dict of relation groups
- Classify the method
- Retrieve column groups
- Construct adjacency matrix
- Classify examples
- Get all terms from vector table
- R Repeated smoothing
- Get the schema for a table
- Get all vectors in a table
- Parse groups from a json file
- Classify method
- Perform validation
- Performs a train
- Get relation groups
- Create a group
- Fast method for classification
- Perform a test on the network
- Creates a matrix with the presence of the given terms
- Retrieve a config object from a file
- Create an index on a table
- Extract terms from the table
- Get all table columns
- Construct a DGL graph from a name
- Extracts the vectors for each element in the group
- Write term vectors
- Creates the edges for the given graph
- Get all the vectors in a table
- Get the schema for the table
- R Repeated smoothing
- Get all terms from vector table
- Compute the classification
- Construct adjacency matrices from terms
- Retrieve column groups
- Parse groups from a file
postgres-retrofit Key Features
postgres-retrofit Examples and Code Snippets
Community Discussions
Trending Discussions on Topic Modeling
QUESTION
I am trying to use a pre-trained model from TensorFlow hub instead of frequency vectorization techniques for word embedding before passing the resultant feature vector to the LDA model.
I followed the steps for the TensorFlow model, but I got this error upon passing the resultant feature vector to the LDA model:
...ANSWER
Answered 2022-Feb-24 at 09:31As the fit function of LatentDirichletAllocation does not allow a negative array, I will recommend you to apply softplus on the embeddings.
Here is the code snippet:
QUESTION
I am new to using LSI with Python and Gensim + Scikit-learn tools. I was able to achieve topic modeling on a corpus using LSI from both the Scikit-learn and Gensim libraries, however, when using the Gensim approach I was not able to display a list of documents to topic mapping.
Here is my work using Scikit-learn LSI where I successfully displayed document to topic mapping:
...ANSWER
Answered 2022-Feb-22 at 19:27In order to get the representation of a document (represented as a bag-of-words) from a trained LsiModel as a vector of topics, you use Python dict-style bracket-accessing (model[bow]).
For example, to get the topics for the 1st item in your training data, you can use:
QUESTION
I am trying to understand how Top2Vec works. I have some questions about the code that I could not find an answer for in the paper. A summary of what the algorithm does is that it:
- embeds words and vectors in the same semantic space and normalizes them. This usually has more than 300 dimensions.
- projects them into 5-dimensional space using UMAP and cosine similarity.
- creates topics as centroids of clusters using HDBSCAN with Euclidean metric on the projected data.
what troubles me is that they normalize the topic vectors. However, the output from UMAP is not normalized, and normalizing the topic vectors will probably move them out of their clusters. This is inconsistent with what they described in their paper as the topic vectors are the arithmetic mean of all documents vectors that belong to the same topic.
This leads to two questions:
How are they going to calculate the nearest words to find the keywords of each topic given that they altered the topic vector by normalization?
After creating the topics as clusters, they try to deduplicate the very similar topics. To do so, they use cosine similarity. This makes sense with the normalized topic vectors. In the same time, it is an extension of the inconsistency that normalizing topic vectors introduced. Am I missing something here?
...ANSWER
Answered 2022-Feb-16 at 16:13I got the answer to my questions from the source code. I was going to delete the question but I will leave the answer any way.
It is the part I missed and is wrong in my question. Topic vectors are the arithmetic mean of all documents vectors that belong to the same topic. Topic vectors belong to the same semantic space where words and documents vector live.
That is why it makes sense to normalize them since all words and documents vectors are normalized, and to use the cosine metric when looking for duplicated topics in the higher original semantic space.
QUESTION
I am trying to extract topic scores for documents in my dataset after using and LDA model. Specifically, I have followed most of the code from here: https://www.machinelearningplus.com/nlp/topic-modeling-gensim-python/
I have completed the topic model and have the results I want, but the provided code only gives the most dominant topic for each document. Is there a simple way to modify the following code to give me the scores for say the 5 most dominant topics?
...ANSWER
Answered 2021-Dec-10 at 10:33Right this is a crusty example because you haven't provided data to reproduce but using some gensim testing corpus, texts and dictionary we can do:
QUESTION
I am using pyLDAvis along with gensim.models.LdaMulticore for topic modeling. I have totally 10 topics. When I visualize the results using pyLDAvis, there is a bar called lambda with this explanation: "Slide to adjust relevance metric". I am interested to extract the list of words for each topic separately for lambda = 0.1. I cannot find a way to adjust lambda in the document for extracting keywords.
I am using these lines:
...ANSWER
Answered 2021-Nov-24 at 10:43You may want to read this github page: https://nicharuc.github.io/topic_modeling/
According to this example, your code could go like this:
QUESTION
Working with the OCTIS package, I am running a CTM topic model on the BBC (default) dataset.
...ANSWER
Answered 2021-Oct-11 at 15:19I'm one of the developers of OCTIS.
Short answer:
If I understood your problem, you can fix this issue by modifying the parameter "bert_path" of CTM and make it dataset-specific, e.g. CTM(bert_path="path/to/store/the/files/" + data)
TL;DR: I think the problem is related to the fact that CTM generates and stores the document representations in some files with a default name. If these files already exist, it uses them without generating new representations, even if the dataset has changed in the meantime. Then CTM will raise that issue because it is using the BOW representation of a dataset, but the contextualized representations of another dataset, resulting in two representations with different dimensions. Changing the name of the files with respect to the name of the dataset will allow the model to retrieve the correct representations.
If you have other issues, please open a GitHub issue in the repo. I've found out about this issue by chance.
QUESTION
ANSWER
Answered 2021-Sep-20 at 01:19You should pass a column of data to the fit_transform function. Here is the example
QUESTION
ANSWER
Answered 2021-Sep-13 at 08:30It's a matter of scale. If you have 1000 types (ie "dictionary words"), you might end up (in the worst case, which is not going to happen) with 1,000,000 bigrams, and 1,000,000,000 trigrams. These numbers are hard to manage, especially as you will have a lot more types in a realistic text.
The gains in accuracy/performance don't outweigh the computational cost here.
QUESTION
Starting from the following example
...ANSWER
Answered 2021-Sep-08 at 11:20You can compute the explained variance with a range of the possible number of components. The maximum number of components is the size of your vocabulary.
QUESTION
I am doing a topic modelling task with LDA, and I am getting 10 components with 15 top words each:
...ANSWER
Answered 2021-Jun-23 at 08:01If I understand correctly, you have a dataframe with all values and you want to keep the top 10 in each row, and have 0s on remaining values.
Here we transform each row by:
- getting the 10th highest values
- reindexing to the original index of the row (thus the columns of the dataframe) and filling with 0s:
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