Semantic-Search | Semantic search using Transformers and others | Machine Learning library

by renatoviolin Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | Semantic-Search Summary

Semantic-Search is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. Semantic-Search has no bugs, it has no vulnerabilities and it has low support. However Semantic-Search build file is not available. You can download it from GitHub.

Simple application using sentece embedding to project the documents in a high dimensional space and find most similarities using cosine similarity. The purpose is to demo and compare the models. To deploy in scale, it is necessary to compute and save the document embeddings to quickly search and compute similarities. The first load take a long time since the application will download all the models. Beside 6 models running, inference time is acceptable even in CPU.

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Security

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Support

Semantic-Search has a low active ecosystem.

It has 99 star(s) with 27 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 0 have been closed. On average issues are closed in 104 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of Semantic-Search is current.

Quality

Semantic-Search has no bugs reported.

Security

Semantic-Search has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

Semantic-Search does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

Semantic-Search releases are not available. You will need to build from source code and install.

Semantic-Search has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed Semantic-Search and discovered the below as its top functions. This is intended to give you an instant insight into Semantic-Search implemented functionality, and help decide if they suit your requirements.

Get prediction
Embed embedding
Returns True if weights are on CPU
Compute the encoder
Tokenize a string
Tokenize a token
Get a batch from a given batch
Computes the score and sentences for a given query
Encodes sentences
Prepare a list of sentences
Update vocabulary with new words
Get words with w2v vectors
Create a vocabulary from a list of sentences
Builds the k - word vocabulary
Get word_vec with k first k first k words
Builds the vocabulary
Set the w2v path

Get all kandi verified functions for this library.

Semantic-Search Key Features

No Key Features are available at this moment for Semantic-Search.

Semantic-Search Examples and Code Snippets

No Code Snippets are available at this moment for Semantic-Search.

Community Discussions

Trending Discussions on Semantic-Search

DomainLabelEmpty (Domain label is empty) encountered with 'semantic-search-v2.aws.magna.china.'

BERT sentence embeddings from transformers

QUESTION

DomainLabelEmpty (Domain label is empty) encountered with 'semantic-search-v2.aws.magna.china.'

Asked 2020-Nov-22 at 16:43

I can create A record in Route53:

But when I use cdk code to do the same thing. It failed. This is the Code:

...

ANSWER

Answered 2020-Nov-22 at 16:43

Try appending the r53 hosted zone name to the recordName attribute of ARecord.

Source https://stackoverflow.com/questions/64956686

QUESTION

BERT sentence embeddings from transformers

Asked 2020-Oct-07 at 13:06

I'm trying to get sentence vectors from hidden states in a BERT model. Looking at the huggingface BertModel instructions here, which say:

...

ANSWER

Answered 2020-Aug-18 at 16:31

I don't think there is single authoritative documentation saying what to use and when. You need to experiment and measure what is best for your task. Recent observations about BERT are nicely summarized in this paper: https://arxiv.org/pdf/2002.12327.pdf.

I think the rule of thumb is:

Use the last layer if you are going to fine-tune the model for your specific task. And finetune whenever you can, several hundred or even dozens of training examples are enough.
Use some of the middle layers (7-th or 8-th) if you cannot finetune the model. The intuition behind that is that the layers first develop a more and more abstract and general representation of the input. At some point, the representation starts to be more target to the pre-training task.

Bert-as-services uses the last layer by default (but it is configurable). Here, it would be [:, -1]. However, it always returns a list of vectors for all input tokens. The vector corresponding to the first special (so-called [CLS]) token is considered to be the sentence embedding. This where the [0] comes from in the snipper you refer to.

Source https://stackoverflow.com/questions/63461262

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Semantic-Search

You can download it from GitHub.
You can use Semantic-Search like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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