language-models | Keras implementations of three language models | Machine Learning library

by pbloem Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | language-models Summary

language-models is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Keras, Neural Network applications. language-models has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However language-models build file is not available. You can download it from GitHub.

Keras implementations of three language models: character-level RNN, word-level RNN and Sentence VAE (Bowman, Vilnis et al 2016). Each model is implemented and tested and should run out-of-the box. The default parameters will provide a reasonable result relatively quickly. You can get better results by using bigger datasets, more epochs, or by tweaking the batch size/learning rate.

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language-models has a low active ecosystem.

It has 36 star(s) with 12 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 4 have been closed. On average issues are closed in 2 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of language-models is current.

Quality

language-models has 0 bugs and 0 code smells.

Security

language-models has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

language-models code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

language-models is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

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

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

Installation instructions, examples and code snippets are available.

language-models saves you 293 person hours of effort in developing the same functionality from scratch.

It has 707 lines of code, 30 functions and 4 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Go through the corpus
Load the words from source files
Load all characters from source files
Generate a sequence of random characters
Pad a sequence of sequences
Sample the probability distribution
Sample logits
Convert batch to categorical
Calculate anneal
Yield successive n - sized chunks from l

Get all kandi verified functions for this library.

language-models Key Features

No Key Features are available at this moment for language-models.

language-models Examples and Code Snippets

No Code Snippets are available at this moment for language-models.

Community Discussions

Trending Discussions on language-models

NLP ELMo model pruning input

Size of the training data of GPT2-XL pre-trained model

ElasticSearch-How to combine results of different queries to improve Mean Average Precision

QUESTION

NLP ELMo model pruning input

Asked 2021-May-27 at 04:47

I am trying to retrieve embeddings for words based on the pretrained ELMo model available on tensorflow hub. The code I am using is modified from here: https://www.geeksforgeeks.org/overview-of-word-embedding-using-embeddings-from-language-models-elmo/

The sentence that I am inputting is
bod =" is coming up in and every project is expected to do a video due on we look forward to discussing this with you at our meeting this this time they have laid out the selection criteria for the video award s go for the top spot this time "

and these are the keywords I want embeddings for:
words=["do", "a", "video"]

...

ANSWER

Answered 2021-May-27 at 04:47

This is not really an AllenNLP issue since you are using a tensorflow-based implementation of ELMo.

That said, I think the problem is that ELMo embeds tokens, not characters. You are getting 48 embeddings because the string has 48 tokens.

Source https://stackoverflow.com/questions/67558874

QUESTION

Size of the training data of GPT2-XL pre-trained model

Asked 2020-Feb-11 at 18:47

In huggingface transformer, it is possible to use the pre-trained GPT2-XL language model. But I don't find, on which dataset it is trained? Is it the same trained model which OpenAI used for their paper (trained on 40GB dataset called webtext) ?

...

ANSWER

Answered 2020-Feb-11 at 18:47

The GPT2-XL model is the biggest of the four architectures detailed in the paper you linked (1542M parameters). It is trained on the same data as the other three, which is the WebText you're mentioning.

Source https://stackoverflow.com/questions/60173639

QUESTION

ElasticSearch-How to combine results of different queries to improve Mean Average Precision

Asked 2019-Jun-09 at 16:38

I am making a query A on elastic search and get the first 50 results. I also make a query B which contains the 30% of the terms of the query A. Each result of query A has a similarity score scoreA and each result of B has scoreB. What I am trying to achieve is combine the results of A and B to improve the Mean Average Precision of each imdividual query. One way that I found is to reorder the results based on this formula:

...

ANSWER

Answered 2019-Jun-09 at 16:38

Combination of results of different queries in Elasticsearch is commonly achieved with bool query. Changes in the way they are combined can be made using function_score query.

In case you need to combine different per-field scoring functions (also known as similarity), to, for instance, do the same query with BM25 and DFR and combine their results, indexing the same field several times with use of fields can help.

Now let me explain how this thing works.

Find official website of David Gilmour

Let's imagine we have an index with following mapping and example documents:

Source https://stackoverflow.com/questions/56492145

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

Vulnerabilities

No vulnerabilities reported

Install language-models

The three models are provided as standalone scripts. Just download or clone the repository and run any of the following:. Add -h to see the parameters you can change. Make sure you have python 3, and the required packages installed.

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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