asyml.io

Website review asyml.io

ASYML - Machine Learning as Machine Assembly

 Generated on February 17 2026 23:40 PM

Old data? UPDATE !

The score is 44/100

SEO Content

Title

ASYML - Machine Learning as Machine Assembly

Length : 44

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Description

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Keywords

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Headings

H1 H2 H3 H4 H5 H6
1 7 0 0 0 0
  • [H1] AsyML
  • [H2] Machine Learning as Machine Assembly
  • [H2] Texar-TF: About × Texar is a highly modularized and customizable toolkit to support a broad set of machine learning (ML), especially natural language processing (NLP) and text generation tasks. Texar provides comprehensive modules for data processing, model architectures, loss functions, training and inference algorithms, evaluation, etc. Use Texar to compose whatever models and algorithms you want.
  • [H2] Texar-PyTorch: About × Texar-PyTorch is the PyTorch equivalence of Texar-TF, with mostly the same interfaces. Texar-PyTorch integrates many of the best features of TensorFlow into PyTorch, delivering a set of highly usable and customizable modules superior to PyTorch native ones, including Data: More ready-to-use APIs; more customizable; more efficient Model: Better factorization; more comprehensive high-level APIs Training: High-level APIs to avoid any boilerplate code
  • [H2] Forte: About × Forte is a high-level and customizable toolkit for building arbitrary complex NLP pipelines. With Forte, you can: Plug in any NLP components in a pipeline, including text analysis, generation, retrieval, etc. Wrap any off-the-shelf NLP models from other libraries as pipeline components, such as spaCy, StanfordNLP, AllenNLP, etc. Obtain a uniform representation of data and intermediate results throughout pipeline.
  • [H2] Stave: About × Stave is a extendable toolkit for building front end interfaces for NLP projects. Stave excels at: A general annotation and visualization interface for core NLP tasks. Integrated and backed up by the strong machine learning and NLP toolkit built by ASYML: Texar and Forte. Can be easily extended by building plugins for your requirements.
  • [H2] Get Started
  • [H2] Companies and Universities Supporting Texar

Images

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Text/HTML Ratio

Ratio : 5%

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Flash

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Iframe

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URL Rewrite

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Underscores in the URLs

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In-page links

We found a total of 8 links including 0 link(s) to files

Anchor Type Juice
Resources Internal Passing Juice
Texar-TF: About Internal Passing Juice
- External Passing Juice
Chenyu External Passing Juice
https://github.com/asyml/texar/releases External Passing Juice
https://github.com/asyml/texar#installation External Passing Juice
https://github.com/asyml/texar-pytorch/releases External Passing Juice
https://github.com/asyml/texar-pytorch#installation External Passing Juice

SEO Keywords

Keywords Cloud

stave zeng cun made github logo chenyu machine logos texar

Keywords Consistency

Keyword Content Title Keywords Description Headings
texar 2
github 2
machine 2
logos 1
cun 1

Usability

Url

Domain : asyml.io

Length : 8

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Printability

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Language

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Dublin Core

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Document

Doctype

HTML 5

Encoding

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W3C Validity

Errors : 0

Warnings : 0

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Deprecated HTML

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Speed Tips

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Mobile

Mobile Optimization

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Optimization

XML Sitemap

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Robots.txt

https://asyml.io/robots.txt

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Analytics

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PageSpeed Insights


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