metapy

Python bindings for MeTA

License: MIT License, University of Illinois/NCSA Open Source License (MIT)

Tags NLP, natural language processing, IR, information retrieval, CL, computational lingusitics, parsing, tagging, tokenizing, syntax, lingustics, natural language, text mining, text analysis

Project description

UNKNOWN

Project details

License: MIT License, University of Illinois/NCSA Open Source License (MIT)

Author: Chase Geigle

Tags NLP, natural language processing, IR, information retrieval, CL, computational lingusitics, parsing, tagging, tokenizing, syntax, lingustics, natural language, text mining, text analysis

Release history Release notifications | RSS feed

0.2.13

Aug 6, 2018

0.2.12

Jul 15, 2018

0.2.11

Jun 26, 2018

0.2.10

May 1, 2018

0.2.9

Oct 21, 2017

0.2.8

Sep 7, 2017

0.2.6

Aug 16, 2017

0.2.5

Aug 16, 2017

0.2.4

Aug 4, 2017

0.2.3

Jul 30, 2017

0.2.2

Apr 27, 2017

0.2.1

Apr 18, 2017

0.1.6

Apr 12, 2017

0.1.5

Mar 28, 2017

0.1.4

Mar 4, 2017

This version

0.1.3

Mar 2, 2017

0.1.2

Feb 15, 2017

0.0.1.dev11 pre-release

Feb 10, 2017

0.0.1.dev9 pre-release

Sep 26, 2016

0.0.1.dev8 pre-release

Sep 13, 2016

0.0.1.dev7 pre-release

Aug 4, 2016

0.0.1.dev6 pre-release

May 5, 2016

0.0.1.dev3 pre-release

Apr 22, 2016

0.0.1.dev2 pre-release

Apr 20, 2016

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for metapy, version 0.1.3
Filename, size	File type	Python version	Upload date	Hashes
Filename, size metapy-0.1.3-cp27-cp27m-macosx_10_6_intel.whl (12.7 MB)	File type Wheel	Python version cp27	Upload date Mar 2, 2017	Hashes View
Filename, size metapy-0.1.3-cp27-cp27m-manylinux1_x86_64.whl (13.2 MB)	File type Wheel	Python version cp27	Upload date Mar 2, 2017	Hashes View
Filename, size metapy-0.1.3-cp27-cp27mu-manylinux1_x86_64.whl (13.2 MB)	File type Wheel	Python version cp27	Upload date Mar 2, 2017	Hashes View
Filename, size metapy-0.1.3-cp27-cp27m-win_amd64.whl (13.3 MB)	File type Wheel	Python version cp27	Upload date Mar 2, 2017	Hashes View
Filename, size metapy-0.1.3-cp33-cp33m-macosx_10_6_x86_64.whl (12.7 MB)	File type Wheel	Python version cp33	Upload date Mar 2, 2017	Hashes View
Filename, size metapy-0.1.3-cp33-cp33m-manylinux1_x86_64.whl (13.2 MB)	File type Wheel	Python version cp33	Upload date Mar 2, 2017	Hashes View
Filename, size metapy-0.1.3-cp33-cp33m-win_amd64.whl (13.3 MB)	File type Wheel	Python version cp33	Upload date Mar 2, 2017	Hashes View
Filename, size metapy-0.1.3-cp34-cp34m-macosx_10_6_intel.whl (12.7 MB)	File type Wheel	Python version cp34	Upload date Mar 2, 2017	Hashes View
Filename, size metapy-0.1.3-cp34-cp34m-manylinux1_x86_64.whl (13.2 MB)	File type Wheel	Python version cp34	Upload date Mar 2, 2017	Hashes View
Filename, size metapy-0.1.3-cp34-cp34m-win_amd64.whl (13.3 MB)	File type Wheel	Python version cp34	Upload date Mar 2, 2017	Hashes View
Filename, size metapy-0.1.3-cp35-cp35m-macosx_10_6_intel.whl (12.7 MB)	File type Wheel	Python version cp35	Upload date Mar 2, 2017	Hashes View
Filename, size metapy-0.1.3-cp35-cp35m-manylinux1_x86_64.whl (13.2 MB)	File type Wheel	Python version cp35	Upload date Mar 2, 2017	Hashes View
Filename, size metapy-0.1.3-cp35-cp35m-win_amd64.whl (13.3 MB)	File type Wheel	Python version cp35	Upload date Mar 2, 2017	Hashes View
Filename, size metapy-0.1.3-cp36-cp36m-macosx_10_6_intel.whl (12.7 MB)	File type Wheel	Python version cp36	Upload date Mar 2, 2017	Hashes View
Filename, size metapy-0.1.3-cp36-cp36m-manylinux1_x86_64.whl (13.2 MB)	File type Wheel	Python version cp36	Upload date Mar 2, 2017	Hashes View
Filename, size metapy-0.1.3-cp36-cp36m-win_amd64.whl (13.3 MB)	File type Wheel	Python version cp36	Upload date Mar 2, 2017	Hashes View
Filename, size metapy-0.1.3.tar.gz (2.8 MB)	File type Source	Python version None	Upload date Mar 2, 2017	Hashes View

Hashes for metapy-0.1.3-cp27-cp27m-macosx_10_6_intel.whl

Hashes for metapy-0.1.3-cp27-cp27m-macosx_10_6_intel.whl
Algorithm	Hash digest
SHA256	`2345573633c5d381fa2749be68c2a0a032c1d5b62f983054553a98527d07953a`
MD5	`11a644cf1c2f834bdd61b0ef314520e3`
BLAKE2-256	`ec00adcfe3076e918cb5a2c88a56b50b398eaa9f6e5489ca8b030672b88d44f2`

Hashes for metapy-0.1.3-cp27-cp27m-manylinux1_x86_64.whl

Hashes for metapy-0.1.3-cp27-cp27m-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`fd5f1040316b611c4ffd343025622277617b1c07873fa858fe9125116df6f6a6`
MD5	`54b5e1c37745aaa70bfa0cb9179805c8`
BLAKE2-256	`7ab882e085e62ca51f8996a0fde00c04c294558007b6383dd17cf2146b1053a7`

Hashes for metapy-0.1.3-cp27-cp27mu-manylinux1_x86_64.whl

Hashes for metapy-0.1.3-cp27-cp27mu-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`56373064979d94065b5e09b5380aa422c76eb8670d1baffd9aefc12ac261fcac`
MD5	`2ae8e74a16a939ba2ec44ca001288afc`
BLAKE2-256	`247722e17e07c38a74ef60e06aeb6ca1d8d028f7f5ca48e40af2422d18463a75`

Hashes for metapy-0.1.3-cp27-cp27m-win_amd64.whl

Hashes for metapy-0.1.3-cp27-cp27m-win_amd64.whl
Algorithm	Hash digest
SHA256	`ef1b09ae789153835c451b3848998880f44eee1e9f89032595615f3516eca0af`
MD5	`da1eb95f3c2bdf11645c31bc2a96633b`
BLAKE2-256	`a3e3787c95d0ba2322f66bacc759e6dd77dcc8c41d95771445968ae4e7b77e08`

Hashes for metapy-0.1.3-cp33-cp33m-macosx_10_6_x86_64.whl

Hashes for metapy-0.1.3-cp33-cp33m-macosx_10_6_x86_64.whl
Algorithm	Hash digest
SHA256	`dbb9a5d586e3e9bc03e45c02eebb052c08630c1133e0c6ff911a635612a82751`
MD5	`47da7eabdb501eb5c4882444cf25497e`
BLAKE2-256	`56585e2ccc71c5717a15b94355f6092baa730ec384c4bfda71fb6aa0132fc960`

Hashes for metapy-0.1.3-cp33-cp33m-manylinux1_x86_64.whl

Hashes for metapy-0.1.3-cp33-cp33m-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`4eb933747143e5c8649fea40d4ea514aa431ad6b5c556723219d20ca7713fc04`
MD5	`35141221a691d15907b899c9b140ce6e`
BLAKE2-256	`2a78058410f125f93a6bfdf38a99bd23f8ba4705e542a4478edeccb453b008c0`

Hashes for metapy-0.1.3-cp33-cp33m-win_amd64.whl

Hashes for metapy-0.1.3-cp33-cp33m-win_amd64.whl
Algorithm	Hash digest
SHA256	`bd3ed1c9fbc24f74b9e543ad11b8f9f90facb910c3f9f0d93d2fcf68a12c401e`
MD5	`2980d3b96400dd96e0a6c3f51eebd500`
BLAKE2-256	`7dff94d34d5aae4a038b19785d1a3bf45ba68a50ce3ee6c087417fe2d2bd0bd9`

Hashes for metapy-0.1.3-cp34-cp34m-macosx_10_6_intel.whl

Hashes for metapy-0.1.3-cp34-cp34m-macosx_10_6_intel.whl
Algorithm	Hash digest
SHA256	`2d004f2b333a2374d7b9301a6c265002eee420686c4d24abffb2aa5a43dc135c`
MD5	`e62a882f2ac0f6ceff9bc63e7cca5258`
BLAKE2-256	`d9daa441193f98f437fb4d730a16c58a8bb96b4623e5cf8fa56cbf2450e56fc6`

Hashes for metapy-0.1.3-cp34-cp34m-manylinux1_x86_64.whl

Hashes for metapy-0.1.3-cp34-cp34m-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`ba87122edd29a0825ed9d8bd7ef076ffae5bce17c14b371178b5d7972ee0cfb4`
MD5	`1be8952ac84723157a103ee45415b1e0`
BLAKE2-256	`723040aa636b4a59fb5fb1e83b5ae503cc7ec98558552a3f0bdfdccae5a41a5f`

Hashes for metapy-0.1.3-cp34-cp34m-win_amd64.whl

Hashes for metapy-0.1.3-cp34-cp34m-win_amd64.whl
Algorithm	Hash digest
SHA256	`53286d912af8a94450336ca63c0abeb2c43a6d240ece375f0aa782bd1cd09fda`
MD5	`c667bf9801d872dee5ed545f123c69bc`
BLAKE2-256	`4b610882cdacdc5c7e31c83a1ca0c0b4dc844f793f194e03e43aa74c1cf0c8a2`

Hashes for metapy-0.1.3-cp35-cp35m-macosx_10_6_intel.whl

Hashes for metapy-0.1.3-cp35-cp35m-macosx_10_6_intel.whl
Algorithm	Hash digest
SHA256	`5958b5b1fe365c0efe89f65a83c52d28ea478e2f6bdd7fd0981333e6858589cc`
MD5	`bfe7b85904a610be41cc6184c2b8fb13`
BLAKE2-256	`4d46dfaef9682eaf2b9788b4686edcf7b9028850415f7f8ffe2d5cfb35396ab7`

Hashes for metapy-0.1.3-cp35-cp35m-manylinux1_x86_64.whl

Hashes for metapy-0.1.3-cp35-cp35m-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`42aa47e8e4bf37378482e907c851c0f7bd327a186b475565bfe37246bbfd91fa`
MD5	`5a1c787d37e1da610c663921cb4747ec`
BLAKE2-256	`99a9b3596c05d20643d745203b3ceb5142b04d3d5ba8109e9140d0225ef14502`

Hashes for metapy-0.1.3-cp35-cp35m-win_amd64.whl

Hashes for metapy-0.1.3-cp35-cp35m-win_amd64.whl
Algorithm	Hash digest
SHA256	`13727dfe2722ab968544ffba529755e4ce6177f0ac32e3e2b7a4935f86b46375`
MD5	`07a4a95de7ccfd917bfdbdd457008cf5`
BLAKE2-256	`5eff9de186d5ae19f6c5eed9b59738f39598e92a7fea7b4aa05a083279e0ce4f`

Hashes for metapy-0.1.3-cp36-cp36m-macosx_10_6_intel.whl

Hashes for metapy-0.1.3-cp36-cp36m-macosx_10_6_intel.whl
Algorithm	Hash digest
SHA256	`1ce6c7bf5f3d5cbf75f2787722870726c10d641cebd13494d8c72834d7fe41ac`
MD5	`5d9672fb53f157340ab0b4e7866fb87a`
BLAKE2-256	`e6f530e54f4a3653a4854ca0397a57a737776cb81f0d2a08126e6b54e4649595`

Hashes for metapy-0.1.3-cp36-cp36m-manylinux1_x86_64.whl

Hashes for metapy-0.1.3-cp36-cp36m-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`a477e2b1d520eb36b2233e4fb1215dbef9c908c7afbe2cb40453e501213b3409`
MD5	`bad4e1071288c554b0ab4e35aa1f11cb`
BLAKE2-256	`e9bd328a1d08cfa6ec0af11ad4636bb500e0d78fbbb7e0a7951d93d76f849eeb`

Hashes for metapy-0.1.3-cp36-cp36m-win_amd64.whl

Hashes for metapy-0.1.3-cp36-cp36m-win_amd64.whl
Algorithm	Hash digest
SHA256	`614cddf01d0e608ebe2f6ed020517bc1d24d207043e56e8c759d2ff1a9c71f53`
MD5	`158ecbe88f1d6a740b803501d932930d`
BLAKE2-256	`03d29f6f1f33a4abeb74907c92ca28de64c34a7ff83556e509e58956e964e79f`

Hashes for metapy-0.1.3.tar.gz

Hashes for metapy-0.1.3.tar.gz
Algorithm	Hash digest
SHA256	`eb43fe6c2801e4d6fee7436e4fa17f0ba549667613d9dd5d2b797da27b0c3b35`
MD5	`c932ac75920c6d2525664c2f1570725f`
BLAKE2-256	`f78c52fbb9d4933f41c14b207577bd29055a8f04692aa0bae2c9e5586f085fb7`