Add dependency versions
{% set name = "natasha" %}
{% set version = "1.3.0" %}
+
package:
name: {{ name|lower }}
version: {{ version }}
source:
- url: https://pypi.io/packages/source/{{ name[0] }}/{{ name }}/{{ name }}-{{ version }}.tar.gz
+ url: https://pypi.io/packages/source/{{ name[0] }}/{{ name }}/natasha-{{ version }}.tar.gz
sha256: 0f174b1b3cde4d31699e212f2ff55617a2894147b1092549cf2bef26315d4e51
build:
number: 0
- script: "{{ PYTHON }} -m pip install . -vv"
+ noarch: python
+ script: {{ PYTHON }} -m pip install . -vv
requirements:
- build:
- - {{ compiler('c') }}
host:
- - python
- pip
- run:
- python
+ run:
+ - ipymarkup >=0.8.0
+ - navec >=0.9.0
- pymorphy2
- - razdel
- - navec
- - slovnet
- - yargy
- - ipymarkup
+ - python
+ - razdel >=0.5.0
+ - slovnet >=0.3.0
+ - yargy >=0.14.0
test:
imports:
@@ -38,15 +38,9 @@ test:
about:
home: https://github.com/natasha/natasha
+ summary: Named-entity recognition for russian language
license: MIT
- license_family: MIT
- license_file: LICENSE.txt
- summary: 'Solves basic Russian NLP tasks, API for lower level Natasha projects'
-
- description: |
- Natasha solves basic NLP tasks for Russian language: tokenization, sentence segmentation, word embedding, morphology tagging, lemmatization, phrase normalization, syntax parsing, NER tagging, fact extraction. Quality on every task is similar or better then current SOTAs for Russian language on news articles, see evaluation section. Natasha is not a research project, underlying technologies are built for production. We pay attention to model size, RAM usage and performance. Models run on CPU, use Numpy for inference.
- doc_url: http://nbviewer.jupyter.org/github/natasha/natasha/blob/master/docs.ipynb
- dev_url: https://github.com/natasha/natasha
+ license_file: LICENSE
extra:
recipe-maintainers:
