<commit_before>{% set name = "bertopic" %}
{% set version = "0.9.4" %}


package:
  name: {{ name|lower }}
  version: {{ version }}

source:
  url: https://pypi.io/packages/source/{{ name[0] }}/{{ name }}/bertopic-{{ version }}.tar.gz
  sha256: 7ffed1e7bb81e6b0f1039141809c9d157fa415d65c298e9887e70b890f274bce

build:
  number: 0
  noarch: python
  script: {{ PYTHON }} -m pip install . -vv

requirements:
  host:
    - pip
    - python >=3.7
  run:
    - hdbscan >=0.8.27
    - numpy >=1.20.0
    - pandas >=1.1.5
    - plotly >=4.7.0
    - python >=3.7
    - pyyaml <6.0
    - scikit-learn >=0.22.2.post1
    - sentence-transformers >=0.4.1
    - tqdm >=4.41.1
    - umap-learn >=0.5.0

test:
  imports:
    - bertopic
    - bertopic.backend
  commands:
    - pip check
  requires:
    - pip

about:
  home: https://github.com/MaartenGr/BERTopic
  summary: BERTopic performs topic Modeling with state-of-the-art transformer models.
  license: MIT
  # TODO: remove license file once bertopic includes it in the source on PyPI.
  # Remove the license file once this issue is closed:
  # - https://github.com/MaartenGr/BERTopic/issues/401
  license_file: LICENSE
  description: |
    BERTopic is a topic modeling technique that leverages 🤗 transformers 
    and c-TF-IDF to create dense clusters allowing for easily interpretable 
    topics whilst keeping important words in the topic descriptions.

    BERTopic supports 
    [**guided**](https://maartengr.github.io/BERTopic/getting_started/guided/guided.html), 
    (semi-) [**supervised**](https://maartengr.github.io/BERTopic/getting_started/supervised/supervised.html), 
    and [**dynamic**](https://maartengr.github.io/BERTopic/getting_started/topicsovertime/topicsovertime.html) 
    topic modeling. It even supports visualizations similar to LDAvis!

    Corresponding medium posts can be found 
    [here](https://towardsdatascience.com/topic-modeling-with-bert-779f7db187e6?source=friends_link&sk=0b5a470c006d1842ad4c8a3057063a99) <commit_msg>updated bertopic recipe - added cython to test:requires<commit_after>{% set name = "bertopic" %}
{% set version = "0.9.4" %}


package:
  name: {{ name|lower }}
  version: {{ version }}

source:
  url: https://pypi.io/packages/source/{{ name[0] }}/{{ name }}/bertopic-{{ version }}.tar.gz
  sha256: 7ffed1e7bb81e6b0f1039141809c9d157fa415d65c298e9887e70b890f274bce

build:
  number: 0
  noarch: python
  script: {{ PYTHON }} -m pip install . -vv

requirements:
  host:
    - pip
    - python >=3.7
    - cython
  run:
    - hdbscan >=0.8.27
    - numpy >=1.20.0
    - pandas >=1.1.5
    - plotly >=4.7.0
    - python >=3.7
    - pyyaml <6.0
    - scikit-learn >=0.22.2.post1
    - sentence-transformers >=0.4.1
    - tqdm >=4.41.1
    - umap-learn >=0.5.0

test:
  imports:
    - bertopic
    - bertopic.backend
  commands:
    - pip check
  requires:
    - pip
    # cython is necessary for 
    # successful pip-check
    - cython

about:
  home: https://github.com/MaartenGr/BERTopic
  summary: BERTopic performs topic Modeling with state-of-the-art transformer models.
  license: MIT
  # TODO: remove license file once bertopic includes it in the source on PyPI.
  # Remove the license file once this issue is closed:
  # - https://github.com/MaartenGr/BERTopic/issues/401
  license_file: LICENSE
  description: |
    BERTopic is a topic modeling technique that leverages 🤗 transformers 
    and c-TF-IDF to create dense clusters allowing for easily interpretable 
    topics whilst keeping important words in the topic descriptions.

    BERTopic supports 
    [**guided**](https://maartengr.github.io/BERTopic/getting_started/guided/guided.html), 
    (semi-) [**supervised**](https://maartengr.github.io/BERTopic/getting_started/supervised/supervised.html), 
    and [**dynamic**](https://maartengr.github.io/BERTopic/getting_started/topicsovertime/topicsovertime.html) 
    topic modeling. It even supports visualizations similar to LDAvis!

    Corresponding medium posts can be found 
    [here](https://towardsdatascience.com/topic-modeling-with-bert-779f7db187e6?source=friends_link&sk=0b5a470c006d1842ad4c8a3057063a99) 
{'fraction_non_alphanumeric': 0.12403288201160542, 'fraction_numerical': 0.05705996131528046, 'mean_word_length': 3.23439099283521, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 23, 'https://': 14, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '743605', 'n_tokens_mistral': 1605, 'n_tokens_neox': 1446, 'n_words': 334}