readme: ---
  language: 
    - ja
  license: cc-by-sa-3.0
  library_name: transformers
  tags:
  - fastText
  pipeline_tag: zero-shot-classification
  widget:
  - text: "海賊王におれはなる"
    candidate_labels: "海, 山, 陸"
    multi_class: true
    example_title: "ワンピース"
  ---
  
  # fasttext-classification
  **This model is experimental.**
  
  fastText word vector base classification
  
  ## Usage
  Google Colaboratory Example
  ```
  ! apt install aptitude swig > /dev/null 
  ! aptitude install mecab libmecab-dev mecab-ipadic-utf8 git make curl xz-utils file -y > /dev/null 
  ! pip install transformers torch mecab-python3 torchtyping > /dev/null 
  ! ln -s /etc/mecabrc /usr/local/etc/mecabrc
  ```
  ```
  from transformers import pipeline
  p = pipeline("zero-shot-classification", "paulhindemith/fasttext-classification", revision="2022.11.13", trust_remote_code=True)
  ```
  ```
  p("海賊王におれはなる", candidate_labels=["海","山","陸"], hypothesis_template="{}", multi_label=True)
  ```
  
  ## License
  This model utilizes the folllowing pretrained vectors.
  
  Name: fastText  
  Credit: https://fasttext.cc/  
  License: [Creative Commons Attribution-Share-Alike License 3.0](https://creativecommons.org/licenses/by-sa/3.0/)  
  Link: https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.ja.vec 