readme: ---
  language: 
    - ja
  license: cc-by-sa-3.0
  library_name: transformers
  tags:
  - fastText
  - embedding
  pipeline_tag: feature-extraction
  widget:
  - text: "海賊王におれはなる"
    example_title: "ワンピース"
  
  ---
  
  
  # fasttext-jp-embedding
  **This model is experimental.**
  
  Pretrained FastText word vector for Japanese
  
  ## 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
  import pandas as pd
  import numpy as np 
  
  text = "海賊王におれはなる"
  
  pipeline = pipeline("feature-extraction", model="paulhindemith/fasttext-jp-embedding", revision="2022.11.13", trust_remote_code=True)
  pd.DataFrame(np.array(pipeline(text)).T, columns=pipeline.tokenizer.tokenize(text))
  ```
  
  ```
  pipeline.tokenizer.target_hinshi = ["動詞", "名詞", "形容詞"]
  pd.DataFrame(np.array(pipeline(text)).T, columns=pipeline.tokenizer.tokenize(text))
  ```
  
  ## 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 