---
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