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---
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 |