Using BART (sentence summary model) with hugging face

 Posted on Tue Jan 19 2021  |  2 minutes  |  345 words  |  近藤綾乃

BART is a model for document summarization Derived from the same transformer as BERT Unlike BERT, it has an encoder-decoder structure This is because it is intended for sentence generation This page shows the steps to run a tutorial on BART. Procedure install transformers Run ``sh pip install transformers Run summary 2. Run the summary from transformers import BartTokenizer, BartForConditionalGeneration, BartConfig model = BartForConditionalGeneration.from_pretrained('facebook/bart-large') tokenizer = BartTokenizer.from_pretrained('facebook/bart-large') ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs. [Read More]
engineering  natural language processing  python  technology  distributed representation  sentence generation 

Procedure for obtaining a distributed representation of a Japanese sentence using a trained Universal Sentence Encoder

 Posted on Mon Jun 22 2020  |  2 minutes  |  405 words  |  近藤綾乃

. A vector of documents can be obtained using Universal Sentence Encoder. Features Supports multiple languages. Japanese is supported. Can handle Japanese sentences as vectors. Usage Clustering, similarity calculation, feature extraction. Usage Execute the following command as preparation. pip install tensorflow tensorflow_hub tensorflow_text numpy Trained models are available. See the python description below for details on how to use it. import tensorflow_hub as hub import tensorflow_text import numpy as np # for avoiding error import ssl ssl. [Read More]
technology  natural language processing  universal sentence encoder  technology  python  distributed representation 

A note on how to use BERT learned from Japanese Wikipedia, now available

 Posted on Wed Jun 17 2020  |  1 minutes  |  472 words  |  近藤綾乃

huggingface has released a Japanese model for BERT. The Japanese model is included in transformers. However, I stumbled over a few things before I could get it to actually work in a Mac environment, so I’ll leave a note. Preliminaries: Installing mecab The morphological analysis engine, mecab, is required to use BERT’s Japanese model. The tokenizer will probably ask for mecab. This time, we will use homebrew to install Mecab [Read More]
technical  natural language processing  bert  technology  python  distributed representation