Workshops of natural language processing
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Updated
Jan 6, 2021 - Jupyter Notebook
Workshops of natural language processing
pretrained models and a training code for sentencepiece
Tensorflow Model Incorporable Sentencepiece Tokenizer Training Code
Escape unknown symbols in SentecePiece vocabularies
Bengali SentencePiece Model created with wiki dump data.
Automated WikiGame-playing 'bot'. Achieved via SentenceTransformer Word Embeddings.
Unsupervised text tokenizer for Neural Network-based text generation.
An Industry Standard Tokenizer, purposed for large-scale language models like OpenAI's GPT Series.
한글을 영어로 번역하는 자연어처리 모델 스터디입니다.
Sentencepiece Dart is a wrapper for Google's Sentencepiece C++ library modified
Search for similar documents using Elasticsearch and BERT.
NMT with RNN Models: (1) in Vanilla style, (2) with Sentencepiece, (3) using Pre-trained models from FairSeq
Fast and versatile tokenizer for language-models, supporting BPE and Unigram tokenization and usable in native and WASM environments
This repository contains codes related to the experiments in "An Experimental Evaluation of Japanese Tokenizers for Sentiment-Based Text Classification" presented at https://www.anlp.jp/nlp2021/. Authors: Andre Rusli and Makoto Shishido (Tokyo Denki University).
Bengali language Tokenizer (SentencePiece)
dataset, train, inference
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