A fast and easy implementation of Transformer with PyTorch

FasySeq

FasySeq is a shorthand as a Fast and easy sequential modeling toolkit. It aims to provide a seq2seq model to researchers and developers, which can be trained efficiently and modified easily. This toolkit is based on Transformer(Vaswani et al.), and will add more seq2seq models in the future.

Dependency

PyTorch >= 1.4
NLTK

Result

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Structure

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To Be Updated

top-k and top-p sampling
multi-GPU inference
length penalty in beam search
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Preprocess

Build Vocabulary

createVocab.py

NamedArguments Description

-f/–file The files used to build the vocabulary.
Type: List

–vocab_num The maximum size of vocabulary, the excess word will be discard according to the frequency.
Type: Int Default: -1

–min_freq

NamedArguments	Description
-f/–file	The files used to build the vocabulary. `Type: List`
–vocab_num	The maximum size of vocabulary, the excess word will be discard according to the frequency. `Type: Int` `Default: -1`
–min_freq	The minimum frequency of token in vocabulary. To finish reading, please visit source site Categories Categories Search for: Recent Posts Basic Data Types in Python: A Quick Exploration What’s Your Story: Jacki O’Neill Research Focus: Week of May 13, 2024 Quiz: What Are CRUD Operations? Python’s Built-in Exceptions: A Walkthrough With Examples Tags algorithms Attention blogathon Calculus Command-line Tools Data Preparation data science data visualization Deep Learning Deep Learning for Computer Vision Deep Learning for Natural Language Processing Deep Learning for Time Series Deep Learning Performance Deep Learning with PyTorch Ensemble Learning Generative Adversarial Networks Imbalanced Classification Linear Algebra Long Short-Term Memory Networks machine learning Machine Learning Algorithms Machine Learning Process Machine Learning Resources machine translation Matplotlib Natural language processing Natural Language Processing & Speech Neural MT nlp NMT opencv Optimization pandas Probability python Python for Machine Learning Python Machine Learning R Machine Learning scikit-learn sentiment analysis Start Machine Learning Statistics Time Series Weka Machine Learning XGBoost Categories Categories Archives Archives Powered by WordPress and Rubine.

The minimum frequency of token in vocabulary.

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