Gergely Dániel Németh

Gergely Dániel Németh

PhD student at the ELLIS Unit Alicante. Working on AI Fairness in Federated Learning.

Posts by Tags

Computer Vision

Natural Language Processing

BERT Visualization in Embedding Projector

less than 1 minute read

Published:

This story shows how to visualize pre-trained BERT embeddings in Tensorflow’s Tensorboard Embedding Projector. The story uses around 50 unique sentences and their BERT embeddings generated with TensorFlow Hub BERT models. See full article here

Simple BERT using TensorFlow 2.0

less than 1 minute read

Published:

This story shows a simple usage of the BERT [1] embedding using TensorFlow 2.0. As TensorFlow 2.0 has been released recently, the module aims to use easy, ready-to-use models based on the high-level Keras API. The previous usage of BERT was described in a long Notebook implementing a Movie Review prediction. In this story, we will see a simple BERT embedding generator using Keras and the latest TensorFlow and TensorFlow Hub modules. All codes are available on Google Colab. See full article here

Machine Translation: Compare to SOTA

less than 1 minute read

Published:

My previous story describes BLEU as the most used metric for Machine Translation (MT). This one aims to introduce the Conferences, Datasets and Competitions where you can compare your models with the State-of-the-art, you can collect knowledge from and where you can meet researchers from the field. See full article here

Identifying the right meaning of the words using BERT

less than 1 minute read

Published:

An important reason for using contextualised word embeddings is that the standard embeddings assign one vector for every meaning of a word, however, there are multiple-meaning words. The hypothesis is that the use of the context can solve the problem of categorizing multiple-meaning words (homonyms and homographs) into the same embedding vector. In this story, we will analyse whether BERT embeddings can be used to classify different meanings of a word to prove that contextualised word embeddings solve the problem. See full article here

Machine Translation: A Short Overview

less than 1 minute read

Published:

This story is an overview of the field of Machine Translation. The story introduces several highly cited literature and famous applications, but I’d like to encourage you to share your opinion in the comments. The aim of this story is to provide a good start for someone new to the field. It covers the three main approaches of machine translation as well as several challenges of the field. Hopefully, the literature mentioned in the story presents the history of the problem as well as the state-of-the-art solutions. See full article here

Visualisation of embedding relations (word2vec, BERT)

less than 1 minute read

Published:

In this story, we will visualise the word embedding vectors to understand the relations between words described by the embeddings. This story focuses on word2vec [1] and BERT [2]. To understand the embeddings, I suggest reading a different introduction as this story does not aim to describe them. See full article here

augmentation

bert

BERT Visualization in Embedding Projector

less than 1 minute read

Published:

This story shows how to visualize pre-trained BERT embeddings in Tensorflow’s Tensorboard Embedding Projector. The story uses around 50 unique sentences and their BERT embeddings generated with TensorFlow Hub BERT models. See full article here

Simple BERT using TensorFlow 2.0

less than 1 minute read

Published:

This story shows a simple usage of the BERT [1] embedding using TensorFlow 2.0. As TensorFlow 2.0 has been released recently, the module aims to use easy, ready-to-use models based on the high-level Keras API. The previous usage of BERT was described in a long Notebook implementing a Movie Review prediction. In this story, we will see a simple BERT embedding generator using Keras and the latest TensorFlow and TensorFlow Hub modules. All codes are available on Google Colab. See full article here

Identifying the right meaning of the words using BERT

less than 1 minute read

Published:

An important reason for using contextualised word embeddings is that the standard embeddings assign one vector for every meaning of a word, however, there are multiple-meaning words. The hypothesis is that the use of the context can solve the problem of categorizing multiple-meaning words (homonyms and homographs) into the same embedding vector. In this story, we will analyse whether BERT embeddings can be used to classify different meanings of a word to prove that contextualised word embeddings solve the problem. See full article here

Visualisation of embedding relations (word2vec, BERT)

less than 1 minute read

Published:

In this story, we will visualise the word embedding vectors to understand the relations between words described by the embeddings. This story focuses on word2vec [1] and BERT [2]. To understand the embeddings, I suggest reading a different introduction as this story does not aim to describe them. See full article here

bleu

classification

custom layer

gpt

keras

linear transformation

machine translation

Machine Translation: Compare to SOTA

less than 1 minute read

Published:

My previous story describes BLEU as the most used metric for Machine Translation (MT). This one aims to introduce the Conferences, Datasets and Competitions where you can compare your models with the State-of-the-art, you can collect knowledge from and where you can meet researchers from the field. See full article here

Machine Translation: A Short Overview

less than 1 minute read

Published:

This story is an overview of the field of Machine Translation. The story introduces several highly cited literature and famous applications, but I’d like to encourage you to share your opinion in the comments. The aim of this story is to provide a good start for someone new to the field. It covers the three main approaches of machine translation as well as several challenges of the field. Hopefully, the literature mentioned in the story presents the history of the problem as well as the state-of-the-art solutions. See full article here

math

medium

numpy

overview

Machine Translation: Compare to SOTA

less than 1 minute read

Published:

My previous story describes BLEU as the most used metric for Machine Translation (MT). This one aims to introduce the Conferences, Datasets and Competitions where you can compare your models with the State-of-the-art, you can collect knowledge from and where you can meet researchers from the field. See full article here

Machine Translation: A Short Overview

less than 1 minute read

Published:

This story is an overview of the field of Machine Translation. The story introduces several highly cited literature and famous applications, but I’d like to encourage you to share your opinion in the comments. The aim of this story is to provide a good start for someone new to the field. It covers the three main approaches of machine translation as well as several challenges of the field. Hopefully, the literature mentioned in the story presents the history of the problem as well as the state-of-the-art solutions. See full article here

quickcode

tensorflow

Simple BERT using TensorFlow 2.0

less than 1 minute read

Published:

This story shows a simple usage of the BERT [1] embedding using TensorFlow 2.0. As TensorFlow 2.0 has been released recently, the module aims to use easy, ready-to-use models based on the high-level Keras API. The previous usage of BERT was described in a long Notebook implementing a Movie Review prediction. In this story, we will see a simple BERT embedding generator using Keras and the latest TensorFlow and TensorFlow Hub modules. All codes are available on Google Colab. See full article here

tokenizers

towardsdatascience

BERT Visualization in Embedding Projector

less than 1 minute read

Published:

This story shows how to visualize pre-trained BERT embeddings in Tensorflow’s Tensorboard Embedding Projector. The story uses around 50 unique sentences and their BERT embeddings generated with TensorFlow Hub BERT models. See full article here

Simple BERT using TensorFlow 2.0

less than 1 minute read

Published:

This story shows a simple usage of the BERT [1] embedding using TensorFlow 2.0. As TensorFlow 2.0 has been released recently, the module aims to use easy, ready-to-use models based on the high-level Keras API. The previous usage of BERT was described in a long Notebook implementing a Movie Review prediction. In this story, we will see a simple BERT embedding generator using Keras and the latest TensorFlow and TensorFlow Hub modules. All codes are available on Google Colab. See full article here

Machine Translation: Compare to SOTA

less than 1 minute read

Published:

My previous story describes BLEU as the most used metric for Machine Translation (MT). This one aims to introduce the Conferences, Datasets and Competitions where you can compare your models with the State-of-the-art, you can collect knowledge from and where you can meet researchers from the field. See full article here

Identifying the right meaning of the words using BERT

less than 1 minute read

Published:

An important reason for using contextualised word embeddings is that the standard embeddings assign one vector for every meaning of a word, however, there are multiple-meaning words. The hypothesis is that the use of the context can solve the problem of categorizing multiple-meaning words (homonyms and homographs) into the same embedding vector. In this story, we will analyse whether BERT embeddings can be used to classify different meanings of a word to prove that contextualised word embeddings solve the problem. See full article here

Machine Translation: A Short Overview

less than 1 minute read

Published:

This story is an overview of the field of Machine Translation. The story introduces several highly cited literature and famous applications, but I’d like to encourage you to share your opinion in the comments. The aim of this story is to provide a good start for someone new to the field. It covers the three main approaches of machine translation as well as several challenges of the field. Hopefully, the literature mentioned in the story presents the history of the problem as well as the state-of-the-art solutions. See full article here

Visualisation of embedding relations (word2vec, BERT)

less than 1 minute read

Published:

In this story, we will visualise the word embedding vectors to understand the relations between words described by the embeddings. This story focuses on word2vec [1] and BERT [2]. To understand the embeddings, I suggest reading a different introduction as this story does not aim to describe them. See full article here

transfromers

visualisation

BERT Visualization in Embedding Projector

less than 1 minute read

Published:

This story shows how to visualize pre-trained BERT embeddings in Tensorflow’s Tensorboard Embedding Projector. The story uses around 50 unique sentences and their BERT embeddings generated with TensorFlow Hub BERT models. See full article here

Visualisation of embedding relations (word2vec, BERT)

less than 1 minute read

Published:

In this story, we will visualise the word embedding vectors to understand the relations between words described by the embeddings. This story focuses on word2vec [1] and BERT [2]. To understand the embeddings, I suggest reading a different introduction as this story does not aim to describe them. See full article here

word meaning

Identifying the right meaning of the words using BERT

less than 1 minute read

Published:

An important reason for using contextualised word embeddings is that the standard embeddings assign one vector for every meaning of a word, however, there are multiple-meaning words. The hypothesis is that the use of the context can solve the problem of categorizing multiple-meaning words (homonyms and homographs) into the same embedding vector. In this story, we will analyse whether BERT embeddings can be used to classify different meanings of a word to prove that contextualised word embeddings solve the problem. See full article here

writing