Probably Approximately a Scientific Blog

JANUARY 12, 2021

The release of Google Translate’s neural models in 2016 reported large performance improvements: “60% reduction in translation errors on several popular language pairs”. BERT likely didn't see enough sentences discussing the color of a dove, thus it defaults to just predicting any color. Using the AllenNLP demo. Is it still useful?

Natural Language Processing 52

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AssemblyAI

NOVEMBER 8, 2023

We’ve used the DistilBertTokenizer , which inherits from the BERT WordPiece tokenization scheme. 2016 (ACL2016) model the Truecasing task through a Sequence Tagging approach performed at the character level. 2016 is still at the forefront of the SOTA models. Training Data : We trained this neural network on a total of 3.7

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Snorkel AI

MARCH 1, 2023

BERT BERT, an acronym that stands for “Bidirectional Encoder Representations from Transformers,” was one of the first foundation models and pre-dated the term by several years. BERT proved useful in several ways, including quantifying sentiment and predicting the words likely to follow in unfinished sentences.

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Sebastian Ruder

DECEMBER 6, 2021

Reading Comprehension assumes a gold paragraph is provided Standard approaches for reading comprehension build on pre-trained models such as BERT. Using BERT for reading comprehension involves fine-tuning it to predict a) whether a question is answerable and b) whether each token is the start and end of an answer span.

BERT 52

BERT NLP Natural Language Processing Computational Linguistics 52

Sebastian Ruder

OCTOBER 26, 2019

In particular, I cover unsupervised deep multilingual models such as multilingual BERT. 2016 ; Eger et al., 2016 ; Lample et al., Mapping-based methods have also recently been applied to BERT-based representations ( Anonymous et al., Why not Machine Translation? 2018 ; Artetxe et al., 2015 , Artetxe et al.,

BERT 52

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Sebastian Ruder

AUGUST 18, 2019

In contrast, current models like BERT-Large and GPT-2 consist of 24 Transformer blocks and recent models are even deeper. The latter in particular finds that simply training BERT for longer and on more data improves results, while GPT-2 8B reduces perplexity on a language modelling dataset (though only by a comparatively small factor).

NLP 75

NLP BERT Natural Language Processing Computational Linguistics 75

Lexalytics

APRIL 5, 2021

Around this time (early 2016), our management team realized that to maintain relevance as a company, we would need to be able to incorporate even more ML into our product. The base model of BERT [ 103 ] had 12 (!) And what’s more, Google made BERT publicly available, so that everyone could have access to contextual word vectors.

Neural Network 52

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Dlabs.ai

AUGUST 10, 2020

One of the first widely discussed chatbots was the one deployed by SkyScanner in 2016. The solution is based on a Transformer-type neural network, used in the BERT model as well, that has recently triumphed in the field of machine learning and natural language understanding.

Natural Language Processing 52

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Sebastian Ruder

JANUARY 24, 2022

6] such as W2v-BERT [7] as well as more powerful multilingual models such as XLS-R [8]. For each input chunk, nearest neighbor chunks are retrieved using approximate nearest neighbor search based on BERT embedding similarity. W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training.

NLP 52

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Unite.AI

SEPTEMBER 20, 2023

Hugging Face , started in 2016, aims to make NLP models accessible to everyone. It is based in New York and was founded in 2016." Post-Processor : Enhances construction features to facilitate compatibility with many transformer-based models, like BERT, by adding tokens such as [CLS] and [SEP].

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Sebastian Ruder

JANUARY 19, 2021

2016 ; Webster et al., A plethora of language-specific BERT models have been trained for languages beyond English such as AraBERT ( Antoun et al., This is similar to findings for distilling an inductive bias into BERT ( Kuncoro et al., 2020 ; Wallace et al., 2020 ; Carlini et al., 2020 )—see (Sun et al.,

NLP 52

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