Mlearning.ai

APRIL 8, 2023

Popular Examples include the Bidirectional Encoder Representations from Transformers (BERT) model and the Generative Pre-trained Transformer 3 (GPT-3) model. 2017) “ BERT: Pre-training of deep bidirectional transformers for language understanding ” by Devlin et al. 2020) “GPT-4 Technical report ” by Open AI.

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Towards AI

JULY 21, 2023

Consider which are specific to the language you are studying and which might be more general. Language diversity Estimate the language diversity of the sample of languages you are studying (Ponti et al., Research Work on methods that address the challenges of low-resource languages.

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Sebastian Ruder

JANUARY 19, 2021

2020 ), Turing-NLG , BST ( Roller et al., 2020 ), and GPT-3 ( Brown et al., 2020 ; Fan et al., 2020 ), quantization ( Fan et al., 2020 ), and compression ( Xu et al., 2020 ; Fan et al., 2020 ), quantization ( Fan et al., 2020 ), and compression ( Xu et al., 2020 ) and Big Bird ( Zaheer et al.,

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Towards AI

JULY 20, 2023

BioBERT and similar BERT-based NER models are trained and fine-tuned using a biomedical corpus (or dataset) such as NCBI Disease, BC5CDR, or Species-800. Data formats for inputting data into NER models typically include Pandas DataFrame or text files in CoNLL format (ie. a text file with one word per line).

Data Extraction 82

Data Extraction NLP Natural Language Processing Automation 82

O'Reilly Media

APRIL 23, 2024

They published the original Transformer paper (not quite coincidentally called “Attention is All You Need”) in 2017, and released BERT , an open source implementation, in late 2018, but they never went so far as to build and release anything like OpenAI’s GPT line of services. Will History Repeat Itself?

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IBM Journey to AI blog

JULY 19, 2023

The burden from growing event volumes is reflected in budgets that are expected to grow from an estimated USD 4 billion in 2017 to over 6 billion by 2020. Regardless of volumes, companies must report these events rapidly to regulators and act quickly on safety signals.

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John Snow Labs

MAY 15, 2023

According to [ Ribeiro 2020 ], the sentiment analysis services of the top three cloud providers fail 9-16% of the time when replacing neutral words, and 7-20% of the time when changing neutral named entities. However, today there is a gap between these principles and current state-of-the-art NLP models.

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NLP Software Engineer Data Scientist Responsible AI 52

Heartbeat

JUNE 6, 2023

References Paperwithcode | Graph Convolutional Network Kai, S., Richong, Z., Yongyi, M., & Xudong L. Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree Lu, Z., & Nie, JY.

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Probably Approximately a Scientific Blog

JANUARY 12, 2021

This long-overdue blog post is based on the Commonsense Tutorial taught by Maarten Sap, Antoine Bosselut, Yejin Choi, Dan Roth, and myself at ACL 2020. Here, BERT has seen in its training corpus enough sentences of the type "The color of something is [color]" to know to suggest different colors as substitutes for the masked word.

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Sebastian Ruder

NOVEMBER 14, 2022

Research models such as BERT and T5 have become much more accessible while the latest generation of language and multi-modal models are demonstrating increasingly powerful capabilities. 2020) (Ahia et al., Models that allow interaction via natural language have become ubiquitious. The development cycle of a language model.

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Snorkel AI

MAY 25, 2023

BERT, the first breakout large language model In 2019, a team of researchers at Goole introduced BERT (which stands for bidirectional encoder representations from transformers). By making BERT bidirectional, it allowed the inputs and outputs to take each others’ context into account. Next, OpenAI released GPT-3 in June of 2020.

Large Language Models 52

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Snorkel AI

MAY 25, 2023

BERT, the first breakout large language model In 2019, a team of researchers at Goole introduced BERT (which stands for bidirectional encoder representations from transformers). By making BERT bidirectional, it allowed the inputs and outputs to take each others’ context into account. Next, OpenAI released GPT-3 in June of 2020.

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Large Language Models BERT Neural Network LLM 52

Applied Data Science

DECEMBER 23, 2022

In our review of 2019 we talked a lot about reinforcement learning and Generative Adversarial Networks (GANs), in 2020 we focused on Natural Language Processing (NLP) and algorithmic bias, in 202 1 Transformers stole the spotlight. Just wait until you hear what happened in 2022. What happened?

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Sebastian Ruder

JANUARY 6, 2020

 Unsupervised pretraining was prevalent in NLP this year, mainly driven by BERT ( Devlin et al., A whole range of BERT variants have been applied to multimodal settings, mostly involving images and videos together with text (for an example see the figure below). 2019 ; Tran, 2020 ), which can be seen below. 2019 ; Wu et al.,

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Sebastian Ruder

FEBRUARY 24, 2021

2020 ), they are still poorly equipped to deal with data that is substantially different from the one they have been pre-trained on. 2020) show that adapting to data of the target domain and target task are complementary. 2020) proposed language-adaptive fine-tuning to adapt a model to new languages. 2020 ; Phang et al.,

Natural Language Processing 52

Natural Language Processing BERT NLP Computer Vision 52

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

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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ML Review

DECEMBER 16, 2019

Hwang et a l present SQLova, a semantic parsing model that uses BERT to encode questions and table headers, and attention-based decoder that produces SQL constructs (for instance, SELECTs, WHERE clauses, aggregation functions, etc) that are later ranked and evaluated. Thank you for reading!

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The Stanford AI Lab Blog

MAY 31, 2022

Language Model Pretraining Language models (LMs), like BERT 1 and the GPT series 2 , achieve remarkable performance on many natural language processing (NLP) tasks. To achieve this, we first chunk each document into segments of roughly 256 tokens, which is half of the maximum BERT LM input length.

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Allen AI

NOVEMBER 27, 2023

Transformer models like BERT , which are pre-trained on large quantities of text, are the go-to approach these days for embedding text in a semantic space. SPECTER, released in 2020, supplies embeddings for a variety of our offerings at Semantic Scholar  — user research feeds , author name disambiguation , paper clustering , and many more!

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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. Advances in Neural Information Processing Systems, 2020. What happened?   Why is it important?  

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NLP ML BERT Computational Linguistics 52

AssemblyAI

NOVEMBER 8, 2023

We’ve used the DistilBertTokenizer , which inherits from the BERT WordPiece tokenization scheme. 2020 (AAAI2020) perform Truecasing as an auxiliary task supporting the main NER one. Training Data : We trained this neural network on a total of 3.7 billion words). Susanto et al., This accounts for mixed-case words. Mayhew et al.,

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Explosion

DECEMBER 28, 2019

The update fixed outstanding bugs on the tracker, gave the docs a huge makeover, improved both speed and accuracy, made installation significantly easier and faster, and added some exciting new features, like ULMFit/BERT/ELMo-style language model pretraining. Dec 9: Ines’ key thoughts on trends in AI from 2019 and looking into 2020.

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Viso.ai

FEBRUARY 25, 2023

No 2018 Oct BERT Pre-trained transformer models started dominating the NLP field. No 2020 May DETR DETR is a simple yet effective framework for high-level vision that views object detection as a direct set prediction problem. No 2020 Jul iGPT The transformer model, originally developed for NLP, can also be used for image pre-training.

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Natural Language Processing 52

The Stanford AI Lab Blog

JANUARY 21, 2022

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. RoBERTa: A Robustly Optimized BERT Pretraining Approach. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. . ↩ Devlin, J., Toutanova, K. arXiv preprint arXiv:1810.04805. ↩ Liu, Y., Goyal, N., Stoyanov, V.

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Lexalytics

APRIL 5, 2021

The base model of BERT [ 103 ] had 12 (!) If you gave BERT a chunk of input text, it produced word vectors that encoded each word’s context, so that now it was finally possible to disambiguate “bank” (the financial institution) from “bank” (the edge of a river). BERT is just too good not to use. Howard and S.

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing BERT 52

Dlabs.ai

AUGUST 10, 2020

Despite 80% of surveyed businesses wanting to use chatbots in 2020 , how many do you think will implement them well? 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.

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Natural Language Processing Chatbots Neural Network Machine Learning 52

Explosion

JANUARY 31, 2021

Named Entity Recognition System OntoNotes CoNLL ‘03 spaCy RoBERTa (2020) 89.7 de_dep_news_trf German bert-base-german-cased 99.0 95.8 - es_dep_news_trf Spanish bert-base-spanish-wwm-cased 98.2 94.4 - zh_core_web_trf Chinese bert-base-chinese 92.5 Stanza (StanfordNLP) 1 88.8 Flair 2 89.7 and CoNLL-2003 corpora.

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Topbots

JUNE 15, 2023

Are All Languages Created Equal in Multilingual BERT? Advances in neural information processing systems 33 (2020): 1877–1901. In Findings of the Association for Computational Linguistics: ACL 2022 , pages 2340–2354, Dublin, Ireland. Association for Computational Linguistics. Shijie Wu and Mark Dredze. Brown, Tom, et al.

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Mlearning.ai

JANUARY 17, 2024

Major milestones in the last few years comprised BERT (Google, 2018), GPT-3 (OpenAI, 2020), Dall-E (OpenAI, 2021), Stable Diffusion (Stability AI, LMU Munich, 2022), ChatGPT (OpenAI, 2022).

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deepsense.ai

NOVEMBER 14, 2023

2023 [link] [link] [link] BERTScore: Evaluating text generation with BERT , Zhang T., 2020 Better automatic evaluation of open-domain dialogue systems with contextualized embeddings , Ghazarian S., 2020 Understanding interobserver agreement: The kappa statistic , Viera A.J., Garrido-Merchán E.C., Hertzmann A., Kishore V.,

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Generative AI ML Prompt Engineer Prompt Engineering 83

Explosion

MAY 17, 2023

In their experiments, OpenAI prompted GPT3 with 32 examples of each task, and found that they were able to achieve similar accuracy to the BERT baselines. OpenAI evaluated GPT-3’s in-context learning capabilities against supervised learning in a variety of configurations (Brown et al., The results in Section 3.7,

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LLM NLP Large Language Models OpenAI 135

Snorkel AI

APRIL 11, 2023

First, “Selection via Proxy,” which appeared in ICLR 2020. And please see our work, our paper “Selection via Proxy” from ICLR 2020 for more details on core-set selection, as well as all of the other datasets and methods that we tried there. I was super fortunate to work with amazing researchers from Stanford on this.

Deep Learning 52

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Snorkel AI

APRIL 11, 2023

First, “Selection via Proxy,” which appeared in ICLR 2020. And please see our work, our paper “Selection via Proxy” from ICLR 2020 for more details on core-set selection, as well as all of the other datasets and methods that we tried there. I was super fortunate to work with amazing researchers from Stanford on this.

Deep Learning 52

Deep Learning Algorithm BERT ML 52

Snorkel AI

APRIL 11, 2023

First, “Selection via Proxy,” which appeared in ICLR 2020. And please see our work, our paper “Selection via Proxy” from ICLR 2020 for more details on core-set selection, as well as all of the other datasets and methods that we tried there. I was super fortunate to work with amazing researchers from Stanford on this.

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Deep Learning Algorithm BERT ML 52

Applied Data Science

MARCH 11, 2022

Language is an abundant resource: petabytes of human-produced data on the internet have been put to use to train huge language models such as GPT-3 and Google BERT. Techniques developed in NLP, such as the Transformer architecture, are useful in very diverse fields such as computer vision and reinforcement learning. The topic is in red.

Neural Network 52

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Jay Alammar

DECEMBER 16, 2020

Retrieved from [link] BibTex: @misc{alammar2020explaining, title={Interfaces for Explaining Transformer Language Models}, author={Alammar, J}, year={2020}, url={[link] } Our understanding of why these models work so well, however, still lags behind these developments. Interfaces for Explaining Transformer Language Models [Blog post].

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