Smaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT
Blog by Victor Sanh : https://medium.com/huggingface/distilbert-8cf3380435b5
#MachineLearning #NLP #Bert #Distillation #Transformers

Smaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT

Blog by Victor Sanh : https://medium.com/huggingface/distilbert-8cf3380435b5

#MachineLearning #NLP #Bert #Distillation #Transformers

Most common libraries for Natural Language Processing:

CoreNLP from Stanford group:
https://stanfordnlp.github.io/CoreNLP/index.html

NLTK, the most widely-mentioned NLP library for Python:
https://www.nltk.org/

TextBlob, a user-friendly and intuitive NLTK interface:
https://textblob.readthedocs.io/en/dev/index.html

Gensim, a library for document similarity analysis:
https://radimrehurek.com/gensim/

SpaCy, an industrial-strength NLP library built for performance:
https://spacy.io/docs/

Source: https://itsvit.com/blog/5-heroic-tools-natural-language-processing/

#nlp #digest #libs

Write With Transformer
Hugging Face released a new version of their Write With Transformer app, using a language model trained directly on Arxiv to generate Deep Learning and NLP completions!
In addition, they add state-of-the-art NLP models such as GPT, GPT-2 and XLNet completions:

https://transformer.huggingface.co/

H / T : Lysandre Debut
#Transformer #Pytorch #NLP

@ArtificialIntelligenceArticles

Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets
Geva et al.: https://arxiv.org/abs/1908.07898
#ArtificialIntelligence #MachineLearning #NLP

What Kind of Language Is Hard to Language-Model?
Mielke et al.: https://arxiv.org/abs/1906.04726
#ArtificialIntelligence #MachineLearning #NLP

Transformers: State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch
By 🤗 Hugging Face : https://huggingface.co/transformers
#Transformers #MachineLearning #NLP

Extreme Language Model Compression with Optimal Subwords and Shared Projections
Zhao et al.: https://arxiv.org/abs/1909.11687
#neuralnetwork #bert #nlp

The State of Transfer Learning in NLP
By Sebastian Ruder : https://ruder.io/state-of-transfer-learning-in-nlp/
#TransferLearning #NaturalLanguageProcessing #NLP

Q8BERT: Quantized 8Bit BERT
Zafrir et al.: https://arxiv.org/abs/1910.06188
#NaturalLanguageProcessing #NLP #Transformer

News classification using classic Machine Learning tools (TF-IDF) and modern NLP approach based on transfer learning (ULMFIT) deployed on GCP

By Imad El Hanafi

Live version: https://nlp.imadelhanafi.com/

Github: https://github.com/imadelh/NLP-news-classification

Blog: https://imadelhanafi.com/posts/text_classification_ulmfit/

#DeepLearning #MachineLearning #NLP

Specializing Word Embeddings (for Parsing) by Information Bottleneck
Li et al.: https://www.aclweb.org/anthology/D19-1276.pdf
#ArtificialIntelligence #MachineLearning #NLP

The Illustrated GPT-2 (Visualizing Transformer Language Models)
Blog by Jay Alammar : https://jalammar.github.io/illustrated-gpt2/
#ArtificialIntelligence #NLP #UnsupervisedLearning

Single Headed Attention RNN: Stop Thinking With Your Head
Stephen Merity : https://arxiv.org/abs/1911.11423
#ArtificialIntelligence #NeuralComputing #NLP

A tutorial to implement state-of-the-art NLP models with Fastai for Sentiment Analysis
Maximilien Roberti : https://towardsdatascience.com/fastai-with-transformers-bert-roberta-xlnet-xlm-distilbert-4f41ee18ecb2
#FastAI #NLP #Transformers

Single Headed Attention RNN: Stop Thinking With Your Head
Stephen Merity : https://arxiv.org/abs/1911.11423
#ArtificialIntelligence #NeuralComputing #NLP

GitHub Typo Corpus: A Large-Scale Multilingual Dataset of Misspellings and Grammatical Errors
Masato Hagiwara, Masato Mita : https://arxiv.org/abs/1911.12893
Code & Dataset https://github.com/mhagiwara/github-typo-corpus
#ArtificialIntelligence #DeepLearning #NLP

Major trends in #NLP : a review of 20 years of #ACL research

The 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) is starting this week in Florence, Italy. We took the opportunity to review major research trends in the animated NLP space and formulate some implications from the business perspective. The article is backed by a statistical and — guess what — NLP-based analysis of ACL papers from the last 20 years

https://towardsdatascience.com/major-trends-in-nlp-a-review-of-20-years-of-acl-research-56f5520d473

Machine Learning on Graphs #NeurIPS2019
Michael Galkin : https://medium.com/@mgalkin/machine-learning-on-graphs-neurips-2019-875eecd41069
#GraphNeuralNetworks #NLP