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