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π»More Efficient NLP Model Pre-training with ELECTRA
Recent advances in language pre-training have led to substantial gains in the field of natural language processing, with state-of-the-art models such as BERT, RoBERTa, XLNet, ALBERT, and T5, among many others. These methods, though they differ in design, share the same idea of leveraging a large amount of unlabeled text to build a general model of language understanding before being fine-tuned on specific NLP tasks such as sentiment analysis and question answering.
https://ai.googleblog.com/
#NLP
#deeplearning
#pretraining
@cedeeplearning
Recent advances in language pre-training have led to substantial gains in the field of natural language processing, with state-of-the-art models such as BERT, RoBERTa, XLNet, ALBERT, and T5, among many others. These methods, though they differ in design, share the same idea of leveraging a large amount of unlabeled text to build a general model of language understanding before being fine-tuned on specific NLP tasks such as sentiment analysis and question answering.
https://ai.googleblog.com/
#NLP
#deeplearning
#pretraining
@cedeeplearning
#Torch-Struct: Deep Structured Prediction Library
The literature on structured prediction for #NLP describes a rich collection of distributions and algorithms over #sequences, #segmentations, #alignments, and #trees; however, these algorithms are difficult to utilize in deep learning frameworks. We introduce Torch-Struct, a library for structured prediction designed to take advantage of and integrate with vectorized, auto-differentiation based #frameworks. Torch-Struct includes a broad collection of #probabilistic structures accessed through a simple and flexible distribution-based API that connects to any deep learning model. The library utilizes batched, vectorized operations and exploits auto-differentiation to produce readable, fast, and testable code. Internally, we also include a number of general-purpose optimizations to provide cross-algorithm efficiency. Experiments show significant performance gains over fast baselines and case-studies demonstrate the benefits of the library. Torch-Struct is available at:
Code: https://github.com/harvardnlp/pytorch-struct
Paper: https://arxiv.org/abs/2002.00876v1
@cedeeplearning
The literature on structured prediction for #NLP describes a rich collection of distributions and algorithms over #sequences, #segmentations, #alignments, and #trees; however, these algorithms are difficult to utilize in deep learning frameworks. We introduce Torch-Struct, a library for structured prediction designed to take advantage of and integrate with vectorized, auto-differentiation based #frameworks. Torch-Struct includes a broad collection of #probabilistic structures accessed through a simple and flexible distribution-based API that connects to any deep learning model. The library utilizes batched, vectorized operations and exploits auto-differentiation to produce readable, fast, and testable code. Internally, we also include a number of general-purpose optimizations to provide cross-algorithm efficiency. Experiments show significant performance gains over fast baselines and case-studies demonstrate the benefits of the library. Torch-Struct is available at:
Code: https://github.com/harvardnlp/pytorch-struct
Paper: https://arxiv.org/abs/2002.00876v1
@cedeeplearning
GitHub
GitHub - harvardnlp/pytorch-struct: Fast, general, and tested differentiable structured prediction in PyTorch
Fast, general, and tested differentiable structured prediction in PyTorch - harvardnlp/pytorch-struct
π»Does Deep Learning Come from the Devil?
Deep learning has revolutionized #computer_vision and natural language processing (#NLP). Yet the #mathematics explaining its success remains elusive. At the Yandex conference on machine learning prospects and applications, Vladimir Vapnik offered a critical perspective.
πΉwe suggest you to tap the linkπΉ
link: https://www.kdnuggets.com/2015/10/deep-learning-vapnik-einstein-devil-yandex-conference.html
πVia: @cedeeplearning
#deeplearning
#machinelearning
#neuralnetworks
Deep learning has revolutionized #computer_vision and natural language processing (#NLP). Yet the #mathematics explaining its success remains elusive. At the Yandex conference on machine learning prospects and applications, Vladimir Vapnik offered a critical perspective.
πΉwe suggest you to tap the linkπΉ
link: https://www.kdnuggets.com/2015/10/deep-learning-vapnik-einstein-devil-yandex-conference.html
πVia: @cedeeplearning
#deeplearning
#machinelearning
#neuralnetworks
πΉFinding a good read among billions of choices
As natural language processing techniques improve, suggestions are getting speedier and more relevant. With the MIT-IBM Watson AI Lab and his Geometric Data Processing Group at MIT, Solomon recently presented a new technique for cutting through massive amounts of text at the Conference on Neural Information Processing Systems (NeurIPS). Their method combines three popular text-analysis tools β topic modeling, word embeddings, and optimal transport β to deliver better, faster results than competing methods on a popular benchmark for classifying documents. If an algorithm knows what you liked in the past, it can scan the millions of possibilities for something similar. As natural language processing techniques improve, those βyou might also likeβ suggestions are getting speedier and more relevant.
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link: http://news.mit.edu/2019/finding-good-read-among-billions-of-choices-1220
πVia: @cedeeplearning
#deepelarning
#NLP
#neuralnetworks
As natural language processing techniques improve, suggestions are getting speedier and more relevant. With the MIT-IBM Watson AI Lab and his Geometric Data Processing Group at MIT, Solomon recently presented a new technique for cutting through massive amounts of text at the Conference on Neural Information Processing Systems (NeurIPS). Their method combines three popular text-analysis tools β topic modeling, word embeddings, and optimal transport β to deliver better, faster results than competing methods on a popular benchmark for classifying documents. If an algorithm knows what you liked in the past, it can scan the millions of possibilities for something similar. As natural language processing techniques improve, those βyou might also likeβ suggestions are getting speedier and more relevant.
ββββββββββββββββ
link: http://news.mit.edu/2019/finding-good-read-among-billions-of-choices-1220
πVia: @cedeeplearning
#deepelarning
#NLP
#neuralnetworks
πΉHow Algorithms Can Predict Our Intentions Faster Than We Can
Artificial Intelligence (AI) and Natural Language Processing (NLP) can gather data from anywhere online where we leave a mark. This includes our social media posts, our email, and even any small comments we leave on blog posts. Every trace we leave online allows NLP to track and predict our future decisions.
This article highlight how NLP can impact our day-to-day lives with the use of case studies.
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https://www.entrepreneur.com/article/328776
πVia: @cedeeplearning
#NLP
#AI
#machinelearning
#deeplearning
#algorithm
Artificial Intelligence (AI) and Natural Language Processing (NLP) can gather data from anywhere online where we leave a mark. This includes our social media posts, our email, and even any small comments we leave on blog posts. Every trace we leave online allows NLP to track and predict our future decisions.
This article highlight how NLP can impact our day-to-day lives with the use of case studies.
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https://www.entrepreneur.com/article/328776
πVia: @cedeeplearning
#NLP
#AI
#machinelearning
#deeplearning
#algorithm
Entrepreneur
How Algorithms Can Predict Our Intentions Faster Than We Can
Every trace we leave online allows NLP to track and predict our future decisions.
π»Unlocking potentials of NLP to fight against COVID-19 crisis
DAMOβs existing model has already been deployed widely in Alibabaβs ecosystem, powering its customer-service AI chatbot and the search engine on Alibabaβs retail platforms, as well as anonymous healthcare data analysis. The model was used in the text analysis of medical records and epidemiological investigation by CDCs in different cities in China for fighting against #COVID-19.
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πVia: @cedeeplearning
πOther social media: https://linktr.ee/cedeeplearning
https://www.analyticsinsight.net/unlocking-potentials-nlp-fight-covid-19-crisis/
#machinelearning
#deeplearning
#neuralnetworks
#NLP
DAMOβs existing model has already been deployed widely in Alibabaβs ecosystem, powering its customer-service AI chatbot and the search engine on Alibabaβs retail platforms, as well as anonymous healthcare data analysis. The model was used in the text analysis of medical records and epidemiological investigation by CDCs in different cities in China for fighting against #COVID-19.
βββββββββββββββββ
πVia: @cedeeplearning
πOther social media: https://linktr.ee/cedeeplearning
https://www.analyticsinsight.net/unlocking-potentials-nlp-fight-covid-19-crisis/
#machinelearning
#deeplearning
#neuralnetworks
#NLP
Linktree
cedeeplearning | Instagram, Facebook | Linktree
Linktree. Make your link do more.
πΉBENEFITS OF SPARK NLP
1. Itβs very accurate
2. Reduced training model sizes
3. Itβs fast
4. It is fully supported by Spark
5. It is scalable
6. Extensive functionality and support
7. A large community
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πVia: @cedeeplearning
πSocial media: https://linktr.ee/cedeeplearning
link: https://www.analyticsinsight.net/benefits-of-spark-nlp/
#spark
#NLP
#deeplearning
#neuralnetworks
1. Itβs very accurate
2. Reduced training model sizes
3. Itβs fast
4. It is fully supported by Spark
5. It is scalable
6. Extensive functionality and support
7. A large community
ββββββββββββββ
πVia: @cedeeplearning
πSocial media: https://linktr.ee/cedeeplearning
link: https://www.analyticsinsight.net/benefits-of-spark-nlp/
#spark
#NLP
#deeplearning
#neuralnetworks
π»HOW TO SOLVE 90% OF NLP PROBLEMS: A STEP-BY-STEP GUIDE
πΉby Emmanuel Ameisen
Whether you are an established company or working to launch a new service, you can always leverage text data to validate, improve, and expand the functionalities of your product. The science of extracting meaning and learning from text data is an active topic of research called Natural Language Processing (#NLP).
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πVia: @cedeeplearning
https://www.topbots.com/solve-ai-nlp-problems-guide/
#deeplearning
#neuralnetworks
#machinelearning
#text_data
#datascience
πΉby Emmanuel Ameisen
Whether you are an established company or working to launch a new service, you can always leverage text data to validate, improve, and expand the functionalities of your product. The science of extracting meaning and learning from text data is an active topic of research called Natural Language Processing (#NLP).
ββββββββββββ
πVia: @cedeeplearning
https://www.topbots.com/solve-ai-nlp-problems-guide/
#deeplearning
#neuralnetworks
#machinelearning
#text_data
#datascience
NLP.pdf
701.7 KB
πΉ A Primer on Neural Network Models
for Natural Language Processing
This tutorial covers input encoding for natural language tasks, feed-forward networks, convolutional networks, recurrent networks and recursive networks, as well as the computation graph abstraction for automatic gradient computation.
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πVia: @cedeeplearning
#deeplearning #CNN #tutorial
#neuralnetworks #RNN #paper #nlp
for Natural Language Processing
This tutorial covers input encoding for natural language tasks, feed-forward networks, convolutional networks, recurrent networks and recursive networks, as well as the computation graph abstraction for automatic gradient computation.
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πVia: @cedeeplearning
#deeplearning #CNN #tutorial
#neuralnetworks #RNN #paper #nlp
βοΈ Top 6 Open Source Pre-trained Models for Text Classification you should use
1. XLNet
2. ERNIE
3. Text-to-Text Transfer Transformer (T5)
4. Binary - Partitioning Transformation (BPT)
5. Neural Attentive Bag-of-Entities Model for Text Classification (NABoE)
6. Rethinking Complex Neural Network Architectures for Document Classification
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πVia: @cedeeplearning
https://www.analyticsvidhya.com/blog/2020/03/6-pretrained-models-text-classification/
#classification #machinelearning
#datascience #model #training
#deeplearning #dataset #neuralnetworks
#NLP #math #AI
1. XLNet
2. ERNIE
3. Text-to-Text Transfer Transformer (T5)
4. Binary - Partitioning Transformation (BPT)
5. Neural Attentive Bag-of-Entities Model for Text Classification (NABoE)
6. Rethinking Complex Neural Network Architectures for Document Classification
ββββββββ
πVia: @cedeeplearning
https://www.analyticsvidhya.com/blog/2020/03/6-pretrained-models-text-classification/
#classification #machinelearning
#datascience #model #training
#deeplearning #dataset #neuralnetworks
#NLP #math #AI
Analytics Vidhya
Top 6 Open Source Pretrained Models for Text Classification you should use
Pretrained models and transfer learning is used for text classification. Here are the top pretrained models you shold use for text classification.
π The Best NLP with Deep Learning Course is Free
Stanford's Natural Language Processing with Deep Learning is one of the most respected courses on the topic that you will find anywhere, and the course materials are freely available online.
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πVia: @cedeeplearning
https://www.kdnuggets.com/2020/05/best-nlp-deep-learning-course-free.html
#deeplearning #NLP
#neuralnetworks
#machinelearning
#free #AI #math
Stanford's Natural Language Processing with Deep Learning is one of the most respected courses on the topic that you will find anywhere, and the course materials are freely available online.
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πVia: @cedeeplearning
https://www.kdnuggets.com/2020/05/best-nlp-deep-learning-course-free.html
#deeplearning #NLP
#neuralnetworks
#machinelearning
#free #AI #math
KDnuggets
The Best NLP with Deep Learning Course is Free - KDnuggets
Stanford's Natural Language Processing with Deep Learning is one of the most respected courses on the topic that you will find anywhere, and the course materials are freely available online.