Introducing state of the art text classification with universal language models · fast.ai NLP


23 bookmarks. First posted by Sylphe may 2018.


This post is a lay-person’s introduction to our new paper, which shows how to classify documents automatically with both higher accuracy and less data…
from instapaper
yesterday by hustwj
Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch. We propose Universal Language Model Fine-tuning (ULMFiT), an effective transfer learning method that can be applied to any task in NLP, and introduce techniques that are key for fine-tuning a language model. Our method significantly outperforms the state-of-the-art on six text classification tasks, reducing the error by 18-24% on the majority of datasets. Furthermore, with only 100 labeled examples, it matches the performance of training from scratch on 100x more data. We open-source our pretrained models and code.
ML  NLP  text  classification  fastai 
may 2018 by foodbaby
“This method dramatically improves over previous approaches to text classification, and the code and pre-trained models allow anyone to leverage this new approach to better solve problems such as:

Finding documents relevant to a legal case;
Identifying spam, bots, and offensive comments;
Classifying positive and negative reviews of a product;
Grouping articles by political orientation;
…and much more.”
may 2018 by sshappell
This post is a lay-person’s introduction to our new paper, which shows how to classify documents automatically with both higher accuracy and less data requirements than previous approaches. via Pocket
IFTTT  Pocket 
may 2018 by roolio
This post shows how to classify documents automatically with both higher accuracy and less data requirements than previous approaches. It explains in simple terms: natural language processing; text classification; transfer learning; language modeling; and how this approach brings these ideas together.
algorithms  nlp  machinelearning  classification 
may 2018 by peterb
the code and pre-trained models allow anyone to leverage this new approach to better solve problems such as:

- Finding documents relevant to a legal case;
- Identifying spam, bots, and offensive comments;
- Classifying positive and negative reviews of a product;
- Grouping articles by political orientation;
- …and much more.

In computer vision the success of transfer learning and availability of pre-trained Imagenet models has transformed the field. Many people including entrepreneurs, scienti...
nlp  machine-learning  ai  transfer-learning  ml  fast.ai  classification 
may 2018 by hellsten
This post is a lay-person’s introduction to our new paper, which shows how to classify documents automatically with both higher accuracy and less data requirements than previous approaches. via Pocket
Pocket 
may 2018 by slightlywinded
“This method dramatically improves over previous approaches to text classification, and the code and pre-trained models allow anyone to leverage this new approach to better solve problems such as:

Finding documents relevant to a legal case;
Identifying spam, bots, and offensive comments;
Classifying positive and negative reviews of a product;
Grouping articles by political orientation;
…and much more.”
fast.ai  text_classification  machine_learning 
may 2018 by Sylphe