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Editor’s note: This article The article is written by data scientist Jeremy Howard and natural language processing expert Sebastian Ruder, and the purpose is to help Liberians Escort help veterans and laypeople better Clear about their new paper. The paper shows how to automatically classify astronomy using less data while still being more accurate than the original method. This article will explain in simple terms natural language processing, literary classes, migration learning, language modeling, and how their methods combine these concepts. If you have ever been interested in NLPYou are very familiar with in-depth learning. You can directly enter LR Escorts into the project homepage.
Introduction
May 14 , we published the paper Universal Language Model Fine-tuning for Text Classification (ULMFiT), a pre-trained model and open sourced it in Python. The paper has been peer reviewed and will be presented at ACLiberians EscortL 2018. The link below provides an in-depth teaching video of the paper method, as well as the Python modules used, as well as training models and construction A script based on your own model.
This model significantly improves the efficiency of the Wentian profession, and at the same time , code and training models will allow each user to better solve the following problems in this new way:
Find documents related to a legal case;
Identify spam, malicious comments or bots People respond to moderators;
Classify positive and negative evaluations of products;
Classify political bias of articles;
Others
ULMFiT requires fewer numbers than other methods
So, what changes does this new technology bring? First, let us understand what the main part of the summary Liberians Escort said, and then we will expand on this in other parts of the article. What does it mean:
Transfer learning has brought huge changes to computer vision, but existing NLP technology still needs to improve the model for specific tasks and train from scratch. We propose an effective transfer learning method that can be applied to any task in the NLP field, and the techniques we propose are critical for changing language models. Our approach is better than existing ones in six civil and military occupationsThe techniques must be excellent. In addition, this method only uses 100 labeled samples for training, and the ultimate performance is achieved from scratch, with LR EscortsModel performance with tens of thousands of training data.
NLP, deep learning and classification
Natural language processing is a special task in the field of computer science and artificial intelligence. As the name implies, it is to use computers to process the languages in the world. Natural languages refer to the words we use to communicate every day, such as English or Chinese, as opposed to specialized languages (computer code or musical notes). The application scope of NLP LR Escorts is very wide, such as search, personal assistant, summary and so on. In general, natural language processing is a very challenging task because the computer code written is difficult to express the different emotions and nuances of the language and lacks flexibility. Maybe you have experienced dealing with NLP in your life, such as making a call with an active response moderator robot, or talking to Siri, but the experience is not smooth.
In the past few years, we have begun to see deep learning go beyond traditional computers, with great success in the field of NLPLiberia Sugar result. Different from the previous need to define a series of fixed rules by the program, deep learning uses Liberians Sugardaddy to directly learn a wealth of non-linear rules from the data. Linear relationships are processed by neural networks. Of course, the most obvious achievement of deep learning is still in the field of computer vision (CV). We can feel its rapid progress in the previous ImageNet image classification competition.
Deep learning has also achieved many successes in the field of NLP. For example, the automatic translation reported by the “New York Times” has been used in many applications. These Liberia Sugar successful NLP tasks have a common feature, that is, they all have a large amount of labeled data available when training the model. However, until now, these applications have only been available on models that can collect large tagged data sets, while also requiring clusters of computers capable of long-term computation.
The most challenging problem of deep learning in the NLP field is exactly the most successful problem in the CV field: classification. This refers to classifying arbitrary items into a group, such as classifying files or images into a dog or cat dataset, or determining whether they are positive or negative LR Escorts‘s and more. Many problems in practice can be regarded as classification problems, which is why the success of deep learning classification on ImageNet has spawned various related commercial applications. In the field of NLP, current technology can make “identification” very well. For example, if you want to know whether a movie review is positive or negative, what you need to do is “emotional analysis.” But as the sentiment of the article becomes more and more ambiguous, the model becomes difficult to judge because there is not enough label data to learn from.
Migration learning
Our purpose is to solve these two problems:
In NLP problems, what should we do when we do not have large-scale data and computing resources?
Make the classification of NLP simple
Research participants (Jeremy HLR Escortsoward and Sebastian Ruder) The field we are engaged in can just solve this problem, that is, migration learning. Transfer learning refers to using a model that solves a specific problem (such as classifying ImageNet images) as a basis to solve similar problems. A common approach is to fine-tune the original model. For example, Jeremy Howard has migrated the above classification model to CT image classification to detect whether there is cancer. Because the adjusted model does not need to be learned from scratch, it can achieve higher accuracy than a model with less data and shorter computation time.
For many years, simple transfer learning using only a single weight layer has been very popular, such as Google’s word2vec embedding. However, in reality complete neural networks include many layers, so applying transfer learning to only a single layer only solves superficial problems.
The point is, if we want to solve NLP problems, where should we migrate our learning? This problem has troubled Jeremy Howard for a long time, but when his friend Stephen Merity announced the development of the AWD LSTM language model, this was a significant improvement in language modeling. A language model is an NLP model that can predict what the next word in a sentence will be. For example, the phone’s built-in language model can guess what word you will type next when sending a message. The reason why this result is very important is that if a language model wants to correctly predict what you are going to say next, it must have a lot of knowledge and a very comprehensive understanding of syntax, semantics and other elements of natural language. clear. We also haveLiberi when browsing or categorizing textsa SugarThis kind of talent, but we are not aware of it.
We found that applying this method to migration learning helps to become a universal method for NLP migration learning:
This method works regardless of file size, number of numbers, and tag type
It has only one structure and training process
It does not need to customize special engineering and pre-processing
It does not require additional related files or tags
Start working
ULMFiT’s high-level method (taking IMDb as an example)
This method has been tried before Liberia Sugar Daddy, but in order to achieve satisfactory performance, millions of texts are required. We found that by adjusting the language model, we can achieve better results. In particular, we found that the model can adapt better to new data sets if the learning rate of the model is carefully controlled and the model is pre-trained on new materials to ensure that it does not forget the intrinsic events it has previously learned. Excitingly, we found that models can learn better with limited samples. On a dataset containing two different astronomy categories, we found that training our model on 100 examples achieved the same results as training it from scratch on 10,000 iconic examples.
Another important feature is that we can use any corpus that is large enough and common to build a universal language model, so that it can be adjusted for any purpose. We decided to do this using Stephen Merity’s WikiText 103 dataset, Liberia Sugar which contains a processed subset of the English Wikipedia.
Many studies in the field of NLP are in the environment around English. If Liberians Sugardaddy trains the model in a non-English language, This will bring about a series of difficulties. Typically, there are very few public non-English language data sets, and if you want to train a literary model for Thai, you have to collect the data yourself. Collecting non-English text data means you need to annotate it yourself or find annotators, since crowdfunding services like Amazon’s Mechanical Turk often only have English annotators.
With ULMFiT, we can practice English very easilyThe non-lingual literary and astronomy class model currently supports 301 languages. To make this task easier, we will release a model zoo in the future with built-in pre-trained models in various languages.
The future of ULMFiT
Liberians Sugardaddy We have proven that this technology has different tasks in the same configuration The Chinese performance was very good. In addition to the astronomy category, we hope that ULMFiT can solve other important NLP problems in the future, such as sequence labeling or natural language generation.
The success of transfer learning in the computer vision field Liberia Sugar and the pre-trained ImageNet model has been transferred to the NLP field. Many entrepreneurs, scientists, and engineers are currently using modified ImageNet models to solve important visual problems. Now that this tool is available for language processing, we hope to see more related applications in this field.
Although we have shown the latest progress in the field of literature and astronomy, a lot of effort is still needed in order for our NLP migration learning to achieve its highest level of utility. There are many important paper analyzes in the field of computer vision, which provide in-depth analysis of the results of transfer learning in this field. Yosinski et al. have tried to answer the question: “How are features in deep LR Escorts neural networks transferable?”, while Huh studied “Why ImageNet is suitable for transfer learning”. Yosinski even created a rich visual LR Escorts toolkit to help participants better understand the features in their computer vision models. If you solve a new problem using ULMFiT on a new data set, please share your feedback with friends in the forum!
Original title: The universal language model ULMFiT created using transfer learning has reached the best level in the field of astronomy
Source of the article: [Microelectronic signal: jqr_AI, WeChat public account: Lunzhi] Welcome to add tracking and follow! Please indicate the source when transcribing and publishing the article.
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