Best Nlp Models For Text Classification

Best Nlp Models For Text Classification – A graphic by Yulia Nikulski that illustrates the topics covered in A Beginner’s Guide to How to Use Transformer-Based NLP Models. Icons by Becris, Freepik, ultimatearm, monkik and Eucalypse from Flaticon.

A few months ago I started working on a project involving text classification. Previously, I only worked with basic NLP techniques to use simple ML algorithms to prepare and classify textual data. However, I am aware of the latest (SOTA) results that can be achieved with Transformer-based NLP models such as BERT, GPT-3, T5 and RoBERTa.

Best Nlp Models For Text Classification

Best Nlp Models For Text Classification

Actually, let me repeat that part to illustrate my knowledge at the time: I know there is considerable buzz around the GPT-3 release and how good that model is. can generate text by itself. And something tells me that this model can be used for text classification. How can I “get this model” and have it predict labels for my data? I have no idea.

Prompting: Better Ways Of Using Language Models For Nlp Tasks

There are excellent blog posts that explain the technical intricacies of Transformers, compare the many language comprehension and language generation models available today, and provide specific implementation instructions and code. NLP tasks. However, I do not have the basic knowledge to understand how to use these NLP models.

As such, this article provides a high-level overview of how these models work, where you can access them, and how you can apply them to your personal datasets and NLP tasks. . It is aimed at people who are familiar with the basics of NLP but are new to more complex language models and transfer learning. For more information beyond the basics, I provide links and links to research articles, blog posts, and code tutorials.

A transformer is a class of neural network architecture developed by Vaswani et al. in 2017 [1]. Without going into too much detail, the architecture of this model consists of a multi-head self-addressing mechanism combined with an encoder-decoder structure. It can outperform different models to SOTA results in terms of evaluation score (BLEU score) and training time using recurrent (RNN) or convolutional neural network (CNN).

An important advantage of the transformer over other neural network (NN) frameworks is that the longer context around the word is considered in a more computationally efficient way [1, 2]. The phrase “make more difficult” is synonymous with “make the registration or voting process more difficult”, although “more difficult” is a distant relative of “make” [1]. Computing the relevant context around a word can be done in parallel, saving significant learning resources.

Natural Language Processing Technology

Transformer was originally developed for NLP tasks and used by Vaswani et al. in machine translation [1]. However, it has also been adapted for image recognition and other fields. To learn more about the exact mechanism and operation of a transformer, check out these in-depth explanations:

The transformer model framework basically replaces other NLP model implementations such as RNN [3]. Current SOTA NLP models use the Transformer architecture in part or in full. The GPT model only uses a Transformer Structure Decoder (unidirectional) [2], while BERT is based on a Transformer Encoder (bidirectional) [4]. T5 uses an encoder-decoder Transformer structure very similar to the original program [3]. These common architectures also differ in the number and size of the elements that make up the encoder or decoder (ie, the number of layers, the hidden size, and the number of heads they use [4]).

In addition to these variations in model structure, language models also differ in the data and tasks they use for pretraining, which will be discussed in more detail in the next paragraph. For more information on specific model architectures and their pre-production design, you can refer to the academic papers that present these models:

Best Nlp Models For Text Classification

Many transformer-based NLP models have been developed specifically for the study of transfer [3, 4]. Transfer learning describes a method in which a model is first trained on a large unlabeled text corpus using self-supervised learning [5]. It is then slightly modified during (downstream) detection of a specific NLP task [3]. The datasets defined for specific NLP tasks are usually relatively small. Training a model only on such a small data set without prior training degrades the results compared to its pre-trained version [2]. The same pre-trained model can be used to correct various downstream NLP tasks, including text classification, summarization or question answering.

Step 2.5: Choose A Model

A variety of unlabeled data sources can be used for prior training. If the data set is large enough, they may be completely irrelevant to the data or task in the repair process. GPT-2 is pre-trained using 40 GB of text [6]. Therefore, the front-end training is very time and resource intensive and usually takes several hours or even days using multiple GPUs. Data sets and learning objectives implemented during pre-training sessions vary significantly between models. While GPT uses a standard language modeling objective [2] that predicts the next word in a sentence, BERT is trained on Masked Language Modeling (MLM) and Next Sentence Prediction (NSP) [4]. The RoBERTa model replicates the architecture of the BERT model, but modifies the prior training by using more data, takes longer training, and removes the NSP target [7].

Sample control points of pre-made models serve as a starting point for fine-tuning. Below, a set of data defined for a particular task is used as training data. There are several different methods of repair, including:

Two additional types of fine-tuning (adapter layers and gradual freezing) are defined in [3]. Regardless of the approach, a task-specific output layer should usually be included in the model. This layer is unnecessary if you want to adapt the model to a new set of text, but the learning objective remains the same. This can happen if you use GPT-2 on a specific text dataset to generate a language.

Many research papers presenting pre-trained Transformer-based models also perform fine-tuning experiments to demonstrate their transfer learning performance. For example, BERT is trained on 11 NLP tasks, adjusting all the parameters of the entire model and delivering the results to a task-specific output layer.

In The Spotlight: Text Classification Using Deep Learning

Multi-task learning is designed to increase model generalization across tasks and domains [8]. The same model can perform different downstream NLP tasks with or without changing model parameters or architecture [6]. GPT-2 is trained on a large and diverse text dataset using only the language modeling objective. Achieves SOTA results for various NLP tasks without fine-tuning the tasks. Only a few examples are given during the inference process to help the model understand what task is being asked [6].

Meta-learning is particularly well-suited for situations where the task-specific data set contains only a few samples (low-resource tasks). This allows models to develop a good starting point and a broad set of skills to quickly adapt to new or previously seen tasks in the inference process [9, 10]. The main difference between multi-task learning and meta-learning is that the performance of multi-task learning tends to work with large data sets. Meta-learning adapts well to any task [10]. Similar to multi-task learning, no fine-tuning is required for meta-learning models.

Zero, one, and multiple learning is sometimes called meta-learning. These specifications refer to the number of representations presented during model inference [9].

Best Nlp Models For Text Classification

Most of the Transformer-based models developed by researchers are open source (except for GPT-3 so far). You can find their model implementations on GitHub (eg for BERT). However, if you want to use the model in your NLP work, you need a checkpoint model. You can access it through the transformers library provided by Hugging Face. This library gives you access to over 32 pre-trained SOTA models. It provides an API that allows you to conveniently integrate models into your code using PyTorch or TensorFlow.

Build Vs Buy: Text Analytics & Nlp

Face Hug is used by organizations such as Google, Microsoft and Facebook. However, the transformers library is also good for beginners. It has extensive documentation and various Google Colab notebooks that provide examples of implementing text classification using, for example, DistilBERT. They also guide you through common examples on how to train and refine the model.

With pipeline objects, you only need to write a few lines of code to infer various tasks. These pipelines use already well-established models, and the documentation explains which models work with which pipeline for which task. Hugging Face even created a zero-shot classification pipeline. It takes any text input and the number of tags and returns the probability of each of those tags. No label should be visible during repair (hence the name zero-shot).

Once you know what NLP task to do, you need to know which Transformer-based model is best suited for that purpose. For example, GPT-2 is designed for language production with its precompiled and unidirectional architecture, which makes it most useful for language production tasks. However, it can also be fine-tuned for, for example, sequence classification.

You can refer to the original research papers on these models to get an idea of ​​what tasks they are suited for and where they work best. You can also check Hugging Face transformers documentation about different models

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