Natural Language Processing Transformers: Empowering BERT-based Sentiment Classification for March Madness

Natural Language Processing Transformers: Empowering BERT-based Sentiment Classification for March Madness

Introduction:

With all the excitement surrounding March Madness, GPT models, and Generative AI, we are thrilled to explore the insights that natural language processing (NLP) transformers can offer. In SAS Viya 2023.02 release, we introduced a BERT-based classifier to our NLP stack. BERT, which stands for Bidirectional Encoder Representations from Transformers, was first introduced in 2018 and has since become a significant advancement in language understanding. But why did SAS choose to add BERT to our NLP lineup, and what are our thoughts on GPT models? In this blog, we will delve into BERT’s implementation in SAS Visual Text Analytics and provide a deep dive into the transformer family of models, which lays the foundation for the advancements in Generative AI. So, let’s jump right in and explore how BERT is implemented in SAS Visual Text Analytics.

Full Article: Natural Language Processing Transformers: Empowering BERT-based Sentiment Classification for March Madness

Exploring the Power of BERT: SAS Visual Text Analytics Introduces BERT-based Classification

With all the excitement surrounding March Madness, GPT models, and Generative AI, SAS is thrilled to dive into the world of natural language processing (NLP) transformers and the insights they can provide. In their recent release, SAS added a BERT-based classifier to their NLP stack, allowing users to tap into the power of BERT for text classification.

But what exactly is BERT? BERT, which stands for Bidirectional Encoder Representations from Transformers, was introduced in a groundbreaking paper titled “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding” by Devlin et al. in 2018. In adding a BERT model to their lineup, SAS aims to provide users with a low-cost entry point, efficient training capabilities, and highly accurate results.

Implementation of BERT in SAS Visual Text Analytics:

To implement BERT in SAS Visual Text Analytics, SAS created a CAS Action Set called Text Classifier, which includes two actions: trainTextClassifier and scoreTextClassifier. The trainTextClassifier action requires appropriately labeled input data for training the model. For example, a collection of sentences related to March Madness, labeled as positive or negative sentiment.

During training, the model’s performance is measured through metrics such as Train Loss, Train Accuracy (%), Validation Loss, and Validation Accuracy (%). Train Loss indicates how well the model performs on the training data, while Train Accuracy measures the percentage of correctly classified examples in the training set. Validation Loss and Validation Accuracy help gauge the model’s generalization abilities to new, unseen data.

Once the model is trained, it can be used to score new data using the scoreTextClassifier action. The scored data is organized in a table, which can be easily imported into SAS Visual Analytics for further exploration and analysis.

Transformers and the Encoder-Decoder Architecture:

To truly understand the power of models like BERT and the GPT family, it’s essential to familiarize yourself with transformers. These transformer-based neural network architectures, first introduced in the 2017 paper “Attention is All You Need” by Vaswani et al., have revolutionized natural language processing and other fields like computer vision and speech recognition.

Transformers derive their name from their ability to transform input sequences into output sequences through a series of transformer blocks. These blocks consist of self-attention mechanisms, feedforward layers, residual connections, and layer normalization. The transformer architecture has become immensely popular due to its ability to focus on relevant information while disregarding irrelevant details.

Encoder-decoder architectures are a specific type of transformer model. In the original Transformer model by Vaswani et al., an encoder-decoder architecture was used for machine translation tasks, where the model translates text from one language to another. The encoder component encodes the source sequence, while the decoder generates the target sequence.

Enhancements have been made to the encoder-decoder architecture, one notable example being the Text-to-Text Transformer (T5) introduced by Raffel et al. in 2020. T5 has further pushed the limits of transfer learning and has been made accessible to the open-source community.

In conclusion, SAS’s inclusion of the BERT-based classifier in SAS Visual Text Analytics empowers users with the capabilities of BERT for text classification tasks. Leveraging transformers and encoder-decoder architectures like BERT, SAS continues to advance the field of NLP and Generative AI. With easy implementation and impressive performance, SAS enables users to unlock the full potential of their data for analysis and decision-making.

Summary: Natural Language Processing Transformers: Empowering BERT-based Sentiment Classification for March Madness

This blog post explores the implementation of BERT (Bidirectional Encoder Representations from Transformers) in SAS Visual Text Analytics. BERT is a transformer-based natural language processing (NLP) model that SAS has added to its NLP stack. The post discusses the reasons behind the inclusion of BERT and provides a demonstration of how BERT-based classification can be used on March Madness data. It also explains the training process of BERT, including train loss, train accuracy, validation loss, and validation accuracy. Additionally, the post explains the transformer architecture and the role of encoders in BERT. Overall, it provides an informative overview of BERT and its implementation in SAS Visual Text Analytics.

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