The Data Savvy

The Recall-Oriented Understudy for Gisting Evaluation (ROUGE)(Lin et al., 2004) is a metric, commonly used to measure the accuracy of text summarization models. Summarization is one of the most difficult tasks to measure because there is no straightforward way to do it. The ROUGE score is one of the most used metrics for summarization. This metric compares a generated summary to a set of human-created reference summaries. Using ROUGE, recall is calculated based on how much of the reference summary is represented by the generated summary. ROUGE refers to a set of metrics. Those most likely to be used are ROUGE-N, ROUGE-L, and ROUGE-LSUM. The ROUGE-N metric measures how many ‘n-grams’ match between our model-generated text and the reference text. In simple terms, n-grams are groups of words or tokens. There is only one word in a unigram (1-gram) and two consecutive words in a bigram(2-gram). ROUGE-N uses n-grams to represent the gram. In ROUGE-1, we are measuring the match-rate of unigra

  Pre-trained Language Models (PTLM) in NLP The main purpose behind Natural Language Processing(NLP) is to make the computer system capable to understand and communicate the language as a human. NLP tasks are challenging because it is not only important to understand individual words, but also to understand the context in which they are used. According to NLP, each word has a meaning, but the meaning is strongly influenced by the context in which it is used. The Language Model is a machine learning model that predicts the next word based on the parts of a sentence. A pre-trained language model has been trained on a large corpus of data. Through this training, the model learns the language’s general rules for word usage and how it is written. The model is then trained with a task-specific dataset.                                                        Pretrained language model concept Text-to-Text Transfer Transformer Model(T5) T5 (Raffel et al., 2019) is Google’s state-of-the-art (SOTA

  Explainable AI (XAI) in Healthcare Artificial Intelligence has been an uprising technology since a decade ago and is still an emerging technology providing various levels of enhancements. But sometimes it is better to interpret the results of the solution in such a way that a user can understand it. Which is referred to as explainable or interpretable Artificial Intelligence. AI is widely used in medical and healthcare fields to help clinicians to make decisions, increase efficiency and reduce the mortality rate. However, the end-user cannot see the logic behind these outputs. Due to their black-box character, these models are difficult for physicians to comprehend, and as a result, they cannot be used in clinical practice. There comes the scope of explainable Artificial intelligence. The act of reliability on artificial intelligence is the next question. As said with a black box nature, AI systems are not widely accepted for many medicinal decision-making scenarios, especially durin

                      AutoML Automated machine learning(AutoML) is a process to fully automates the end-to-end of the machine learning process and makes it more usable by intended users. AutoML is also related to AI and the introduction of AutoML in Robotics is a boom in technology. AutoML tools will outperform the manual machine learning (ML) modelling, with high robustness then only it has relevance. How to provide robustness while dealing with real-world datasets, with considerable time constraints is a relevant question in this area. TPOT, Auto-sklearn, Auto-keras and H2O-Automl are some of the open-source AutoML tools and Google cloud Automl and Microsoft AzureML are commercial AutoML Tools. To compare the robustness of the open-source AutoML tools many researchers take different datasets from openML and conduct studies based on different criteria. They use quantitative measurement based on time to understand the robustness of these AutoML tools. These experiments show, that diffe

             Text Summarization With the rapid increase of textual data over the past few years, a valuable resource for extracting and analyzing data has emerged. In order to retrieve useful knowledge within a reasonable amount of time, it is necessary to summarize this information. Therefore, the demand for automatic text summarization systems is on the rise. Since it is becoming increasingly important to receive information in a concise and easily understood form, summarization is among the prime problems in Natural Language Processing (NLP). Summarization is a complex and challenging task because, as part of summarization, the model needs to identify the context of the given text and ensure that the generated sentences are grammatically correct and readable. A second challenge is determining which information to use for the summary and which to omit. The summarization process is undergoing much research, but the generated summary quality needs improvement. Recent approaches to summ