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UltraFastBERT: Exponentially Faster Language Modeling

Unite.AI

This article introduces UltraFastBERT, a BERT-based framework matching the efficacy of leading BERT models but using just 0.3% of the available neurons while delivering results comparable to BERT models with a similar size and training process, especially on the downstream tasks.

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#39 Top 5 ML Algorithms, Graph RAG, & Tutorial for Creating an Agentic Multimodal Chatbot.

Towards AI

Featured Community post from the Discord Aman_kumawat_41063 has created a GitHub repository for applying some basic ML algorithms. It offers pure NumPy implementations of fundamental machine learning algorithms for classification, clustering, preprocessing, and regression. Our must-read articles 1. Meme of the week!

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RoBERTa: A Modified BERT Model for NLP

Heartbeat

An open-source machine learning model called BERT was developed by Google in 2018 for NLP, but this model had some limitations, and due to this, a modified BERT model called RoBERTa (Robustly Optimized BERT Pre-Training Approach) was developed by the team at Facebook in the year 2019. What is RoBERTa?

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Google Research, 2022 & beyond: Algorithmic advances

Google Research AI blog

Robust algorithm design is the backbone of systems across Google, particularly for our ML and AI models. Hence, developing algorithms with improved efficiency, performance and speed remains a high priority as it empowers services ranging from Search and Ads to Maps and YouTube. You can find other posts in the series here.)

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Building a Sentiment Classification System With BERT Embeddings: Lessons Learned

The MLOps Blog

Due to this, businesses are now focusing on an ML-based approach, where different ML algorithms are trained on a large dataset of prelabeled text. These algorithms not only focus on the word but also its context in different scenarios and relation with other words. are used to classify the text sentiment.

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Text Classification in NLP using Cross Validation and BERT

Mlearning.ai

transformer.ipynb” uses the BERT architecture to classify the behaviour type for a conversation uttered by therapist and client, i.e, Figure 4 Data Cleaning Conventional algorithms are often biased towards the dominant class, ignoring the data distribution. the same result we are trying to achieve with “multi_class_classifier.ipynb”.

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Unlock the Power of BERT-based Models for Advanced Text Classification in Python

John Snow Labs

Text classification with transformers involves using a pretrained transformer model, such as BERT, RoBERTa, or DistilBERT, to classify input text into one or more predefined categories or labels. BERT (Bidirectional Encoder Representations from Transformers) is a language model that was introduced by Google in 2018.

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