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Fine-Tuning Legal-BERT: LLMs For Automated Legal Text Classification

Towards AI

Unlocking efficient legal document classification with NLP fine-tuning Image Created by Author Introduction In today’s fast-paced legal industry, professionals are inundated with an ever-growing volume of complex documents — from intricate contract provisions and merger agreements to regulatory compliance records and court filings.

BERT 111
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Optimizing LLM for Long Text Inputs and Chat Applications

Analytics Vidhya

Large Language Models (LLMs) have revolutionized characteristic dialect preparing (NLP), fueling applications extending from summarization and interpretation to conversational operators and retrieval-based frameworks.

LLM 208
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Top BERT Applications You Should Know About

Marktechpost

Language model pretraining has significantly advanced the field of Natural Language Processing (NLP) and Natural Language Understanding (NLU). Models like GPT, BERT, and PaLM are getting popular for all the good reasons. It aims to reduce a document to a manageable length while keeping the majority of its meaning.

BERT 98
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Making Sense of the Mess: LLMs Role in Unstructured Data Extraction

Unite.AI

This advancement has spurred the commercial use of generative AI in natural language processing (NLP) and computer vision, enabling automated and intelligent data extraction. Named Entity Recognition ( NER) Named entity recognition (NER), an NLP technique, identifies and categorizes key information in text.

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The Role of Vector Databases in Modern Generative AI Applications

Unite.AI

Take, for instance, word embeddings in natural language processing (NLP). Creating embeddings for natural language usually involves using pre-trained models such as: GPT-3 and GPT-4 : OpenAI's GPT-3 (Generative Pre-trained Transformer 3) has been a monumental model in the NLP community with 175 billion parameters.

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LLMOps: The Next Frontier for Machine Learning Operations

Unite.AI

LLMs are deep neural networks that can generate natural language texts for various purposes, such as answering questions, summarizing documents, or writing code. LLMs, such as GPT-4 , BERT , and T5 , are very powerful and versatile in Natural Language Processing (NLP). However, LLMs are also very different from other models.

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This AI Paper from Peking University and Microsoft Proposes LongEmbed to Extend NLP Context Windows

Marktechpost

Embedding models are fundamental tools in natural language processing (NLP), providing the backbone for applications like information retrieval and retrieval-augmented generation. This limitation restricts their use in scenarios demanding the analysis of extended documents, such as legal contracts or detailed academic reviews.

NLP 110