Calculating ROUGE score between two files (line-by-line)
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Updated
Jul 8, 2021 - Perl
Calculating ROUGE score between two files (line-by-line)
Python ROUGE Score Implementation for Chinese Language Task (official rouge score)
An graph-eval framework for LLM's
An implementation of ROUGE family metrics for automatic summarization.
A Python wrapper of the official ROUGE-1.5.5.pl script and a re-implementation of full ROUGE metrics.
Finetune GPT2 for text summarization
ROUGE L metric implementation using tensorflow ops
Calculating ROUGE score for Korean (Wrapper for ROUGE-1.5.5.pl script)
Novel data representation leading to granular citations and higher accuracy
Fine-tuning GPT-3.5 and Llama3 LLMs for enhanced persona consistency in chatbots using Google's Synthetic Persona Chat dataset
Python implementation for evaluating summarization by ROUGE package
A library for evaluating Retrieval-Augmented Generation (RAG) systems
Python ROUGE implementation 🦁
A benchmark of ChatGPT and some of its challengers on summarization task
In modern international business, conversations often shift between languages. Transcript AI bridges this gap by utilizing Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to ensure no context is lost in translation. It doesn't just transcribe; it understands business intent.
Agent Orchestration - LLM for Legal Metadata Extraction: A Comparative Analysis of Efficiency and Precision (paper 161 PROPOR)
This repository explores the use of advanced sequence-to-sequence networks and transformer models, such as BERT, BART, PEGASUS, and T5, for summarizing multi-text documents in the medical domain. It leverages extensive datasets like CORD-19 and a Biomedical Abstracts dataset from Hugging Face to fine-tune these models.
Unsupervised approach for inducing dialogue schemas from domain-specific conversations. This repository houses the source code and research findings from the application of cutting-edge NLP and ML techniques to dialogue systems.
Implementation of an interactive chatbot for summarizing legal and policy documents. Includes data preprocessing (cleaning, tokenization, hierarchical chunking), extractive TF-IDF baselines, and fine-tuned abstractive models (DistilBART, LED). Integrates a retrieval layer for document relevance and uses ROUGE, BLEU, and cosine similarity metrics.
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