Search Results for "text summarization java code"

The JINSECT toolkit is a Java-based toolkit and library that supports and demonstrates the use of n-gram graphs within Natural Language Processing applications, ranging from summarization and summary evaluation to text classification and indexing.

TML has moved to http://www.villalon.cl/tml.html and the code to https://github.com/villalon/tml

This project aims to implement in java the following text mining techniques: Text Language Detection, Keywords and keyphrases extraction, Text Classification, Text Clustering, Single or multiple documents Summarization, Plagiarism Detection.

Performs summarization, categorization, key phrase generation,full text indexing and search,part of speech tagging,identification of place and human names in various document format. Latest code is available at http://www.twit88.com/

BLEURT-20-D12 is a PyTorch implementation of BLEURT, a model designed to assess the semantic similarity between two text sequences. It serves as an automatic evaluation metric for natural language generation tasks like summarization and translation. The model predicts a score indicating how similar a candidate sentence is to a reference sentence, with higher scores indicating greater semantic overlap. Unlike standard BLEURT models from TensorFlow, this version is built from a custom PyTorch transformer library. ...

Llama-3.2-1B-Instruct is Meta’s multilingual, instruction-tuned large language model with 1.24 billion parameters, optimized for dialogue, summarization, and retrieval tasks. It builds upon the Llama 3.1 architecture and incorporates fine-tuning techniques like SFT, DPO, and quantization-aware training for improved alignment, efficiency, and safety. The model supports eight primary languages (including English, Spanish, Hindi, and Thai) and was trained on a curated mix of publicly available...

VaultGemma is a sub-1B parameter variant of Google’s Gemma family that is pre-trained from scratch with Differential Privacy (DP), providing mathematically backed guarantees that its outputs do not reveal information about any single training example. Using DP-SGD with a privacy budget across a large English-language corpus (web documents, code, mathematics), it prioritizes privacy over raw utility. The model follows a Gemma-2–style architecture, outputs text from up to 1,024 input tokens,...