公开(公告)号：US20230401385A1

公开(公告)日：2023-12-14

申请号：US17966485

申请日：2022-10-14

Applicant: Oracle International Corporation

Inventor： Saransh Mehta ,  Siddhant Jain ,  Pramir Sarkar

IPC: G06F40/295 ,  G06N5/02 ,  G06F40/126

CPC classification number: G06F40/295 ,  G06N5/022 ,  G06F40/126

Abstract: A novel system is described for performing hierarchical named entity recognition (“HNER”) processing that includes identifying categories at different hierarchical levels for a named entity. The HNER system uses a novel architecture comprising an encoder model and a system of trained machine learning (ML) models to perform the HNER processing, where each trained model in the system of ML models corresponds to a particular hierarchical level, and each model is trained to extract one or more named entities and predict a category for each extracted named entity for the corresponding hierarchical level. Novel techniques are also described for training the various models in HNER system including an encoder model and models in the system of models.

公开(公告)号：US20230100303A1

公开(公告)日：2023-03-30

申请号：US17488289

申请日：2021-09-28

Applicant: Oracle International Corporation

Inventor： Siddhant Jain ,  Saransh Mehta ,  Shahid Reza

IPC: G06N3/04 ,  G06K9/62

Abstract: Systems and methods for fractional inference on GPU and CPU for large scale deployment of customized transformers based language models are disclosed herein. The method can include, receiving data for use in generation of a machine learning model output, ingesting the data with a first machine learning model on a Graphic Processing Unit, receiving at least one intermediate output from the first machine learning model at a temporary store, receiving the at least one intermediate output from the temporary store at a Central Processing Unit, ingesting the at least one intermediate output with a second machine learning model on the Central Processing Unit, and outputting a prediction with the second machine learning model.