wilfoderek Goto Github PK

bert_sentence_similarity

Bert预训练模型fine-tune计算文本相似度

bert_sentense_similarity

BERT_Sentense_Similarity

beto

BETO - Spanish version of the BERT model

betoqadiscord

Chatbot para discord en español programado en python usando Beto

bigdata-ecosystem-architecture

Life-cycle: Internal working of HDFS, SQOOP, HIVE, SPARK, HBASE, KAFKA with code.

blackhat-python3

Source code for the book "Black Hat Python" by Justin Seitz. The code has been fully converted to Python 3, reformatted to comply with PEP8 standards and refactored to eliminate dependency issues involving the implementation of deprecated libraries.

blog

Public repo for HF Semantic Search Explication

boaz-adv-project

빅데이터 연합동아리 BOAZ 12기 ADV Vision 팀 [Fight Detection] 레포지토리입니다.

bootstrap-ajax-typeahead

Twitter Bootstrap Ajax Typeahead Plugin

build-generative-ai-apps-with-langchain

"Build Generative AI Apps with LangChain" is a repository that contains a comprehensive framework for building generative AI applications using LangChain, an innovative language modeling platform. This repository includes all the necessary code and documentation to help developers quickly build and deploy generative AI applications

camunda-bpm-examples

A collection of usage examples for camunda BPM intended to get you started quickly

chatbot-en-espanol

Chatbot in spanish using differents model: Seq2Seq model with Luong attention and transformer

chatbotspanish

Implementación de un ChatBot en Epañol/Spanish coon Soft Attention

chatgpt

Lightweight package for interacting with ChatGPT's API by OpenAI. Uses reverse engineered official API.

ckeditor5

Powerful rich text editor framework with a modular architecture, modern integrations, and features like collaborative editing.

ckeditor5-angular

Official CKEditor 5 Angular 2+ component.

ckeditor5_customwio

Modificación del componente para pruebas parciales.

ckg

Clinical Knowledge Graph (CKG) is a platform with twofold objective: 1) build a graph database with experimental data and data imported from diverse biomedical databases 2) automate knowledge discovery making use of all the information contained in the graph

class-attendance-system-using-face-recognition

code_summarization

Experiments with LLM-based code summarization using few-shot learning for use in technical audits.

codealpaca

colbert

ColBERT: Contextualized Late Interaction over BERT (code for SIGIR'20 paper)

computervisionlab

Exercises and examples of my Computer Vision subject with Deep Learning

contriever

Contriever: Unsupervised Dense Information Retrieval with Contrastive Learning

convolutional-long-short-term-memory-based-iot-node-for-violence-detection

Abstract— Violence detection has been investigated extensively in the literature. Recently, IOT based violence video surveillance is an intelligent component integrated in security system of smart buildings. Violence video detector is a specific kind of detection models that should be highly accurate to increase the model’s sensitivity and reduce the false alarm rate. This paper proposes a novel architecture of ConvLSTM model that can run on low-cost Internet of Things (IOT) device such as raspberry pi board. The paper utilized convolutional neural networks (CNNs) to learn spatial features from video’s frames that were applied to Long Short- Term Memory (LSTM) for video classification into violence/non-violence classes. A complex dataset including two public datasets: RWF-2000 and RLVS-2000 was used for model training and evaluation. The challenging video content includes crowds and chaos, small object at far distance, low resolution, and transient action. Additionally, the videos were captured in various environments such as street, prison, and schools with several human actions such as playing football, basketball, tennis, swimming and eating. The experimental results show high performance of the proposed violence detection model in terms of average metrics having an accuracy of 73.35 %, recall of 76.90 %, precision of 72.53 %, F1 score of 74.01 %, false negative rate of 23.10 %, false positive rate of 30.20 %, and AUC of 82.0 %.

core

Server core components which are a part of ONLYOFFICE Document Server

corenlp-summarizer

Spanish text summarization demo using CoreNLP

coursera-data-structures-algorithms

Coursera: Data Structures and Algorithms Specialization

coursera-stanford-algorithms-specialization

Notebook for quick search

coursera-stanford-graph-search-shortest-paths-and-data-structures

Notebook for quick search