Transformer Explained Visually: Learn How LLM Transformer Models Work with Interactive Visualization
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Updated
Jun 6, 2025 - JavaScript
Transformer Explained Visually: Learn How LLM Transformer Models Work with Interactive Visualization
Traditional Mandarin LLMs for Taiwan
Natural Language Processing (NLP). Covering topics such as Tokenization, Part Of Speech tagging (POS), Machine translation, Named Entity Recognition (NER), Classification, and Sentiment analysis.
Tensorflow implementation of Semi-supervised Sequence Learning (https://arxiv.org/abs/1511.01432)
Source code for our paper: "ARIA: Training Language Agents with Intention-Driven Reward Aggregation".
Python scripts and datasets of the "Extremely Low-Resource Neural Machine Translation: A Case Study of Cantonese" project
Initial Exploratory Works on Knowledge Tracing in Transformer Based Language Models
Worth-reading papers and related resources on pretrained-language models(PLMs). On the Shoulder of Giants!
Implementation of a LLaDA-inspired Masked Diffusion Model for Text using PURE BYTE-LEVEL TOKENIZATION (cuz why not) and Mixed Precision Training for speed.
Vector Space Model and Language Model for Information Retrieval system based on collections of text documents.
Redefining prompt engineering as structured cognition.
This repository contains the code and resources for the paper "Exploring Explainability in Arabic Language Models: An Empirical Analysis of Techniques," accepted at ACLing 2024.
An end-to-end RNN-based text generation pipeline built with PyTorch. Includes web scraping, preprocessing, vocabulary construction, sequence generation, model training, and sample text generation.
This repo contains notes of the Spacy Masterclass (NLP Course)
Solvr.ai: Your AI-powered hub for limitless possibilities. Answer questions, summarize documents, generate images, get AI assistance, analyze social sentiment, and more. Streamline your workflow, make informed decisions, and unleash the full potential of AI in one convenient platform.
A Guide to Help Teach the Fundamentals of Responsible AI
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