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πŸ‘‹ Hello, I'm [rong4ivy]

  • πŸŽ“ Master student at the Institute of Natural Language Processing, University of Stuttgart, Germany
  • Deeply engaged in advancing the frontiers of AI

πŸ’Ό Professional Experience

  • Specialized in optimizing large language models (LLMs) to improve performance and tailor them to specific applications, assessing their effectiveness, and identifying areas for improvement.
  • Actively collaborating in research across NLP, psycholinguistics, and LLM fields, contributing to academic publications.

πŸš€ Key Skills

  • Programming Languages: Proficient in Python; intermediate in R and JavaScript.
  • Generative AI: Skilled in fine-tuning pretrained language models, evaluating LLMs, and developing applications with LangChain and AutoGen.
  • Natural Language Processing (NLP): Experienced in tokenization, named entity recognition, and sentiment analysis.
  • Deep Learning: Proficient with PyTorch, focusing on building and training neural networks.
  • Machine Learning: Adept at developing and deploying models across various applications.

I'm always open to collaborations, discussions, and learning from others in the generative AI and NLP community.Thank you for visiting my GitHub profile. I look forward to connecting with fellow enthusiasts! πŸŽ‰ Keep Coding and Stay Curious!

πŸ’» Tech Stack:

Python R Pandas NumPy PyTorch LangChain

πŸ“Š GitHub Stats:



πŸ† GitHub Trophies

πŸ“Š GitHub Stats:



πŸ† GitHub Trophies

Rong's Projects

spatial_bench icon spatial_bench

Spatial Benchmark with textual spatial reasoning datasets using Large Language Models (LLMs).

spatialevalllm icon spatialevalllm

Code for "Evaluating Spatial Understanding of Large Language Models" TMLR 2024.

spatialqa icon spatialqa

Analyzing the spatial reasoning skills of language models

stepgame icon stepgame

[AAAI 2022] Dataset and pytorch codes for the paper titled "StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts" in AAAI 2022 (Oral)

temporal-dataset-mctaco icon temporal-dataset-mctaco

Dataset and code for β€œGoing on a vacation” takes longer than β€œGoing for a walk”: A Study of Temporal Commonsense Understanding, EMNLP 2019.

tkger icon tkger

Some papers on Temporal Knowledge Graph Embedding and Reasoning

towards-learning-terminological-concept-systems icon towards-learning-terminological-concept-systems

A pipeline approach to automatically extract terminological concept systems from text. We use multilingual neural language models to extract terms and their relations on a an intra-sentence level.

vadersentiment icon vadersentiment

VADER Sentiment Analysis. VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains.

vae icon vae

Pytorch implementation of a Variational Autoencoder trained on CIFAR-10. The encoder and decoder modules are modelled using a resnet-style U-Net architecture with residual blocks.

wav2vec2-dialect-recognition icon wav2vec2-dialect-recognition

**LING487 Final Project:** Comparing Army Brat dialect (DR8) consonants and vowels to New England (DR1), and Southern (DR5) dialects using Wav2Vec2 pretrained model running TIMIT database. * particularly interested in "Dawn vs. Don" differences and r's/ l's

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