Risk Management Platform for the Military |🏅국방부장관상(Minister of National Defense Award)
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Updated
Jul 7, 2024 - Jupyter Notebook
Risk Management Platform for the Military |🏅국방부장관상(Minister of National Defense Award)
🔍 LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
LLM-PowerHouse: Unleash LLMs' potential through curated tutorials, best practices, and ready-to-use code for custom training and inferencing.
A Unified Library for Parameter-Efficient and Modular Transfer Learning
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Developed Python script to extract comments data from Amazon and Official site. Performed NLP based Tokenization, Lemmatization, vectorization and processed data in Machine understandable language Have used VADERS, ROBERTA and BERT models to find the sentiment of the reviews and used the textBlob library for processing textual data.
# Project--Sentiment_Analysis Developed Python script to extract comments data from Amazon and Official site. Performed NLP based Tokenization, Lemmatization, vectorization and processed data in Machine understandable language Have used VADERS, ROBERTA and BERT models to find the sentiment of the reviews and used the ratings on the source to chec
👑 Easy-to-use and powerful NLP and LLM library with 🤗 Awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications, including 🗂Text Classification, 🔍 Neural Search, ❓ Question Answering, ℹ️ Information Extraction, 📄 Document Intelligence, 💌 Sentiment Analysis etc.
Transformers 库快速入门教程
Annotations of the interesting ML papers I read
Finetuning the LLM model for NLP tasks and cybersec based instruction datasets
Neural Network Compression Framework for enhanced OpenVINO™ inference
State of the Art Natural Language Processing
Leveraging BERT and c-TF-IDF to create easily interpretable topics.
BertChunker: Efficient and Trained Chunking for Unstructured documents. 训练Bert做文档语义分段.
Building a Medical chatbot using SBERT model. Dataset used was MEDQUAD
Convert LaBSE model from TF Hub to PyTorch.
End-to-end Multi-task Solutions for Aspect Category Sentiment Analysis (ACSA) on Vietnamese Datasets
Generalist and Lightweight Model for Relation Extraction (Extract any relationship types from text)
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