Tag：natural language processing (nlp)
-
[NLP] Tutorial on fine-tuning using Alpaca-Lora based on the llama model
Stanford Alpaca is fine-tuning on the whole model of LLaMA, i.e., all parameters in the pre-trained model are fine-tuned (full fine-tuning). However, this method still requires high hardware cost and is inefficient for training. [NLP] Understanding Efficient Fine-Tuning of Large Language Models (PEFT) Therefore, Alpaca-Lora utilizes the Lora technique by adding additional network layers to […]
-
[Python natural language processing + tkinter graphical interface] to achieve intelligent medical customer service Q&A robot combat (with source code, dataset, demo ultra-detailed)
If you need source code and datasets, please like and follow the collection and leave a private message in the comment section~~~. I. Introduction to Q&A Intelligent Customer Service QA Question and Answer is an abbreviation of Question-and-Answer, which retrieves answers based on the questions posed by the user and answers the user in natural […]
-
[nltk_data] Error loading reuters: < urlopen error [Errno 11004] [nltk_data] getaddrinfo
When completing cs224n Assignment1, I need to use the reuters corpus in the nltk library, but when I run nltk.download(“reuters”) in the code, I get an error that I can’t download it due to network problems: [nltk_data] Error loading reuters: <urlopen error [Errno 11004] [nltk_data] getaddrinfo According to the online tutorials tossed a long time, […]
-
Big Data Knowledge Graph – Knowledge Graph + flask based Big Data (KBQA) NLP Medical Knowledge Quiz System
Big Data Knowledge Graph – Knowledge Graph + flask based big data NLP medical knowledge Q&A system (the most detailed explanation and source code on the net / recommended collection) I. Project overview Second, the basic process of medical knowledge Q&A system that realizes knowledge graphs III. Version numbers used for project tools IV. Installation […]
-
NLP machine translation panorama: from the basic principles to the full analysis of technical practice
catalogs I. Introduction to Machine Translation1. What is Machine Translation (MT)?2. Source and target languages3. Translation models4. Importance of context II. Rule-Based Machine Translation (RBMT)1. Rule-making2. Dictionaries and vocabulary selection3. Constraints and challenges4. PyTorch implementation III. Statistical Machine Translation (SMT)1. Data-driven2. Phrase alignment3. Scoring and selection4. PyTorch implementation IV. Neural network-based machine translation1. Encoder-Decoder structure2. […]
-
NLP Information Extraction Fully Explained: a Practical Guide to PyTorch from Named Entities to Events
catalogs introductoryImportance of context and information extractionObjectives and structure of the article Overview of information extractionWhat is Information ExtractionApplication Scenarios for Information ExtractionKey challenges in information extraction entity identificationWhat is entity identificationApplication Scenarios for Entity RecognitionPyTorch implementation codeInputs, outputs and processes Relational extractionWhat is Relationship ExtractionApplication Scenarios for Relational ExtractionPyTorch implementation codeInputs, outputs and processes […]
-
Introduction to LDA Topic Modeling and Python Implementation
I. Introduction to the LDA Theme Model The LDA topic model is mainly used to infer the topic distribution of documents, which can give the topic of each document in the document set in the form of a probability distribution according to the topic for topic clustering or text classification. The LDA topic model does […]