Category：NLP
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[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 […]
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[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 […]
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Transformer Code Explained (Pytorch Edition)
preamble Based on the previous postClassical Network Architecture Learning-TransformerThe study, today we use pytorch to build their own transformer model, to deepen the understanding of transformer, not only in the field of NLP can not get around the transformer, but also in the field of CV is also very hot, a lot of models are […]
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[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, […]
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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. […]
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NLP Large Model Fine-Tuning Principles
1. Background LLM (Large Language Model) Large Language Model, designed to understand and generate human language, needs to be trained on a large amount of text data. It is generally based on the Transformer structure and has a parameter count of Billion or more. For example, GPT-3 (175B), PaLM (560B). Three big things are happening […]
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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 […]
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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 […]