How NLP technology empowers search engines

In the era of globalization, search engines not only need to provide users with accurate information, but also need to understand multiple languages and dialects. This article explores in detail how search engines handle multiple languages and dialects through NLP technology to ensure high-quality search results for users in different regions and cultures. It also […]

[NLP] DeepSpeed-FastGen: High-throughput text generation for LLM through MII and DeepSpeed-Inference

1. Introduction Large language models (LLMs) such as GPT-4 and LLaMA have become dominant workloads serving AI applications at all levels. From general chat models to document summarization, from self-driving to co-piloting at every layer of the software stack, the need to deploy and serve these models at scale has skyrocketed. While frameworks such as […]

An overview of NLP syntax analysis: from theory to practical interpretation of PyTorch

This article comprehensively explores the theory and practice of syntactic analysis in natural language processing (NLP). From the definitions of syntax and grammar to various syntactic theories and methods, the article analyzes the multiple dimensions of syntactic analysis in detail. Finally, through practical demonstrations of PyTorch, we show how to apply these theories to specific […]

PaddleNLP Natural Language Processing Knowledge Graph When using uie-x-base, uie-m-large, uie-m-base models, an error Out of memory error on GPU 0 gpu memory is not enough

Hi, I’m @ cargoyouxing I’m interested in … I’m currently learning… ?I’m looking to collaborate on… How to reach me… README directory (continuously updated) Various error handling, crawler practice and templates, Baidu Intelligent Cloud face recognition, computer vision deep learning CNN image recognition and classification, PaddlePaddle natural language processing knowledge graph, GitHub, operation and maintenance… […]

14-NLP’s Bert implements multi-classification of text

Article directory code Interpretation of the overall code process debug the above code Code from pypro.chapters03.demo03_data acquisition and processing import train_list, label_list, val_train_list, val_label_list import tensorflow astf from transformers import TFBertForSequenceClassification bert_model = “bert-base-chinese” model = TFBertForSequenceClassification.from_pretrained(bert_model, num_labels=32) model.compile(metrics=[‘accuracy’], loss=tf.nn.sigmoid_cross_entropy_with_logits) model.summary() result = model.fit(x=train_list[:24], y=label_list[:24], batch_size=12, epochs=1) print(result.history) # Save the model (the essence of […]

[NLP] Llama2 model runs on Mac machine

This article will introduce how to use llama.cpp to locally deploy and run the quantitative version of Llama2 model inference on MacBook Pro, and build a simple document Q&A application locally based on LangChain. The experimental environment of this article is Apple M1 chip + 8GB memory. Llama2 and llama.cpp Llama2 is an iterative version […]

13-NLP Bert multi-classification implementation case (data acquisition and processing)

Article directory Preface knowledge 1. Code interpretation 1.1 Code display 1.2 Process introduction 1.3 Debug method introduced line by line 3. Knowledge points 3.2 Code questions 1. Use tokenizer.encode() to convert each line of text into token IDs in the BERT vocabulary. Does it include word embeddings? Preface knowledge An article can belong to multiple […]

Analysis of the LSTM principle of NLP

Article directory background Limitations of simpleRNN LSTM Write a sigmoid example by hand Neural networks supporting long memory Interpretation of the three gates Background SimpleRNN has certain limitations, Text on image: The picture title mentions “SimpleRNN is a basic model. It is used to solve sequential problems, in which the output of each step will […]

LSTM and BiLSTM of NLP

Article directory Code display Code interpretation Introduction to Bidirectional LSTM (BiLSTM) Code display import pandas as pd import tensorflow astf tf.random.set_seed(1) df = pd.read_csv(“../data/Clothing Reviews.csv”) print(df.info()) df[‘Review Text’] = df[‘Review Text’].astype(str) x_train = df[‘Review Text’] y_train = df[‘Rating’] print(y_train.unique()) <class ‘pandas.core.frame.DataFrame’> RangeIndex: 23486 entries, 0 to 23485 Data columns (total 11 columns): # Column Non-Null […]

Building RNN neural network for NLP

Article directory Code display code intent Code interpretation 1. Embedding Layer 2. SimpleRNN Layer (simple recurrent neural network layer) 3. Dense Layer (fully connected layer) 4. Dense Layer (the second fully connected layer) Summarize Introduction to knowledge points 1. Embedding 2. SimpleRNN 3. Dense Code display # Build RNN neural network from tensorflow.keras.models import Sequential […]