CRF + +: a CRF Toolkit

reference resources: CRF++: Yet Another CRF toolkitCRF + + usage: Chinese TranslationModel DownloadModel installation and training ย  train Mode 1: % crf_learn template_file train_file model_file Where template_file and train_file files that need to be prepared in advance. crf_learn in model_file to generate the trained model file. The tra ...

Added by harshilshah on Wed, 02 Feb 2022 03:41:09 +0200

On the strategy of text generation or text translation decoding

catalogue 1, Basic process of text generation and Translation Training and decoding of translation models Training process Decoding process Training and decoding of generation class model (GPT Series) Training process Decoding process 2, Decoding strategy 1. Greedy search 2,beam_search cluster search 3. Random sampling 4. Top-K Samp ...

Added by phencesgirl on Thu, 27 Jan 2022 00:23:34 +0200

Implementation of thousands of words data set emotion analysis competition based on paddelnlp Skep pre training model

Emotion analysis competition: score 0.7943 Here, the high-level API of the propeller is used to quickly build a model to submit the results of the emotion analysis competition. Please refer to the specific principle and analysis NLP punch in camp practice lesson 5: text emotion analysis . The following will be divided into three parts: sent ...

Added by rocket on Wed, 26 Jan 2022 02:25:15 +0200

4_ Making RNN s more effective

1, Stacked RNN from keras.models import Sequential from keras.layers import LSTM,Embedding,Dense # Set super parameters vocabulary = 10000 # There are 10000 words in the dictionary embedding_dim=32 # shape(x)=32, and the dimension of word vector x is 32 word_num = 500 # Each film review has 500 words. If it exceeds 500 words ...

Added by sicKo on Tue, 25 Jan 2022 09:20:14 +0200

Some optimization on improving the accuracy of OCR recognition

preface 1. This article is based on the previous article: Some optimizations on improving the accuracy of OCR recognition (2) Some optimizations are made to improve the accuracy of picture direction recognition to 96%. 2. Before reading this article, it is recommended to read the previous one for better understanding 1, Optimization ide ...

Added by vronsky on Sat, 22 Jan 2022 10:35:05 +0200

Code implementation and experiment of named entity recognition based on deep active learning

In many problems, obtaining a large amount of accurate data needs a high cost, which often limits the application of deep learning. Active learning can use a small amount of labeled data to achieve high learning accuracy by filtering the unlabeled data. This paper will provide code implementation, show experimental results and some thoughts. C ...

Added by ronald29x on Fri, 21 Jan 2022 10:28:15 +0200

Code Intelligence: Problems and Solutions

Code Intelligence: Problems and Solutions Today, the natural language processing revolution is triggered by the large model based on pre training, and the code intelligence technology is also developing rapidly. So, what is code intelligence doing? Maybe many students will have more science fiction ideas, such as programmers going to lose thei ...

Added by dmonares on Fri, 21 Jan 2022 01:22:47 +0200

Implementation of skip gram model

Probabilistic representation of CBOW model: P(A): probability of occurrence of A. P(A,B): the probability of simultaneous occurrence of event A and event B, which is called joint probability. P(A|B): the probability of event A after giving the information of event B, which is called A posteriori probability. CBOW model: the probability of o ...

Added by Alien on Tue, 18 Jan 2022 21:17:33 +0200

Introduction to LDA topic model and its Python implementation

1, Introduction to LDA theme model LDA topic model is mainly used to infer the topic distribution of documents. The topic of each document in the document set can be given in the form of probability distribution, and topic clustering or text classification can be carried out according to the topic. LDA topic model does not care about the orde ...

Added by SpiderSprog on Sun, 09 Jan 2022 05:26:09 +0200

Implementation of text classification task based on BERT model (transformers+torch)

โ€ƒ ๐Ÿš€ The principle analysis of BERT can be seen as follows: Bert: pre training of deep bidirectional transformers for language understanding โ€ƒ ๐Ÿš€ The code implementation mainly uses huggingface's transformers Library (4.9.1) and torch Library (1.6.0) pip install transformers pip install torch ๐Ÿš€ Let's talk about my approach fi ...

Added by zarp on Tue, 04 Jan 2022 09:07:19 +0200