Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Tokenization, parsing, classical NLP pipelines, translation and information extraction.
Signals: nlp, natural-language-processing, tokenizer, named-entity-recognition, text-classification, machine-translation, sentiment-analysis, spacy
637 results
NLTK Source
State of the Art Natural Language Processing
:herb: NodeJS PHP Parser - extract AST or tokens
🤗 AutoTrain Advanced
A coding-free framework built on PyTorch for reproducible deep learning studies. PyTorch Ecosystem. 🏆26 knowledge distillation methods presented at TPAMI, CVPR, ICLR, ECCV, NeurIPS, ICCV, AAAI, etc are implemented so far. 🎁 Trained models, training logs and configurations are available for ensuring the reproducibiliy and benchmark.
The Hugging Face course on Transformers
Deepparse is a state-of-the-art library for parsing multinational street addresses using deep learning
The most popular spellchecking library.
extract text from any document. no muss. no fuss.
The most accurate natural language detection library for Python, suitable for short text and mixed-language text
Module for automatic summarization of text documents and HTML pages.
[EMNLP 2022] An Open Toolkit for Knowledge Graph Extraction and Construction
A calibre plugin that generates Kindle Word Wise and X-Ray files for KFX, AZW3, MOBI and EPUB eBook.
Python Packages as AWS Lambda Layers
a Deep Learning Framework for Text https://delft.readthedocs.io/
Self-contained Japanese Morphological Analyzer written in pure Go
Must-read Papers on LLM Agents.
Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.
llms 大模型 笔记50篇 此仓库包含关于机器学习、深度学习、计算机视觉、自然语言处理、大模型 爬虫等领域 项目实战
Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).
Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].
使用卷积神经网络-长短期记忆网络（bi-LSTM）-注意力机制对股票收盘价进行回归预测。The convolution neural network, short-term memory network and attention mechanism are used to predict the closing price.
Simple Keras model that tackles the Stanford Natural Language Inference (SNLI) corpus using summation and/or recurrent neural networks
[ICLR 2021] Combining Label Propagation and Simple Models Out-performs Graph Neural Networks (https://arxiv.org/abs/2010.13993)
24,523 repositories in the index in total.
