Natural language processing
Natural language processing (NLP) is a field of computer science that studies how computers and humans interact. In the 1950s, Alan Turing published an article that proposed a measure of intelligence, now called the Turing test. More modern techniques, such as deep learning, have produced results in the fields of language modeling, parsing, and natural-language tasks.
Here are 19,460 public repositories matching this topic...
AiLearning:数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2
-
Updated
Jul 7, 2022 - Python
TensorFlow code and pre-trained models for BERT
-
Updated
Jun 28, 2022 - Python
中文分词 词性标注 命名实体识别 依存句法分析 成分句法分析 语义依存分析 语义角色标注 指代消解 风格转换 语义相似度 新词发现 关键词短语提取 自动摘要 文本分类聚类 拼音简繁转换 自然语言处理
-
Updated
Jul 7, 2022 - Python
-
Updated
Jul 7, 2022 - Python
Oxford Deep NLP 2017 course
-
Updated
Jun 12, 2017
-
Updated
Jul 7, 2022 - Python
Adding a Dataset
- Name: Stanford dog dataset
- Description: The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/
- Paper: http://vision.stanford.edu/aditya86/ImageNetDogs/
- Data: *[link to the Github repository or current dataset location](http://vision.stanford.edu/aditya86/Ima
-
Updated
Jun 14, 2022
In gensim/models/fasttext.py:
model = FastText(
vector_size=m.dim,
vector_size=m.dim,
window=m.ws,
window=m.ws,
epochs=m.epoch,
epochs=m.epoch,
negative=m.neg,
negative=m.neg,
# FIXME: these next 2 lines read in unsupported FB FT modes (loss=3 softmax or loss=4 onevsall,
# or model=3 superviA comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
-
Updated
Apr 28, 2022
此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现,也是作为一个算法工程师必会的理论基础知识。
-
Updated
Jun 21, 2022 - Jupyter Notebook
A very simple framework for state-of-the-art Natural Language Processing (NLP)
-
Updated
Jul 6, 2022 - Python
Natural Language Processing Tutorial for Deep Learning Researchers
-
Updated
Jul 25, 2021 - Jupyter Notebook
Is your feature request related to a problem? Please describe.
I typically used compressed datasets (e.g. gzipped) to save disk space. This works fine with AllenNLP during training because I can write my dataset reader to load the compressed data. However, the predict command opens the file and reads lines for the Predictor. This fails when it tries to load data from my compressed files.
-
Updated
Jul 7, 2022
Checking the Python files in NLTK with "python -m doctest" reveals that many tests are failing. In many cases, the failures are just cosmetic discrepancies between the expected and the actual output, such as missing a blank line, or unescaped linebreaks. Other cases may be real bugs.
If these failures could be avoided, it would become possible to improve CI by running "python -m doctest" each t
modest natural-language processing
-
Updated
Jul 6, 2022 - JavaScript
This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.
-
Updated
Dec 22, 2020 - Python
-
Updated
Jul 7, 2022 - TypeScript
500 AI Machine learning Deep learning Computer vision NLP Projects with code
-
Updated
May 31, 2022
Stanford CoreNLP: A Java suite of core NLP tools.
-
Updated
Jul 4, 2022 - Java
Awesome pre-trained models toolkit based on PaddlePaddle.(300+ models including Image, Text, Audio and Video with Easy Inference & Serving deployment)
-
Updated
Jul 6, 2022 - Python
all kinds of text classification models and more with deep learning
-
Updated
Jul 5, 2022 - Python
Pre-Training with Whole Word Masking for Chinese BERT(中文BERT-wwm系列模型)
-
Updated
Mar 30, 2022 - Python
大规模中文自然语言处理语料 Large Scale Chinese Corpus for NLP
-
Updated
Jul 5, 2022
Created by Alan Turing
- Wikipedia
- Wikipedia


What does this PR do?