中英机器文本翻译
☆169Jul 2, 2019Updated 7 years ago
Alternatives and similar repositories for Machine-Translation
Users that are interested in Machine-Translation are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- 英中机器文本翻译☆63Jan 2, 2019Updated 7 years ago
- 基于双向RNN,Attention机制的编解码神经机器翻译模型☆62Jan 15, 2018Updated 8 years ago
- 英中文本机器翻译的☆101Nov 8, 2019Updated 6 years ago
- 基于Transform的机器翻译系统☆21Jun 1, 2020Updated 6 years ago
- 基于seq2seq的机器翻译模型☆13Nov 28, 2021Updated 4 years ago
- Deploy to Railway using AI coding agents - Free Credits Offer • AdUse Claude Code, Codex, OpenCode, and more. Autonomous software development now has the infrastructure to match with Railway.
- Vietnamese and Chinese to English☆15Dec 17, 2018Updated 7 years ago
- a neural machine translation system from english (chinese) to chinese (english) based on 30m parallel data.☆70Mar 31, 2021Updated 5 years ago
- 机器翻译练习☆15Apr 29, 2020Updated 6 years ago
- 中文->英文的机器翻译,完全基于kreas-transformer。模型已上传,可直接跑。☆58May 1, 2020Updated 6 years ago
- 在tensor2tensor中使用自己的语料实现中英文翻译☆23Mar 18, 2019Updated 7 years ago
- BERT微调在机器翻译上的应用,哎哟,效果贼好。☆51Mar 1, 2021Updated 5 years ago
- 基于Keras实现seq2seq,进行英文到中文的翻译☆17Nov 17, 2020Updated 5 years ago
- NLP homework:RNN+Attention机器翻译模型, Transormer代码学习☆29Feb 2, 2019Updated 7 years ago
- 基于Transformer的机器翻译系统☆12Jun 28, 2022Updated 4 years ago
- Managed hosting for WordPress and PHP on Cloudways • AdManaged hosting for WordPress, Magento, Laravel, or PHP apps, on multiple cloud providers. Deploy in minutes on Cloudways by DigitalOcean.
- Tools for understanding natural language robot commands☆12Feb 21, 2021Updated 5 years ago
- 本项目采用BERT等预训练模型实现多项选择型阅读理解任务(Multiple Choice MRC)☆16Jun 20, 2021Updated 5 years ago
- 本项目包含几种常用 NLP算法的实现:关键词(keyword)、命名实体(named entity)、自动摘要(abstract)、文本相似度比较(text similarity)等☆16Jan 16, 2022Updated 4 years ago
- An open-source neural machine translation toolkit developed by Tsinghua Natural Language Processing Group☆706Apr 26, 2022Updated 4 years ago
- VCWE: Visual Character-Enhanced Word Embeddings (NAACL 2019)☆16Jun 25, 2019Updated 7 years ago
- Translate English to Chinese with seq2seq + attention model.☆21Oct 5, 2018Updated 7 years ago
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding 论文的中文翻译 Chinese Translation!☆685Jun 1, 2020Updated 6 years ago
- ChineseNMT: Translate English to Chinese with PyTorch Implementation of Transformer☆500Mar 20, 2023Updated 3 years ago
- 使用transformer架构实现简单的英语翻译中文模型☆113Oct 31, 2019Updated 6 years ago
- Virtual machines for every use case on DigitalOcean • AdGet dependable uptime with 99.99% SLA, simple security tools, and predictable monthly pricing with DigitalOcean's virtual machines, called Droplets.
- TestB榜第10的方案,bleu32.1☆63Nov 28, 2019Updated 6 years ago
- keras implement of dgcnn for reading comprehension☆163Oct 14, 2019Updated 6 years ago
- 基于LLAMA2的增量预训练藏文大语言模型Tibetan-LLAMA2-7B&Tibetan-LLAMA2-13B; 指令微调藏文大模型Tibetan-Alpaca-7B&Tibetan-Alpaca-13B。☆45Jun 8, 2024Updated 2 years ago
- 将报表数据转换格式并入库时遇到许多重复性工作,于是用Python写了一些脚本进行自动化处理,并用PySide2做了GUI界面,做成了一个工具合集☆10Sep 29, 2021Updated 4 years ago
- Chinese-English Neural machine translation with Encoder-Decoder seq2seq model : Bidirection-GRU + Fasttext word embedding + Attention + …☆20Dec 30, 2021Updated 4 years ago
- 伪原创相关☆14Sep 4, 2019Updated 6 years ago
- 本仓库是基于bert4keras实现的古文-现代文翻译模型。具体使用了基于掩码自注意力机制的UNILM(Li al., 2019)预训练模型作为翻译系统的backbone。我们首先使用了普通的中文(现代文)BERT、Roberta权重作为UNILM的初始权重以训练UNILM…☆53May 3, 2022Updated 4 years ago
- obesity challenge☆13May 12, 2018Updated 8 years ago
- 使用keras搭建seq2seq完成中英文翻译☆53Oct 1, 2018Updated 7 years ago
- Deploy to Railway using AI coding agents - Free Credits Offer • AdUse Claude Code, Codex, OpenCode, and more. Autonomous software development now has the infrastructure to match with Railway.
- transformer,机器翻译,中文--英文☆87Feb 8, 2023Updated 3 years ago
- 使用gensim库训练doc2vec模型☆12Oct 28, 2018Updated 7 years ago
- ☆329May 10, 2019Updated 7 years ago
- pytorch☆54Nov 18, 2023Updated 2 years ago
- 深度学习500问,以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述,以帮助自己及有需要的读者。 全书分为18个章节,近30万字。由于水平有限,书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作,联系sc…☆18Aug 24, 2019Updated 6 years ago
- 命名实体识别☆13Jul 28, 2020Updated 5 years ago
- 一个基于SnowNLP的新浪微博评论情感分析工具☆54Oct 18, 2017Updated 8 years ago