taro VS fairseq

Compare taro vs fairseq and see what are their differences.


开放式跨端跨框架解决方案,支持使用 React/Vue/Nerv 等框架来开发微信/京东/百度/支付宝/字节跳动/ QQ 小程序/H5/React Native 等应用。 https://taro.zone/ (by NervJS)


Facebook AI Research Sequence-to-Sequence Toolkit written in Python. (by pytorch)
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taro fairseq
1 17
29,778 14,127
0.8% 3.1%
9.8 9.5
about 12 hours ago about 12 hours ago
JavaScript Python
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.


Posts with mentions or reviews of taro. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2020-10-22.
  • Top 10 Developer Trends, Thu Oct 22 2020
    dev.to | 2020-10-22
    NervJS / taro


Posts with mentions or reviews of fairseq. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-05-01.
  • BART: Denoising Seq2Seq Pre-training for NLG (explained)
    news.ycombinator.com | 2021-10-08
  • [R] Facebook & CMU’s Zero-Shot VideoCLIP Outperforms Fully-Supervised SOTA Methods for Video-Text Understanding
    Code for https://arxiv.org/abs/2109.14084 found: https://github.com/pytorch/fairseq/tree/main/examples/MMPT
  • Fairseq S^2: A Scalable and Integrable Speech Synthesis Toolkit
    news.ycombinator.com | 2021-09-16
  • Facebook AI Introduces GSLM (Generative Spoken Language Model), A Textless NLP Model That Breaks Free Completely of The Dependence on Text for Training
    4 Min Read | GLSM Paper | Expressive Resynthesis Paper | Prosody-Aware GSLM Paper | Code and Pretrained Models
  • HuBERT: Speech representations for recognition & generation (upgraded Wav2Vec by Facebook)
  • Wav2vec Unsupervised (wav2vec-U) is a framework for building speech recognition
    news.ycombinator.com | 2021-05-21
  • [HELP] I want to translate from Spanish to English. Is there any free api for this with better privacy?
    reddit.com/r/api | 2021-05-18
    GitHub link https://github.com/pytorch/fairseq/tree/master/examples/m2m_100
  • I’ve struck AI gold. Finally, an alternative to AID and indeed all of OpenAI that I haven’t heard anyone mention yet and is IMO just as good as Dragon and ready to use right now
    reddit.com/r/AIDungeon | 2021-05-01
    The model is made by Facebook, but it is released publicly and anybody can run it. You can download it and run it on your own machine (if you have the hardware and technical skill, which the vast majority of people probably do not.) You can get it from GitHub here: https://github.com/pytorch/fairseq/blob/master/examples/megatron_11b/README.md
    reddit.com/r/AIDungeon | 2021-05-01
  • [2104.01027] Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training
    Self-supervised learning of speech representations has been a very active research area but most work is focused on a single domain such as read audio books for which there exist large quantities of labeled and unlabeled data. In this paper, we explore more general setups where the domain of the unlabeled data for pre-training data differs from the domain of the labeled data for fine-tuning, which in turn may differ from the test data domain. Our experiments show that using target domain data during pre-training leads to large performance improvements across a variety of setups. On a large-scale competitive setup, we show that pre-training on unlabeled in-domain data reduces the gap between models trained on in-domain and out-of-domain labeled data by 66%-73%. This has obvious practical implications since it is much easier to obtain unlabeled target domain data than labeled data. Moreover, we find that pre-training on multiple domains improves generalization performance on domains not seen during training. Code and models will be made available at this https URL.

What are some alternatives?

When comparing taro and fairseq you can also consider the following projects:

DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.

text-to-text-transfer-transformer - Code for the paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer"

gpt-neox - An implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library.

widevine-l3-decryptor - A Chrome extension that demonstrates bypassing Widevine L3 DRM

Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

angular-cli - CLI tool for Angular

k2 - FSA/FST algorithms, differentiable, with PyTorch compatibility.

riny-cards - A web application for learning a language with the help of flash cards.

AirMice.Py - Control Mouse using Hand powered by Media Pipe Hand Tracking and Gesture Control for Windows 10 and above

Auto.js - A UiAutomator on android, does not need root access(安卓平台上的JavaScript自动化工具)

vue-introjs - intro.js bindings for Vue.