google-research/FLAN 排名第 9,966，有 1,574 个星标。
同类别比较
google-research/FLAN 排名第 9,966，此处有 8 个同类条目；maxpumperla/elephas 排名第 9,967。
同类别比较| 名称 | 排名 | 指标 |
|---|
| modelscope/AgentEvolver | 9,949 | - 语言:
- Python
- 星标数:
- 1,578
- 分叉数:
- 172
- 简介:
- AgentEvolver: Towards Efficient Self-Evolving Agent System
|
|---|
| photosynthesis-team/piq | 9,951 | - 语言:
- Python
- 星标数:
- 1,578
- 分叉数:
- 123
- 简介:
- Measures and metrics for image2image tasks. PyTorch.
|
|---|
| BloodAxe/pytorch-toolbelt | 9,957 | - 语言:
- Python
- 星标数:
- 1,575
- 分叉数:
- 126
- 简介:
- PyTorch extensions for fast R&D prototyping and Kaggle farming
|
|---|
| FluxML/Zygote.jl | 9,963 | - 语言:
- Julia
- 星标数:
- 1,574
- 分叉数:
- 221
- 简介:
- 21st century AD
|
|---|
| maxpumperla/elephas | 9,967 | - 语言:
- Python
- 星标数:
- 1,574
- 分叉数:
- 303
- 简介:
- Distributed Deep learning with Keras & Spark
|
|---|
| sdv-dev/CTGAN | 9,968 | - 语言:
- Python
- 星标数:
- 1,574
- 分叉数:
- 334
- 简介:
- Conditional GAN for generating synthetic tabular data.
|
|---|
| vlawhern/arl-eegmodels | 9,970 | - 语言:
- Python
- 星标数:
- 1,573
- 分叉数:
- 334
- 简介:
- This is the Army Research Laboratory (ARL) EEGModels Project: A Collection of Convolutional Neural Network (CNN) models for EEG signal classification, using Keras and Tensorflow
|
|---|
| IDEA-Research/MaskDINO | 9,976 | - 语言:
- Python
- 星标数:
- 1,571
- 分叉数:
- 163
- 简介:
- [CVPR 2023] Official implementation of the paper "Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation"
|
|---|
关键参数与规格
关键参数与规格| 参数 | 值 |
|---|
| 语言 | Python |
|---|
| 类别 | Model Development |
|---|
| 子类别 | Dataset engineering |
|---|
| 星标数 | 1,574 |
|---|
| 分叉数 | 159 |
|---|
| 贡献者 | 6 |
|---|
| 创建日期 | 2021-08-21 |
|---|
| 更新日期 | 2026-10-12 |
|---|
| 1 天星标数 | 0 |
|---|
| 7 天星标数 | -1 |
|---|
| 排名 | 9,966 |
|---|
| 上次排名 | 9,992 |
|---|
| 排名变化 | 26 |
|---|
| 7 天星标排名 | 17,094 |
|---|
| 上次 7 天星标排名 | 17,090 |
|---|
| 7 天星标排名变化 | -4 |
|---|
| 总星标排名 | 7,785 |
|---|
| 上次总星标排名 | 7,780 |
|---|
| 总星标排名变化 | -5 |
|---|
| 国家 | USA |
|---|
README 摘录
The FLAN Instruction Tuning Repository **Original Flan (2021)** | **The Flan Collection (2022)** | **Flan 2021 Citation** | **License** This repository contains code to generate instruction tuning dataset collections. The first is the original Flan 2021, documented in Finetuned Language Models are Zero-Shot Learners, and the second is the expanded version, called the Flan Collection, described in The Flan Collection: Designing Data and Methods for Effective Instruction Tuning and used to produce Flan-T5 and Flan-PaLM. Flan 2021 To generate the
Flan 2021 data as Seqio mixtures, first install the relevant then use mixtures. Flan 2021 Citation Please cite the following if you found Flan 2021 useful in your research. License The code in this repository is licensed according to the LICENSE file. Contact Us To contact us feel free to create an Issue in this repository, or email the respective authors that contributed to this code base: Jason Wei for the Flan 2021 paper, Le Hou for the Scaling Flan
paper, and Shayne Longpre for the Flan Collection.
常见问题
记录了多少星标和分叉？
google-research/FLAN 有 1,574 个星标和 159 个分叉。
仓库排名是多少？
google-research/FLAN 排名第 9,966。
记录的语言和类别是什么？
google-research/FLAN 有 1,574 个星标，语言为 Python，类别为 Model Development。
哪个仓库排名最接近？
google-research/FLAN 与 maxpumperla/elephas 比较，后者排名第 9,967，有 1,574 个星标。
The FLAN Instruction Tuning Repository
Original Flan (2021) | The Flan Collection (2022) | Flan 2021 Citation | License
This repository contains code to generate instruction tuning dataset collections. The first is the original Flan 2021, documented in Finetuned Language Models are Zero-Shot Learners, and the second is the expanded version, called the Flan Collection, described in The Flan Collection: Designing Data and Methods for Effective Instruction Tuning and used to produce Flan-T5 and Flan-PaLM.
Flan 2021
To generate the Flan 2021 data as Seqio mixtures, first install the relevant requirements.txt then use mixtures.py.
Flan 2021 Citation
Please cite the following if you found Flan 2021 useful in your research.
@inproceedings{weifinetuned,
title={Finetuned Language Models are Zero-Shot Learners},
author={Wei, Jason and Bosma, Maarten and Zhao, Vincent and Guu, Kelvin and Yu, Adams Wei and Lester, Brian and Du, Nan and Dai, Andrew M and Le, Quoc V},
booktitle={International Conference on Learning Representations}
}
License
The code in this repository is licensed according to the LICENSE file.
Contact Us
To contact us feel free to create an Issue in this repository, or email the respective authors that contributed to this code base: Jason Wei for the Flan 2021 paper, Le Hou for the Scaling Flan paper, and Shayne Longpre for the Flan Collection.