SimBERT升级版（SimBERTv2）！

Last update: Dec 31, 2022

Related tags

Deep Learning roformer-sim

Overview

RoFormer-Sim

RoFormer-Sim，又称SimBERTv2，是我们之前发布的SimBERT模型的升级版。

介绍

https://kexue.fm/archives/8454

训练

tensorflow 1.14 + keras 2.3.1 + bert4keras 0.10.6

下载

chinese_roformer-sim-char_L-12_H-768_A-12.zip(提取码：2cgz)
chinese_roformer-sim-char_L-6_H-384_A-6.zip(提取码：h68q)
chinese_roformer-sim-char-ft_L-12_H-768_A-12.zip(提取码：w15n)

引用

Bibtex：

@techreport{roformer-sim,
  title={RoFormer-Sim: Integrating Retrieval and Generation into RoFormer},
  author={Jianlin Su},
  year={2021},
  url="https://github.com/ZhuiyiTechnology/roformer-sim",
}

联系

邮箱：[email protected]

链接

追一科技：https://zhuiyi.ai

Owner

Zhuiyi Technology is a leading enterprise intelligent service AI company in China. We focus on deep learning and NLP.

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SimBERT升级版（SimBERTv2）！

Related tags

Overview

RoFormer-Sim

介绍

训练

下载

引用

联系

链接

Owner

Using multidimensional LSTM neural networks to create a forecast for Bitcoin price

bio_inspired_min_nets_improve_the_performance_and_robustness_of_deep_networks

Optimizers-visualized - Visualization of different optimizers on local minimas and saddle points.

Implementation of TimeSformer, a pure attention-based solution for video classification

pytorch implementation of fast-neural-style

A general python framework for visual object tracking and video object segmentation, based on PyTorch

Implementation of Nyström Self-attention, from the paper Nyströmformer

This is a collection of our NAS and Vision Transformer work.

FEDn is an open-source, modular and ML-framework agnostic framework for Federated Machine Learning

Shared Attention for Multi-label Zero-shot Learning

object recognition with machine learning on Respberry pi

Official PyTorch implementation for paper "Efficient Two-Stage Detection of Human–Object Interactions with a Novel Unary–Pairwise Transformer"

Implementation of ConvMixer-Patches Are All You Need? in TensorFlow and Keras

Repo for EMNLP 2021 paper "Beyond Preserved Accuracy: Evaluating Loyalty and Robustness of BERT Compression"

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