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convert_tf_bert_model_to_pytorch

将 tensorflow 版本的预训练 bert model 转化为 pytorch 版本

google-research

Google Research

grasscutter

A server software reimplementation for a certain anime game.

morethan-log

😎 A static blog using notion database

ner

命名实体识别实践与探索

rand.github.io

rich-game

2021级种子班软件工程大富翁游戏

somecode

ssrand.github.io

stable-diffusion

Optimized Stable Diffusion modified to run on lower GPU VRAM

transformers

🤗 Transformers: State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX.

transparent-instrument-attitude-detection

仪器姿态识别，实例分割

twitter-sentiment-analysis---analytics-vidhya

Problem Statement The objective of this task is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets. Formally, given a training sample of tweets and labels, where label '1' denotes the tweet is racist/sexist and label '0' denotes the tweet is not racist/sexist, your objective is to predict the labels on the test dataset. Motivation Hate speech is an unfortunately common occurrence on the Internet. Often social media sites like Facebook and Twitter face the problem of identifying and censoring problematic posts while weighing the right to freedom of speech. The importance of detecting and moderating hate speech is evident from the strong connection between hate speech and actual hate crimes. Early identification of users promoting hate speech could enable outreach programs that attempt to prevent an escalation from speech to action. Sites such as Twitter and Facebook have been seeking to actively combat hate speech. In spite of these reasons, NLP research on hate speech has been very limited, primarily due to the lack of a general definition of hate speech, an analysis of its demographic influences, and an investigation of the most effective features. Data Our overall collection of tweets was split in the ratio of 65:35 into training and testing data. Out of the testing data, 30% is public and the rest is private. Data Files train.csv - For training the models, we provide a labelled dataset of 31,962 tweets. The dataset is provided in the form of a csv file with each line storing a tweet id, its label and the tweet. There is 1 test file (public) test_tweets.csv - The test data file contains only tweet ids and the tweet text with each tweet in a new line.

videos

Code for the manim-generated scenes used in 3blue1brown videos