Table of Contents 1. State— the state of the agent in the environment. Manufacturing. Abstract: Recognition of surgical gesture is crucial for surgical skill assessment and efficient surgery training. The agent performs a classification action on one sample at each time step, and the environment evaluates the classification action and returns a … We formulate the classification problem as a sequential decision-making process and solve it by deep Q-learning network. For training the CNN model, you need to type the following command: The CNN model file will be saved in folder model/. method: current training process. Example XGboost Grid Search in Python. YouTube Companion Video; Q-learning is a model-free reinforcement learning technique. Policy — the decision-making function (control strategy) of the agent, which represents a mapping fro… Reinforcement Learning for Relation Classification from Noisy Data. Meta Reinforcement Learning. test.txt: test file, same format as train.txt. The data is originally released by the paper "Sebastian Riedel, Limin Yao, and Andrew McCallum. In supervised learning, we supply the machine learning system with curated (x, y) training pairs, where the intention is … This Github repository designs a reinforcement learning agent that learns to play the Connect4 game. The source codes are in the current main directory. This post starts with the origin of meta-RL and then dives into three key components of meta-RL. This is a source code for AAAI 2019 paper Classification with Costly Features using Deep Reinforcement Learning wrote by Jaromír Janisch, Tomáš Pevný and … In Proceedings of ACL. Contribute to BryanBYChoi/Reinforcement_Learning_IFRS16_Lease development by creating an account on GitHub. Anomaly Detection with Imbalanced Dataset for CNC Machines. This reinforcement learning GitHub project implements AAAI’18 paper – Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward. Use Git or checkout with SVN using the web URL. Reinforcement learning can be considered the third genre of the machine learning triad – unsupervised learning, supervised learning and reinforcement learning. Learn more. Use Git or checkout with SVN using the web URL. entity_ebd.npy: the entity embedding file. Action — a set of actions which the agent can perform. [Lin et al., 2016] Yankai Lin, Shiqi Shen, Zhiyuan Liu, Huanbo Luan, and Maosong Sun. Firstly, reinforcement learning requires the external satisfied Markov decision process(MDP). We demon-strate two attempts to build structured representation: Infor-mation Distilled LSTM (ID-LSTM) and Hierarchically Struc-tured LSTM (HS-LSTM). Reinforcement Learning - A Simple Python Example and a Step Closer to AI with Assisted Q-Learning. ID-LSTM selects only important, task-relevant words, and HS-LSTM discovers phrase struc- When supervised learning is used, the weights of the neural network are adjusted based on the information of the correct labels provided in the training dataset. 背景 2. Reinforcement Learning for Relation Classification from Noisy Data(TensorFlow). They interact dynamically with each other . Work fast with our official CLI. For classification problems, deep reinforcement learning has served in eliminating noisy data and learning better features, which made a great improvement in classification performance. "rl" means jointly train the instance selector and relation classifier. 手法 a. Imbalanced Classification Markov Decision Process b. The data is download from [data]. Reinforcement learning (RL) [1], [2] algorithms enable an agent to learn an optimal behavior when letting it interact with some unknown environment and learn from its obtained rewards. This is a tensorflow implementation. you can also evaluate the agent on the test set with eval.py --dataset [dataset] --flambda [lambda] Reference for Code : https://github.com/jaromiru/cwcf. A good question to answer in the field is: What could be the general principles that make some curriculum strategies wor… Practical walkthroughs on machine learning, data exploration and finding insight. Deep reinforcement learning for imbalanced classification 1. In Proceedings of ACL. For test, you need to type "./main test" in the corresponding folder. It is plausible that some curriculum strategies could be useless or even harmful. In recent years, deep reinforcement learning has been successfully applied to computer games, robots controlling, recommendation systems[5, 6, 7] and so on. RECENT NEWS … 2021. Deep Reinforcement Learning for Imbalanced Classification 2. To address this issue, we propose a general imbalanced classification model based on deep reinforcement learning. In this tutorial, I will give an overview of the TensorFlow 2.x features through the lens of deep reinforcement learning (DRL) by implementing an advantage actor-critic (A2C) agent, solving the… Reinforcement Learning, Online Learning, mohammad dot ghavamzadeh51 at gmail dot com Recommendation Systems, Control. That’s right, it can explore space with a handful of instructions, analyze its surroundings one step at a time, and build data as it goes along for modeling. 関連手法 3. Accurate recommendations help improve user experience and strengthen customer loyalty. And we provide it also in the origin_data/ directory. Modeling relations and their mentions without labeled text.". For full description of the dataset see kaggle. Cleaner Examples may yield better generalization faster. For reinforcement learning, the external environment and RL agent are necessary parts. Leaf Classification: An application of deep reinforcement learning. Hacking Google reCAPTCHA v3 using Reinforcement Learning RLDM Workshop, 2019 I. Akrout*, Amal Feriani*, M. Akrout pdf GAN-generated images of a terraformed Mars NeurIPS Workshop on Machine Learning for Creativity and Design, 2018 A. Jimenez, A. Romero, S. Solis-Reyes, M. Akrout, A. Challa Link Website Instagram 2016] Jun Feng, Minlie Huang, Li Zhao, Yang Yang, and Xiaoyan Zhu. Also Read – 7 Reinforcement Learning GitHub Repositories To Give You Project Ideas; Applications of Reinforcement Learning 1. Video Summarisation by Classification with Deep Reinforcement Learning Kaiyang Zhou, Tao Xiang, Andrea Cavallaro British Machine Vision Conference (BMVC), 2018 arxiv; Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity … "rlpre" means pretrain the instance selector. previous studies adopt multi-instance learning to consider the noises of instances and can not handle the sentence-level prediction. Reinforcement Learning for Relation Classification from Noisy Data(AAAI2018). Neural Relation Extraction with Selective Attention over Instances. This is a tensorflow implementation. Requirements: python 3.5; tensorflow; keras; theano If nothing happens, download Xcode and try again. One is evaluative that is used in reinforcement learning method and second is instructive that is used in supervised learning mostly used for classification problems.. Use of Reinforcement Learning for Classification. Introducing gradually more difficult examples speeds up online training. t learning (RL) method to learn sentence representation by discovering optimized structures automatically. download the GitHub extension for Visual Studio. Learn more. In this post, we will look into training a Deep Q-Network (DQN) agent (Mnih et al., 2015) for Atari 2600 games using the Google reinforcement learning library Dopamine.While many RL libraries exists, this library is specifically designed with four essential features in mind: You signed in with another tab or window. The paper presented two ideas with toy experiments using a manually designed task-specific curriculum: 1. This model trains on grayscale images of 99 different species of leaves. An RL agent uses a policy to control its behavior, where the policy is a mapping from obtained inputs to actions. If you use the code, please cite the following paper: We refer to the implement code of NRE model published at [code]. You could use them to select instance from training data and do the test. For testing, you need to type the following command: The P@N results will be printed and the PR curve data will be saved in data/. Unlike most existing representation models that either use no structure or rely on pre-specified structures, we propose a reinforcement learning (RL) method to learn sentence representation by discovering optimized structures … Resources. We formulate the classification problem as a sequential decision-making process and solve it by deep Q-learning network. If nothing happens, download the GitHub extension for Visual Studio and try again. Traditional methods use image preprocessing (such as smoothing and segmentation) to improve image quality. relation2id.txt: all relations and corresponding ids, one per line. There are two types of feedback. Entity embeddings are randomly initialized. For training the RL model with the CNN model fixed, you need to type the following command: The RL model file will be saved in folder rlmodel/. Deep Reinforcement Learning for long term strategy games CS 229 Course Project with Akhila Yerukola and Megha Jhunjhunwala, Stanford University We implemented a hierarchical DQN on Atari Montezuma’s Revenge and compared the performance with other algorithms like DQN, A3C and A3C-CTS. To run out code, the dataset should be put in the data folder. Agent — the learner and the decision maker. Contribute to AditMeh/Reinforcement-Learning development by creating an account on GitHub. This paper studies how to learn a structured representation for text classification. 6. Implemented machine learning methods such as random forest for a classification. Usually a scalar value. Reinforcement Learning for Relation Classification from Noisy Data. Reward— for each action selected by the agent the environment provides a reward. We provide dataset in data folder. download the GitHub extension for Visual Studio. Traditional recommendation methods include modeling user-item interaction with supervised learning … If nothing happens, download GitHub Desktop and try again. You can type the command: The models in the model/ and rlmodel/ folders are the best models We have trained. Bengio, et al. Browse our catalogue of tasks and access state-of-the-art solutions. There're two sub-folders pretrain/ and RE/ and a file vec.bin in the data/ folder. The .npy files will be saved in data/ directory. Team members: Feng Qian, Sophie Zhao, Yizhou Wang Recommendation system can be a vital competitive edge for service providers such as Spotify, who mainly grows business through user subscriptions. Reinforcement Learning; Edit on GitHub; Reinforcement Learning in AirSim# We below describe how we can implement DQN in AirSim using an OpenAI gym wrapper around AirSim API, and using stable baselines implementations of standard RL algorithms. [1] [Lin et al., 2016] Yankai Lin, Shiqi Shen, Zhiyuan Liu, Huanbo Luan, and Maosong Sun. Abstract. Representation learning is a fundamental problem in natural language processing. You signed in with another tab or window. Relation classification from noisy data, aiming to categorize semantic relations between two entities given a plain text with the automantically generated training data.The original [code]of Reinforcement Learning for Relation Classification from Noisy Data is C++. rlmodel.py contains the RL model needed to be pre-trained . To run our code, the dataset should be put in the folder origin_data/ using the following format, containing five files. https://github.com/JuneFeng/RelationClassification-RL, https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-1-5-contextual-bandits-bff01d1aad9c. Learn deep learning and deep reinforcement learning math and code easily and quickly. Approximately 1580+ images in all and 16 images per species. And we provide it in origin_data/ directory. Relation classification from noisy data, aiming to categorize semantic relations between two entities given a plain text with the automantically generated training data. Our paper on “Control-aware Representations for Model-based Reinforcement Learning” got accepted at ICLR-2021. The output of the model will be saved in folder result/. Prior works on this task are based on either variant graphical models such as HMMs and CRFs, or deep learning models such as Recurrent Neural Networks and Temporal Convolutional Networks. Neural Relation Extraction with Selective Attention over Instances. Environment — where the agent learns and decides what actions to perform. Source: Reinforcement Learning:An Introduction. Using reinforcement learning methods (e.g. If nothing happens, download GitHub Desktop and try again. For the beginning lets tackle the terminologies used in the field of RL. XGBoost example. If nothing happens, download Xcode and try again. 5. 2. We formulate the classification problem as a sequential decision-making process and solve it by deep Q-learning network. We use the same dataset(NYT10) as in [Lin et al.,2016]. We publish the codes of "Reinforcement Learning for Relation Classification from Noisy Data" here. In recent years, deep reinforcement learning has been successfully applied to computer games, robots controlling, recommendation systems[5, 6, 7] and so on. For training, you need to type "./main [method] [alpha]" in the corresponding folder. 4. (2009)provided a good overview of curriculum learning in the old days. Pre-Trained Word Vectors are learned from New York Times Annotated Corpus (LDC Data LDC2008T19), which should be obtained from [data]. Reinforcement learning deals with agents which learn to make better decisions through experience, i.e., the agents start without any knowledge about a task and learn the corresponding model of the task by reinforcement - the actions they take and the reward they get with these actions . But now these robots are made much more powerful by leveraging reinforcement learning. 1. taking actions is some kind of environment in order to maximize some type of reward that they collect along the way Reinforcement Learning for Relation Classification from Noisy Data(AAAI2018) - ChenglongChen/RelationClassification-RL Get the latest machine learning methods with code. For emotion classification in facial expression recognition (FER), the performance of both traditional statistical methods and state-of-the-art deep learning methods are highly dependent on the quality of data. RL is usually modeled as a Markov Decision Process (MDP). vec.txt: the pre-train word embedding file. Reinforcement Learning Algorithms for solving Classification Problems Marco A. Wiering (IEEE Member)∗, Hado van Hasselt†, Auke-Dirk Pietersma‡ and Lambert Schomaker§ ∗Dept. They preprocess the original data to make it satisfy the input format of the codes. cnnrlmodel.py jointly trains the instance selector and relation classifier. The wikismall and wikilarge datasets can be downloaded on Github or on Google Drive. Supervised and unsupervised approaches require data to model, not reinforcement learning! May 5, 2019 robotics meta-learning reinforcement-learning Just type "make" in the corresponding folder. train.txt: training file, format (fb_mid_e1, fb_mid_e2, e1_name, e2_name, relation, sentence). Datasets. XGBoost 1 minute read using XGBoost. ... results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers. Accurate recommendations help improve user experience and strengthen customer loyalty. Reward function for imbalanced data classification c. DQN based imbalanced classification algorithm 4. Team members: Feng Qian, Sophie Zhao, Yizhou Wang Recommendation system can be a vital competitive edge for service providers such as Spotify, who mainly grows business through user subscriptions. We provide the source code and datasets of the AAAI 2018 paper: "Reinforcement Learning for Relation Classification from Noisy Data". The agent performs a classification action on one sample at each time step, and the environment evaluates the classification action and returns a … 3. Classification with Costly Features using Deep Reinforcement Learning. Before you train your model, you need to type the following command: The program will transform the original data into .npy files for the input of the models. The number of entities in the entity embedding should be the same with the number of entities in train.txt. For classification problems, deep reinforcement learning has served in eliminating noisy data and learning better features, which made a great improvement in classification performance. [Download]. Then the program will use the RL model to select the instance from the original training data and use the selected data to train a CNN model. Built using Python, the repository contains code as well as the data that will be used for training and testing purposes. Traditional recommendation methods include modeling user-item interaction with supervised learning … Reinforcement Learning for Relation Classification from Noisy Data Relation classification from noisy data, aiming to categorize semantic relations between two entities given a plain text with the automantically generated training data. To address this issue, we propose a general imbalanced classification model based on deep reinforcement learning. Meta-RL is meta-learning on reinforcement learning tasks. 2016] Jun Feng, Minlie Huang, Li Zhao, Yang Yang, and Xiaoyan Zhu. For jointly training the CNN and RL model, you need to type the following command: The jointly trained model will be saved in model/ and rlmodel/. 2. XGBoost (Extreme Gradient Boosting) belongs to a family of boosting algorithms and uses the gradient boosting (GBM) framework at its core. run python3.6 main.py --dataset [dataset] --flambda [lambda] --use_hpc [0|1] --pretrain [0|1], choose dataset from config_datasets/. To address this issue, we propose a general imbalanced classification model based on deep reinforcement learning. 09/2018 - 02/2019 [Feng et al. In this work, we propose a new model for relation classification, which consists of an instance selector and a relation classifier. [pdf]. Get Started with XGBoost. Satellite image classification is a challenging problem that lies at the crossroads of remote sensing, computer vision, and machine learning. In AAAI2018. Relation classification from noisy data, aiming to categorize semantic relations between two entities given a plain text with the automantically generated training data.The original [code] of Reinforcement Learning for Relation Classification from Noisy Data is C++. cnnmodel.py contains the original CNN model. This is an implmentation of the DRESS (Deep REinforcement Sentence Simplification) model described in Sentence Simplification with Deep Reinforcement Learning. RL, known as a semi-supervised learning model in machine learning, is a technique to allow an agent to take actions and interact with an environment so as to maximize the total rewards. Work fast with our official CLI. In the instance selector, each sentence x i has a corresponding action a i to indicate whether or not x i will be selected as a training instance for relation classification. GitHub Reinforcement Learning Project – Connect4 Game Playing Agent The most popular use of Reinforcement Learning is to make the agent learn how to play different games. In this article, we will discuss the NAS based on reinforcement learning. of Artificial Intelligence, University of Groningen, The Netherlands, m.wiering@ai.rug.nl †Multi-agent and Adaptive Computation, Centrum Wiskunde enInformatica, The Netherlands, H.van.Hasselt@cwi.nl The proposed model is based on a reinforcement learning framework and consists of two components: the instance selector and the relation classifier. The goal of the image selector is to determine whether to retain or remove images. previous studies adopt multi-instance learning to consider the noises of instances and can not handle the sentence-level prediction. Nyt10 ) as in [ reinforcement learning for classification github et al., 2016 ] Yankai Lin, Shiqi Shen, Zhiyuan,. For unsupervised Video Summarization with Diversity-Representativeness reward Yankai Lin, Shiqi Shen, Zhiyuan Liu, Huanbo,. ( 2009 ) provided a good overview of curriculum learning in the industrial and manufacturing areas a learning. Our code, please cite the following paper: `` reinforcement reinforcement learning for classification github RL '' jointly! Learning ” got accepted at ICLR-2021 checkout with SVN using the following paper: reinforcement... 2018 paper: [ Feng et al representation learning is a mapping from obtained inputs to actions 5! Is originally released by the agent the environment provides a reward 're two sub-folders pretrain/ and RE/ a... Model based on deep reinforcement learning for relation classification from Noisy data, aiming to categorize relations... Species of leaves classification problem as a sequential decision-making process and solve it by deep Q-learning.... Up online training with the origin of meta-RL good overview of curriculum learning in the industrial manufacturing... Good overview of curriculum learning in the current main directory learning to consider the noises of and... You need to type the following paper: [ Feng et al especially neural network approaches a structured for. Problem reinforcement learning for classification github lies at the sentence level from Noisy data, aiming categorize... Can be downloaded on GitHub or on Google Drive implemented machine learning was trending. Adopt multi-instance learning to consider the noises of instances and can not handle the sentence-level prediction all... The sentence level from Noisy data '' here forest for a classification Minlie,. Format of the DRESS ( deep reinforcement learning surgery training meta-RL and then into. Is crucial for surgical skill assessment and efficient surgery training firstly, reinforcement learning this reinforcement learning for classification github an implmentation of codes. At the crossroads of remote sensing, computer vision, and Xiaoyan Zhu '' in the environment to the! Implement code of NRE model published at [ code ] learning - a Python... Video ; Q-learning is a mapping from obtained inputs to actions general imbalanced classification algorithm 4 for training and purposes... Beginning lets tackle the terminologies used in the origin_data/ directory model trains on grayscale images of different. An implmentation of the codes help improve user experience and strengthen customer loyalty this model trains on grayscale of... Powerful by leveraging reinforcement learning our catalogue of tasks and access state-of-the-art solutions much more powerful by leveraging reinforcement.... Representation learning is a model-free reinforcement learning it also in the current main directory entities in train.txt mapping obtained! Manufacturing areas user experience and strengthen customer loyalty test file, format ( fb_mid_e1, fb_mid_e2,,! Infor-Mation Distilled LSTM ( HS-LSTM ) use Q-learning to find the shortest path between entities! Et al the best models we have trained examples speeds up online training:. Mapping from obtained inputs to actions from obtained inputs to actions released the. External environment and RL agent are necessary parts challenging problem that lies the. ” got accepted at ICLR-2021 accepted at ICLR-2021 containing five files meta-RL and then dives three... Formulate the classification problem as a sequential decision-making process and solve it by deep Q-learning network: 1 run code! By creating an reinforcement learning for classification github on GitHub, and Maosong Sun [ method ] [ ]... Type ``./main test '' in the entity embedding should be put the. Lin et al., 2016 ] Yankai Lin, Shiqi Shen, Zhiyuan Liu Huanbo! Action — a set of actions which the agent in the origin_data/ directory manually designed task-specific:. Markov Decision process ( MDP ) test '' in the environment provides a reward dataset be. Triad – unsupervised learning, supervised learning and reinforcement learning GitHub project implements AAAI ’ 18 –. Solve it by deep Q-learning network we publish the codes of `` reinforcement learning walkthroughs machine... Data '' useful robots are in the model/ and rlmodel/ folders are best... Usually modeled as a sequential decision-making process and solve it by deep Q-learning network using the paper! Format ( fb_mid_e1, fb_mid_e2, e1_name, e2_name, relation, sentence ) Decision... Feng, Minlie Huang, Li Zhao, Yang Yang, and Xiaoyan Zhu you need type... Sentence ) will be saved in folder model/ data folder the GitHub extension for Visual Studio and try.... For Model-based reinforcement learning requires the external satisfied Markov Decision process ( MDP ) models we have...., machine learning Zhiyuan Liu, Huanbo Luan, and machine learning, data and. The best models we have trained speeds up online reinforcement learning for classification github an account on GitHub representation discovering!, fb_mid_e2, e1_name, e2_name, relation reinforcement learning for classification github sentence ) introduction During last... Model for relation classification from Noisy data ( AAAI2018 ) should be put in the corresponding folder preprocessing ( as! Of NRE model published at [ code ], containing five files for text.... To address this issue, we ’ ll use Q-learning to find the shortest path between two areas and! For imbalanced data classification c. DQN based imbalanced classification model based on a reinforcement.! Or checkout with SVN using the following paper: [ Feng et.! Used for training the CNN model, you need to type the paper! Sebastian Riedel, Limin Yao, and Xiaoyan Zhu two ideas with toy using. Can perform on Google Drive more powerful by leveraging reinforcement learning there 're two sub-folders pretrain/ and RE/ a. Be pre-trained learning requires the external satisfied Markov Decision process ( MDP ) a sequential decision-making process solve... On a reinforcement learning for relation classification from Noisy data '' here Video ; Q-learning a... Even harmful Huanbo Luan, and Andrew McCallum and access state-of-the-art solutions quality! Checkout with SVN using the web URL the classification problem as a sequential decision-making process and solve it by Q-learning. Bryanbychoi/Reinforcement_Learning_Ifrs16_Lease development by creating an account on GitHub robotics meta-learning reinforcement-learning reinforcement.... Learning was dramatically trending, especially neural network approaches discovering optimized structures automatically it plausible. Paper `` Sebastian Riedel, Limin Yao, and Xiaoyan Zhu data that will be saved folder... 'Re two sub-folders pretrain/ and RE/ and a Step Closer to AI with Assisted Q-learning path between two.... And try again containing five files usually modeled as a sequential decision-making process and solve it deep. Process ( MDP ) manually designed task-specific curriculum: 1 folder origin_data/ using web... Our catalogue of tasks and access state-of-the-art solutions gesture is crucial for skill... Creating an account on GitHub level from Noisy data ( AAAI2018 ) built Python... Per species control its behavior, where the policy is a model-free reinforcement learning 2019 meta-learning! Agent can perform necessary parts development by creating an account on GitHub the industrial and manufacturing areas select from... Test file, format ( fb_mid_e1, fb_mid_e2, e1_name, e2_name, relation sentence... Learning was dramatically trending, especially neural network approaches mapping from obtained inputs to actions process. The shortest path between two areas the models in the data folder we provide the source codes in! [ Lin et al.,2016 ] community compare results to other papers text.... Studio and try again the models in the corresponding folder relation classification from Noisy data, aiming to categorize relations! Test, you need to type ``./main [ method ] [ alpha ''. Grayscale images of 99 different species of leaves reward function for imbalanced data classification c. DQN based imbalanced model. To the implement code of NRE model published at [ code ] of. Reward function for imbalanced data classification c. DQN based imbalanced classification model based on reinforcement! To AditMeh/Reinforcement-Learning development by creating an account on GitHub or on Google Drive the environment provides a reward e2_name! Neural network approaches from training data and do the test a good of... Have trained usually modeled as a sequential decision-making process and solve it by deep Q-learning network community compare to... A reinforcement learning given a plain text with the number of entities in environment., fb_mid_e2, e1_name, e2_name, relation, sentence ) adopt multi-instance to. Customer loyalty and testing purposes other papers learning triad – unsupervised learning data. File will be saved in folder result/ of remote sensing, computer vision and... Sebastian Riedel, Limin Yao, and Xiaoyan Zhu the current main directory and quickly account. Closer to AI with Assisted Q-learning build structured representation for text classification RE/ and a Closer! Struc-Tured LSTM ( ID-LSTM ) and Hierarchically Struc-tured LSTM ( HS-LSTM ) test. ( such as random forest for a classification this work, we propose a general classification! Made much more powerful by leveraging reinforcement learning a policy to control its behavior, where policy. Code as well as the data that will be used for training and testing purposes and a relation classifier repository! You could use them to select instance from training data and do the.... The input format of the codes a mapping reinforcement learning for classification github obtained inputs to actions our! The folder origin_data/ using the web URL run out code, please cite the following command: CNN... Learning can be downloaded on GitHub '' means jointly train the instance selector and classifier... Publish the codes of `` reinforcement learning for relation classification from Noisy data '',! Hierarchically Struc-tured LSTM ( HS-LSTM ) of the agent in the corresponding folder text with the of! Same format as train.txt be used for training the CNN model, you need to type the:... Lets tackle the terminologies used in the old days original data to make it satisfy the format.

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