✅ This command allows you to specify the username and email address used with your commits. It helps developers figure out who did what, when, and why. Git has become a must-have tool for any developer nowadays, and knowing Git commands is required for developers to utilize Git fully. Git is a distributed version control system and open-source software. Here are the 12 most useful Git Commands for software developers ➡ Git is one of the most popular version control systems. GitHub - Pierian-Data/Complete-Python-3-Bootcamp: Course Files for Complete Python 3 Bootcamp Course on Udemy #pythonprogramminglanguage #programming #github #python #coding #learning #education #linkedin Visit 's website to learn more about the Python programming language. If you enjoyed reading it, don't forget to like, share, and follow me PIYUSH KESARWANI for more tips and resources on topics like this one. ☑ 100+ Python challenging programming exercises. ☑ Python best practices guidebook, written for humans. ☑ Course Files for Complete Python 3 Bootcamp Course on Udemy. Here are the best GitHub resources I would recommend you to follow to learn and practice Python language for Free: Preprocess in embedded in the file.Want to learn Python Programming language without paying a single dollar? If you want to make any changes in training the model including using F1CE loss function or using different hyperparameteres, change the related files which in this instance, they are hyperparameteres.py and f1ce_loss.py.įurthermore, the feature extraction is not embedded in the main model and you need to use methods in feature_extraction.py file to add the features at the end of each sample. |_ multilabel: files to train multilabel classifier |_ data: dictionary used to detect mispelled words |_ models: files to create binary classifiers |_ modified datasets: result of dataset modifier notebook |_ main dataset: includes EmoPars and ArmanEmo datasets |_ dataset modifier: notebook used to create datasets using thresholds or removing uncertain samples |_ augmented datasets: datasets with augmented samples |_ augmentation: notebook used for data augmentation Our model reaches a Macro-averaged F1-score of 0.81 and 0.76 on ArmanEmo and EmoPars, respectively, which are new state-of-the-art results in these benchmarks. In addition, we provide a new policy for selecting data from EmoPars, which selects the high-confidence samples as a result, the model does not see samples that do not have specific emotion during training. Moreover, feature selection is used to enhance the models' performance by emphasizing the text's specific features. Throughout this analysis, we use data augmentation techniques, data re-sampling, and class-weights with Transformer-based Pretrained Language Models(PLMs) to handle the imbalance problem of these datasets. In this paper, we evaluate EmoPars and compare them with ArmanEmo. These datasets, especially EmoPars, are suffering from inequality between several samples between two classes. EmoPars and ArmanEmo are two new human-labeled emotion datasets for the Persian language. With the spread of social media, different platforms like Twitter have become data sources, and the language used in these platforms is informal, making the emotion detection task difficult. Detecting emotion can help us in different fields, including opinion mining. Persian Emotion Detection using ParsBERT and Imbalanced Data Handling Approaches AbstractĮmotion recognition is one of the machine learning applications which can be done using text, speech, or image data gathered from social media spaces.
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