| # torchMoji examples | |
| ## Initialization | |
| [create_twitter_vocab.py](create_twitter_vocab.py) | |
| Create a new vocabulary from a tsv file. | |
| [tokenize_dataset.py](tokenize_dataset.py) | |
| Tokenize a given dataset using the prebuilt vocabulary. | |
| [vocab_extension.py](vocab_extension.py) | |
| Extend the given vocabulary using dataset-specific words. | |
| [dataset_split.py](dataset_split.py) | |
| Split a given dataset into training, validation and testing. | |
| ## Use pretrained model/architecture | |
| [score_texts_emojis.py](score_texts_emojis.py) | |
| Use torchMoji to score texts for emoji distribution. | |
| [encode_texts.py](encode_texts.py) | |
| Use torchMoji to encode the text into 2304-dimensional feature vectors for further modeling/analysis. | |
| ## Transfer learning | |
| [finetune_youtube_last.py](finetune_youtube_last.py) | |
| Finetune the model on the SS-Youtube dataset using the 'last' method. | |
| [finetune_insults_chain-thaw.py](finetune_insults_chain-thaw.py) | |
| Finetune the model on the Kaggle insults dataset (from blog post) using the 'chain-thaw' method. | |
| [finetune_semeval_class-avg_f1.py](finetune_semeval_class-avg_f1.py) | |
| Finetune the model on the SemeEval emotion dataset using the 'full' method and evaluate using the class average F1 metric. | |