In this project, I trained a LSTM, a Transformer and a BERT model to solve the NER tagging problem on the dataset Conll2003.

First, install the following packages:

• pytorch
• seqeval
• transformers
• tensorboard You can run pip install pytorch seqeval transformers tensorboard to install them all. You can also use conda install command if you are in an activated anaconda environment, this method is more recommended.

Run optimize.py, this will automatically find the best parameters for LSTM and Transformer. Then modify model_config.py to set the parameters you find that works best or just some numbers you like. If you only want to train one model: you shoud run train_xxx.py. Replace xxx with the model name you like. Otherwise, run main.py. This will train the models. You can run tensorboard to monitor the training progress. The command is tensorboard --logdir runs. After training, run write_lstm_and_transformer_out.py and write_bert_out.py to write the outputs.

This project includes 5 sub-directories.

• conll 2003 The dataset directory. It is empty when I submitted it.
• logs The directory that stores all the outputs of each step. I did most of the experiments on the Universty's GPU farm, and used nohup python xxx.py > xxx.log & command to output all the logs. I didn't use logging package because nohup command is pretty handful. Also, I need to mention that I only put the last version of the logs in the directory, because I believe no one wants to see my countless errors during the experiment.
• models The directory that stores all the trained models. It is empty when I submitted it.
• out The directory that includes the results of the predictions. The format of each file is exactly the same as test.txt, I of changed the last element of each line to the predictions.
• runs The directory that stores the tensorboard files, recording loss on the training set, f1, precision and recall scores on the valid set each epoch.

I will introduce these scripts in logical order rather than filename order.

• datapreprocess.py This script loads data from the dataset and constructs the word map and label map. It also batches the data. This is for LSTM and Transformer.
• seqProc.py This script provides the tokenizer and decoder funtion for LSTM and Transformer.
• dataset.py This script provides the dataloader for BERT. I should have unified this with LSTM and Transformer, but due to time constraints I couldn't make it. Any way, both dataloaders run.
• models.py The LSTMTagger, TransformerTagger and BERTTagger source codes written in pytorch. I will explain the details in the report.
• optimize.py Using optuna package to search for the best parameters, including layer numbers, neuron numbers, loss weights etc. Both for LSTM and Transformer.
• model_config.py Using pickle to save the model is easy yet not graceful. I saved the models with torch.save(model.state_dict(), f"models/{model_type}_{epoch}.pt") However, this makes it model has to be created before dict is loaded. Modifying all the files is not a good experience. So I stored the parameters in the config file so that only modifying the config file is enough for every step of the experiment.
• evaluate.py Provides a general evaluate function for different models and even for different seqeval functions. It is super cool.
• train_lstm.py Train LSTM model or provide train_lstm() function.
• train_transformer.py Train Transformer model or provide train_transformer() function.
• train_bert.py Train BERT model or provide train_bert() function.
• main.py Train all the models.
• write_lstm_and_transformer_out.py Predict LSTM and Transformer on the test set, then write the output files.
• write_bert_out.py Predict BERT on the test set and write the outputs.