AY23S1 SC4002 NLP Assignment - G06

Installation

To set up the required environment, use the provided environment file with Conda: conda env create -f environment.yml Alternatively, ensure you have the following prerequisites installed for inference:

Python 3.x
PyTorch
Gensim

Quick inference

Part 1

Download pre-trained models: best_model_bilstm.pth

wget https://github.com/Yuxinn-J/SC4002_G06/releases/download/v1/best_model_bilstm.pth -P pretrained_models

For one-shot mode, pass a sentence as an argument:
```
python NER/inference.py "European Union rejects German call to boycott British lamb."
```
- The output will display NER tags for each token in the input sentence.
For interactive mode, invoke the script with the --interactive flag:
```
python NER/inference.py --interactive
```
- The script will prompt you to enter sentences one by one and will output the predicted tags for each, until you type exit.
Notes:
- Make sure ./NER/best_model_bilstm.pth is the correct name and path of your saved model.
- Save the Word2Vec model locally to speed up future usage.

Part 2

To train and test seven different models on Question_Classification task.

* part2.ipynb

To fine-tune the transformers under different settings.

chmod a+x start_transformers.sh
./start_transformers.sh

* You may need to activate a Python virtual environment and adjust the path to align with the relevant files as necessary.

Submission Files

The directory structure is outlined below, with individual files for different parts of the assignment:

NER
│   Part1_1.ipynb                                 // Explore word embedding
│   Part1_2.ipynb                                 // Analyze dataset                 
│   Part1_3_Models.ipynb                          // Initial model performance benchmarking
│   Part1_3_Tuning.ipynb                          // Optimal BiLSTM setting discovery
│   Part1_3_Final.ipynb                           // Final model training and testing
│   hyperparameter_tuning_results.json            // Results of hyper-parameter tuning
│   train.py                                      // Train code (quite messy...)
│   inference.py                                  // Inference code
|   best_model_bilstm.pth                         // best model weight
|
Question_Classification
|   part2.ipynb                                   // Train and test models
|   part2_fine_tune.py                            // Fine-tune transformers under different settings
|   start_transformers.sh                         // Script to run the transformers training tasks
|   Transformers_log.out                          // Transformers training log file
|   
└─── model_weights_transformers                   // weights of tranformers 
|
datasets
│─── CoNLL2003
│─── TREC
|
archive

Yuxinn-J / SC4002_G06