Spelling-Error-Detection

Project Report

The project report (as a blogpost) can be found at project-spelling-error-detection.md

Steps to reproduce:

1. Clone the Github / SVN repo (for people from Chair of Prof. Bast)

git clone https://github.com/georgestanley/Spelling-Error

svn co https://ad-svn.informatik.uni-freiburg.de/student-projects/stanley-george

2. Go to the respective working branch (Do this only if specifically mentioned by someone)

3. Create a docker image by the following steps:

   1. Build the Image:

      docker build -t stanley-george-project .

   2. Run the container. Here we also have to mount the datasets folder if one is interested in training on the complete dataset.

      docker run -it -p 8050:8050 --gpus=all --name stanley-george-project -v /nfs/students/stanley-george/data:/app/data/ stanley-george-project

   Note: To remove an existing image with the same name, use the command:

   docker rm stanley-george-project

4. Now that code and data is in place, let's try them:

   1. Train a spell-classifier (from application folder).

      For E.g. To train the LSTM spell classifier with context and Semi-Character encoding, execute the command:

      python -m application.lstm_spell_classifier_w_context --data_folder=data

      A table of the different input parameters that can be provided are listed in Section xx

   2. Evaluate of the test dataset.

      Execute the same file as in step (i) except set the mode to test

      python -m application.lstm_spell_classifier_w_context --mode=test

   3. Run all the Unit-tests.

      Execute the shell script which triggers all unit-test files as: ./application/run_tests.sh

   4. Run individual unit-tests.

      All the uni-tests are in the application/tests folder. You can trigger them as below from the parent directory:

      python -m application.tests.test_lstm_spell_classifier_w_context

5. Makefile

   1. Type make to see the possible options
   2. Type make start_app_console to start the app in the console mode
   3. Type make start_app_webapp to start the app as a Webapp made using Dash (Go to localhost:8050)
   4. Type make start_app_file_eval to start the app and test a text file
   5. Type make run_unit_tests to do PEP8 code checkstyle
   6. Type make checkstyle to do PEP8 code checkstyle

Input Arguments

Name	Datatype	Default Values Semi-Character with Context	Default Values Semi-Character without Context	Default Values One-hot encoded with context
data_folder	String	data	data	data
output_root	String	results	results	results
input_file	String	dev_10.jsonl	top_100_words.json	dev_10.jsonl
val_file	String	'bea60k.repaired.val/bea60_sentences_val_truth_and_false.json	bea60k.repaired.val/bea60_words_val_truth_and_false.json	bea60k.repaired.val/bea60_sentences_val_truth_and_false.json
epochs	int	10	10	10
lr	float	0.001	0.01	0.001
bs	int	1000	1000	32
hidden_dim	int	100	100	100
hidden_layers	int	2	2	2
max_len	int	NA	NA	60
lower_case_mode	bool	False	NA	False
mode	String	train	train	train
eval_model_path	String	trained_models/semi_character_w_context.pth	trained_models/semi_character_wo_context.pth	trained_models/onehot_w_context.pth
eval_file	String	bea60k.repaired.test/bea60_sentences_test_truth_and_false.json	bea60k.repaired.test//bea60_words_test_truth_and_false.json	bea60k.repaired.test//bea60_sentences_test_truth_and_false.json

Directory Structure

|-- Makefile
|-- README.md
|-- Dockerfile
|-- results
|-- runs
|-- application
|   |-- Model.py
|   |-- __init__.py
|   |-- app.py
|   |-- lstm_spell_classifier_w_context.py
|   |-- lstm_spell_classifier_w_context_onehot.py
|   |-- lstm_spell_classifier_wo_context.py
|   |-- run_tests.sh
|   |-- tests
|   |-- utils
|-- bashrc
|-- data
|-- trained_models
`-- requirements.txt

Folder/file definitions

Folder	Description
results	The output folder for an experiment gets generated here and houses the saved models and log files
runs	Contains the tensorboard metrics logs.
application	Contains the important codes related to our application
data	The data files needed which serve as input to our models
trained_models	Contains the pretrained models which can be used for evaluation purpose.