Source code translator using the power of deep learning.
Create a new .env file in the root project directory with the following dotenv schema:
# ---------------------------------------
# Theory API
# ---------------------------------------
#
# Debug mode (verbose output)
DEBUG=1
# Language-version pair this API instance is dedicated to
LVP=cobol_to_csharp_9
# Output directory path containing saved checkpoints and tokenizer vocabularies
MODEL_DIR=output
# Training dataset path
TRAIN_DATASET_PATH=data/cobol_to_csharp_9_train.csv
# Validation dataset path
VALID_DATASET_PATH=data/cobol_to_csharp_9_valid.csv
# ---------------------------------------
# Theory neural network hyperparameters
# ---------------------------------------
#
# Buffer size
BUFFER_SIZE=20000
# Batch size
BATCH_SIZE=256
# Number of layers
NUM_LAYERS=6
# Model dimensionality
D_MODEL=512
# Dense feed-forward neural network units/neurons
DFF=2048
# Number of attention heads
NUM_HEADS=8
# Dropout rate
DROPOUT_RATE=0.1
# ---------------------------------------
# AWS
# ---------------------------------------
#
# Access key ID
AWS_ACCESS_KEY_ID=
# Secret access key
AWS_SECRET_ACCESS_KEY=
# S3 bucket name
AWS_STORAGE_BUCKET_NAME=
python train.py \
# * Saved model and checkpoint directory
--out="output"
# * LVP name
--lvp=cobol_to_csharp_9
# Training dataset path
--train-data="data/train.csv"
# Validation dataset path
--valid-data="data/valid.csv"
# Epochs
--epochs=1000
# Buffer size
--buffer-size=20000
# Batch size
--batch-size=256
# Number of layers
--layers=6
# Model dimensionality
--d-model=512
# Dense forward-feed network size (units/neurons)
--dff=2048
# Number of attention heads
--heads=8
# Dropout rate
--dropout=0.1
# Whether to enable Weights & Biases (wandb) integration
# See here for more info: https://docs.wandb.ai/quickstart
--wandb=True
# * = optionalpython run_api.pyFor endpoint documentation, please see the Postman collection.
This script masks a given file and outputs it as MASKED_<name> in the same directory.
python run_mask.py \
# Language-version pair
--lvp cobol_to_csharp_9 \
# Source file path
--file="path/to/file.cs"This script masks a file and creates a data generator with all unique lines (for use with theory_data_gen).
python create_data_generator.py \
# Language-version pair
--lvp cobol_to_csharp_9 \
# Source file path
--file="path/to/file.cs" \
# Source code repo or project name
--name="author/name" \
# Target name (added to comments in output file)
--target="C# 9"Copyright © 2021 Autumn Labs, Inc. All Rights Reserved.
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