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summarize.py
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summarize.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Aug 5 21:56:35 2019
@author: WT
"""
from utils.misc import save_as_pickle
from summarization.trainer import train_and_fit
from summarization.evaluate import infer
from argparse import ArgumentParser
import logging
logging.basicConfig(format='%(asctime)s [%(levelname)s]: %(message)s', \
datefmt='%m/%d/%Y %I:%M:%S %p', level=logging.INFO)
logger = logging.getLogger('__file__')
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--data_path", type=str, default="C:/Users/WT/Desktop/Python_Projects/NLP/TextSummarisation/cnn_stories/cnn/stories/",\
help="Full path to CNN dataset")
parser.add_argument("--level", type=str, default="bpe", help="Level of tokenization (word, char or bpe)")
parser.add_argument("--bpe_word_ratio", type=float, default=0.7, help="Ratio of BPE to word vocab")
parser.add_argument("--bpe_vocab_size", type=int, default=7000, help="Size of bpe vocab if bpe is used")
parser.add_argument("--max_features_length", type=int, default=200, help="Max length of features (word, char or bpe level)")
parser.add_argument("--d_model", type=int, default=128, help="Transformer model dimension")
parser.add_argument("--ff_dim", type=int, default=128, help="Transformer Feed forward layer dimension")
parser.add_argument("--num", type=int, default=6, help="Transformer number of layers per block")
parser.add_argument("--n_heads", type=int, default=4, help="Transformer number of attention heads")
parser.add_argument("--LAS_embed_dim", type=int, default=128, help="LAS Embedding dimension")
parser.add_argument("--LAS_hidden_size", type=int, default=128, help="LAS listener hidden_size")
parser.add_argument("--batch_size", type=int, default=32, help="Batch size")
parser.add_argument("--num_epochs", type=int, default=8000, help="No of epochs")
parser.add_argument("--lr", type=float, default=0.0003, help="learning rate")
parser.add_argument("--gradient_acc_steps", type=int, default=2, help="Number of steps of gradient accumulation")
parser.add_argument("--max_norm", type=float, default=1.0, help="Clipped gradient norm")
parser.add_argument("--model_no", type=int, default=0, help="Model ID: 0 = Transformer, 1 = LAS")
parser.add_argument("--train", type=int, default=1, help="Train model on dataset")
parser.add_argument("--infer", type=int, default=0, help="Infer input sentence labels from trained model")
args = parser.parse_args()
save_as_pickle("args.pkl", args)
if args.train:
train_and_fit(args)
if args.infer:
infer(args, from_data=True)