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if [ ! -f glove.840B.300d.txt ]; then | ||
wget http://nlp.stanford.edu/data/glove.840B.300d.zip -O glove.840B.300d.zip | ||
unzip glove.840B.300d.zip | ||
fi | ||
python preprocess_intent.py | ||
python preprocess_slot.py |
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import json | ||
import logging | ||
import pickle | ||
import re | ||
from argparse import ArgumentParser, Namespace | ||
from collections import Counter | ||
from pathlib import Path | ||
from random import random, seed | ||
from typing import List, Dict | ||
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import torch | ||
from tqdm.auto import tqdm | ||
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from utils import Vocab | ||
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logging.basicConfig( | ||
format="%(asctime)s | %(levelname)s | %(message)s", | ||
level=logging.INFO, | ||
datefmt="%Y-%m-%d %H:%M:%S", | ||
) | ||
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def build_vocab( | ||
words: Counter, vocab_size: int, output_dir: Path, glove_path: Path | ||
) -> None: | ||
common_words = {w for w, _ in words.most_common(vocab_size)} | ||
vocab = Vocab(common_words) | ||
vocab_path = output_dir / "vocab.pkl" | ||
with open(vocab_path, "wb") as f: | ||
pickle.dump(vocab, f) | ||
logging.info(f"Vocab saved at {str(vocab_path.resolve())}") | ||
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glove: Dict[str, List[float]] = {} | ||
logging.info(f"Loading glove: {str(glove_path.resolve())}") | ||
with open(glove_path) as fp: | ||
row1 = fp.readline() | ||
# if the first row is not header | ||
if not re.match("^[0-9]+ [0-9]+$", row1): | ||
# seek to 0 | ||
fp.seek(0) | ||
# otherwise ignore the header | ||
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for i, line in tqdm(enumerate(fp)): | ||
cols = line.rstrip().split(" ") | ||
word = cols[0] | ||
vector = [float(v) for v in cols[1:]] | ||
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# skip word not in words if words are provided | ||
if word not in common_words: | ||
continue | ||
glove[word] = vector | ||
glove_dim = len(vector) | ||
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assert all(len(v) == glove_dim for v in glove.values()) | ||
assert len(glove) <= vocab_size | ||
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num_matched = sum([token in glove for token in vocab.tokens]) | ||
logging.info( | ||
f"Token covered: {num_matched} / {len(vocab.tokens)} = {num_matched / len(vocab.tokens)}" | ||
) | ||
embeddings: List[List[float]] = [ | ||
glove.get(token, [random() * 2 - 1 for _ in range(glove_dim)]) | ||
for token in vocab.tokens | ||
] | ||
embeddings = torch.tensor(embeddings) | ||
embedding_path = output_dir / "embeddings.pt" | ||
torch.save(embeddings, str(embedding_path)) | ||
logging.info(f"Embedding shape: {embeddings.shape}") | ||
logging.info(f"Embedding saved at {str(embedding_path.resolve())}") | ||
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def main(args): | ||
seed(args.rand_seed) | ||
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intents = set() | ||
words = Counter() | ||
for split in ["train", "eval"]: | ||
dataset_path = args.data_dir / f"{split}.json" | ||
dataset = json.loads(dataset_path.read_text()) | ||
logging.info(f"Dataset loaded at {str(dataset_path.resolve())}") | ||
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intents.update({instance["intent"] for instance in dataset}) | ||
words.update( | ||
[token for instance in dataset for token in instance["text"].split()] | ||
) | ||
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intent2idx = {tag: i for i, tag in enumerate(intents)} | ||
intent_tag_path = args.output_dir / "intent2idx.json" | ||
intent_tag_path.write_text(json.dumps(intent2idx, indent=2)) | ||
logging.info(f"Intent 2 index saved at {str(intent_tag_path.resolve())}") | ||
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build_vocab(words, args.vocab_size, args.output_dir, args.glove_path) | ||
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def parse_args() -> Namespace: | ||
parser = ArgumentParser() | ||
parser.add_argument( | ||
"--data_dir", | ||
type=Path, | ||
help="Directory to the dataset.", | ||
default="./data/intent/", | ||
) | ||
parser.add_argument( | ||
"--glove_path", | ||
type=Path, | ||
help="Path to Glove Embedding.", | ||
default="./glove.840B.300d.txt", | ||
) | ||
parser.add_argument("--rand_seed", type=int, help="Random seed.", default=13) | ||
parser.add_argument( | ||
"--output_dir", | ||
type=Path, | ||
help="Directory to save the processed file.", | ||
default="./cache/intent/", | ||
) | ||
parser.add_argument( | ||
"--vocab_size", | ||
type=int, | ||
help="Number of token in the vocabulary", | ||
default=10_000, | ||
) | ||
args = parser.parse_args() | ||
return args | ||
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if __name__ == "__main__": | ||
args = parse_args() | ||
args.output_dir.mkdir(parents=True, exist_ok=True) | ||
main(args) |
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import json | ||
import logging | ||
from argparse import ArgumentParser, Namespace | ||
from collections import Counter | ||
from pathlib import Path | ||
from random import seed | ||
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from preprocess_intent import build_vocab | ||
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logging.basicConfig( | ||
format="%(asctime)s | %(levelname)s | %(message)s", | ||
level=logging.INFO, | ||
datefmt="%Y-%m-%d %H:%M:%S", | ||
) | ||
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def main(args): | ||
seed(args.rand_seed) | ||
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tags = set() | ||
words = Counter() | ||
for split in ["train", "eval"]: | ||
dataset_path = args.data_dir / f"{split}.json" | ||
dataset = json.loads(dataset_path.read_text()) | ||
logging.info(f"Dataset loaded at {str(dataset_path.resolve())}") | ||
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tags.update({tag for instance in dataset for tag in instance["tags"]}) | ||
words.update([token for instance in dataset for token in instance["tokens"]]) | ||
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tag2idx = {tag: i for i, tag in enumerate(tags)} | ||
tag_idx_path = args.output_dir / "tag2idx.json" | ||
tag_idx_path.write_text(json.dumps(tag2idx, indent=2)) | ||
logging.info(f"Tag 2 index saved at {str(tag_idx_path.resolve())}") | ||
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build_vocab(words, args.vocab_size, args.output_dir, args.glove_path) | ||
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def parse_args() -> Namespace: | ||
parser = ArgumentParser() | ||
parser.add_argument( | ||
"--data_dir", | ||
type=Path, | ||
help="Directory to the dataset.", | ||
default="./data/slot/", | ||
) | ||
parser.add_argument( | ||
"--glove_path", | ||
type=Path, | ||
help="Path to Glove Embedding.", | ||
default="./glove.840B.300d.txt", | ||
) | ||
parser.add_argument("--rand_seed", type=int, help="Random seed.", default=13) | ||
parser.add_argument( | ||
"--output_dir", | ||
type=Path, | ||
help="Directory to save the processed file.", | ||
default="./cache/slot/", | ||
) | ||
parser.add_argument( | ||
"--vocab_size", | ||
type=int, | ||
help="Number of token in the vocabulary", | ||
default=10_000, | ||
) | ||
args = parser.parse_args() | ||
return args | ||
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if __name__ == "__main__": | ||
args = parse_args() | ||
args.output_dir.mkdir(parents=True, exist_ok=True) | ||
main(args) |
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