Datasets:
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"""
a quick script for getting wordcounts of all danish words in gigaword
"""
# import torch
# import torch.multiprocessing as mp
# mp.set_start_method('spawn', force=True)
# torch.set_num_threads(1)
import json
import os
from collections import Counter, defaultdict
from pathlib import Path
# from dacy.download import download_model, DEFAULT_CACHE_DIR
from typing import List, Optional, Tuple
import spacy
# model = "da_dacy_large_tft-0.0.0"
word_freq_path = "/data/DAGW/word_freqs"
dagw_sektioner = "/data/DAGW/dagw-master/sektioner"
# download_model(model, DEFAULT_CACHE_DIR)
# path = os.path.join(DEFAULT_CACHE_DIR, model)
nlp = spacy.load("da_core_news_lg", exclude=["parser", "ner"])
# nlp.get_pipe("transformer").model.attrs["flush_cache_chance"] = 0.1
Path(word_freq_path).mkdir(parents=True, exist_ok=True)
sections = os.listdir(dagw_sektioner)
filepaths = {}
for p in sections:
subpath = os.path.join(dagw_sektioner, p)
filepaths[p] = [
os.path.join(subpath, p)
for p in os.listdir(subpath)
if p != "LICENSE" and not p.endswith(".jsonl")
]
def wordpiece_group_text(text, size=500):
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"Maltehb/-l-ctra-danish-electra-small-uncased", strip_accents=False
)
out = tokenizer.encode(text, add_special_tokens=False)
prv = 0
for i in range(size, len(out), size):
yield tokenizer.decode(out[prv:i])
prv = i
if prv < len(out):
yield tokenizer.decode(out[prv : len(out)])
def group_text(text, size=2400):
length = len(text)
prv = 0
for i in range(size, length, size):
yield text[prv:i]
prv = i
if prv < length:
yield text[prv:length]
def text_gen(filepaths):
for i, file in enumerate(filepaths):
if i % 10000 == 0:
print("\t", i, "/", len(filepaths))
with open(file, "r") as f:
text = f.read()
for t in group_text(text):
yield t
class WordCounter:
def __init__(self, l: Optional[List] = None):
self.dict = defaultdict(lambda: defaultdict(int))
if l is not None:
self.add(l)
def add(self, l: list):
for token, pos in l:
self.dict[token][pos] += 1
def __add__(self, other):
for k_tok in other.dict:
if k_tok in self.dict:
for pos, count in other.dict[k_tok].items():
self.dict[k_tok][pos] += count
else:
self.dict[k_tok] = other.dict[k_tok]
return self
for sec in filepaths:
print("Starting Section:", sec)
docs = nlp.pipe(texts=text_gen(filepaths[sec]), n_process=10, batch_size=8)
n = 0
word_counts = WordCounter()
for i, doc in enumerate(docs, start=1):
word_counts += WordCounter([(t.text, t.tag_) for t in doc])
if i % 10000 == 0:
with open(
os.path.join(word_freq_path, f"wordfreq_{sec}_{n}.json"), "w"
) as f:
json_str = json.dumps(word_counts.dict)
f.write(json_str)
word_counts = WordCounter()
n += 1
with open(os.path.join(word_freq_path, f"wordfreq_{sec}_{n}.json"), "w") as f:
json_str = json.dumps(word_counts.dict)
f.write(json_str)
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