Datasets:
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import gzip
import json
import numpy as np
import pandas as pd
from transformers import AutoTokenizer
COLLATE_LENGTH = 370
def emit(line_id, nl_str, en_str, nl_l, en_l):
obj = {
"id": line_id,
"translation": {
"nl": nl_str.strip(),
"en": en_str.strip(),
},
"nl_len": nl_l,
"en_len": en_l,
}
writer.write(str.encode(json.dumps(obj)))
writer.write("\n".encode("utf-8"))
class TokenLength:
def __init__(self, tokenizer):
self.tokenizer = AutoTokenizer.from_pretrained(
tokenizer, max_length=4096, truncation=False, use_fast=False
)
def __call__(self, text: str):
return len(self.tokenizer.encode(text, max_length=4096, truncation=False))
class Counter:
def __init__(self, start=0):
self.count = start
def __call__(self):
self.count += 1
return self.count
class Buffer:
def __init__(
self,
id: int,
emit_lines: bool,
max_length: int,
en_prefix="",
):
self.id = id
self.emit_lines = emit_lines
self.max_length = max_length
self.en_prefix = en_prefix
self.counter = Counter()
self.nl_l = None
self.en_l = None
self.nl_buf = None
self.en_buf = None
self.cur_max_length = None
self.reset()
def set_cur_max_length(self):
"""You can check the distribution with the following code:
%matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = [9.5,6]
fig, ax = plt.subplots(1, 1)
r = np.random.beta(20,8,102000)
ax.hist(r, density=True, histtype='stepfilled', alpha=0.2, bins=200)
ax.legend(loc='best', frameon=False)
plt.show()
"""
self.cur_max_length = int(self.max_length * np.random.beta(20, 8))
def reset(self):
self.nl_l = None
self.en_l = None
self.nl_buf = None
self.en_buf = None
self.set_cur_max_length()
def add_ok(self, nl_str, en_str, separator="\n"):
"""If the new text fits within the max_length tokens, add it, else return False"""
nl_new = self.nl_buf + f"{separator}{nl_str}" if self.nl_buf else nl_str
en_new = self.en_buf + f"{separator}{en_str}" if self.en_buf else en_str
nl_new_l = token_length(nl_new)
en_new_l = token_length(en_new)
# Check if we can add it or if the result would be too long
if (
nl_new_l > self.cur_max_length
or token_length(self.en_prefix + en_new) > self.cur_max_length
):
return False
else:
self.nl_buf = nl_new
self.en_buf = en_new
self.nl_l = nl_new_l
self.en_l = en_new_l
return True
def emit(self, row, separator):
nl_str = row.translation["nl"]
en_str = row.translation["en"]
nl_id = row.meta["sentenceIds"]["nl"]
en_id = row.meta["sentenceIds"]["en"]
# if one of the sentences ends on a . but the other doesn't, add a dot to the other
if nl_str.endswith(".") and not en_str.endswith("."):
en_str += "."
elif en_str.endswith(".") and not nl_str.endswith("."):
nl_str += "."
# Strip any leading "- " or "- " from the sentences
nl_str = nl_str.lstrip("- ")
en_str = en_str.lstrip("- ")
nl_len = token_length(nl_str)
en_len = token_length(en_str)
if self.emit_lines and nl_len <= COLLATE_LENGTH and en_len <= COLLATE_LENGTH:
emit(
line_id=f"{row.tconst}-nl{nl_id}-en{en_id}-l-",
nl_str=nl_str,
en_str=en_str,
nl_l=nl_len,
en_l=en_len,
)
if self.add_ok(nl_str.strip(), en_str.strip(), separator):
return
# If buf.add returns false, we've hit the maximum length boundary, so emit the current buffer, if it is not Empty
if self.nl_buf:
emit(
line_id=f"{row.tconst}-b{self.id}-{self.counter()}",
nl_str=self.nl_buf,
en_str=self.en_buf,
nl_l=self.nl_l,
en_l=self.en_l,
)
# After emit of the buffer, we reset the buffer
self.reset()
# Add the first line in this new buffer
result = self.add_ok(nl_str.strip(), en_str.strip())
if not result:
self.reset()
if __name__ == "__main__":
token_length = TokenLength(tokenizer="yhavinga/ul2-base-dutch")
line_counter = Counter()
buffers = [
Buffer(
id=index, emit_lines=(index == 0), max_length=buf_max_length, en_prefix=""
)
for index, buf_max_length in enumerate([0.6 * 370, 370])
]
df = pd.read_json("episode_opensubtitles.json.gz", lines=True)
with gzip.open("outfile", mode="wb") as writer:
for row in df.itertuples():
for buffer in buffers:
buffer.emit(row, separator="\n")
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