Lightweight NLP library in pure Python - currently implements a text classifier
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# SPDX-License-Identifier: GPL-3.0-or-later
import unicodedata
from typing import List, cast
import hashlib
import emoji
def tokenize(text: str, max_ngram_length: int = 1) -> List[str]:
text = unicodedata.normalize("NFKD", text).casefold()
parts = []
highest_end = 0
for emoji_part in emoji.emoji_list(text):
parts += list(text[highest_end : emoji_part["match_start"]])
parts.append(emoji_part["emoji"])
highest_end = emoji_part["match_end"]
parts += list(text[highest_end:])
converted_text = [part for part in parts if part]
tokens = [""]
for char in converted_text:
if (
char.isalpha()
or char.isnumeric()
or char == "'"
or (char in ",." and (" " + tokens[-1])[-1].isnumeric())
):
tokens[-1] += char
elif emoji.is_emoji(char):
tokens.append(char)
tokens.append("")
elif tokens[-1] != "":
tokens.append("")
tokens = [string for string in tokens if string]
if max_ngram_length == 1:
return tokens
ngrams = []
for ngram_length in range(1, max_ngram_length + 1):
for index in range(len(tokens) + 1 - ngram_length):
ngrams.append(" ".join(tokens[index : index + ngram_length]))
return ngrams
def _hash_single(token: str, hash_function: type) -> int:
return int.from_bytes(
hash_function(token.encode("utf-8")).digest()[:6], "big"
)
def _get_hash_function(hash_algorithm: str) -> type:
if hash_algorithm in {
"sha224",
"md5",
"sha512",
"sha3_256",
"blake2s",
"sha3_224",
"sha1",
"sha256",
"sha384",
"shake_256",
"blake2b",
"sha3_512",
"shake_128",
"sha3_384",
}:
return cast(type, getattr(hashlib, hash_algorithm))
raise ValueError("not a valid hash function: " + hash_algorithm)
def hash_single(token: str, hash_algorithm: str) -> int:
return _hash_single(token, _get_hash_function(hash_algorithm))
def hash_list(tokens: List[str], hash_algorithm: str) -> List[int]:
hash_function = _get_hash_function(hash_algorithm)
return [_hash_single(token, hash_function) for token in tokens]
def normalize(text: str) -> str:
return " ".join(tokenize(text, 1))