def train_kenlm_language_model(input_data_paths, output_model_dir):
output_model_dir = Path(output_model_dir)
output_model_dir.mkdir(exist_ok=True, parents=True)
output_model_path = output_model_dir / 'kenlm_model.arpa'
with log_action('Training tokenizer'):
tokenizer = SentencePieceBPETokenizer()
tokenizer.train([str(path) for path in input_data_paths], vocab_size=20000)
tokenizer.save(str(output_model_dir), 'spm_tokenizer')
with log_action('Tokenizing'):
tokenized_data_paths = get_temp_filepaths(len(input_data_paths))
for tokenized_data_path, input_data_path in zip(tokenized_data_paths, input_data_paths):
encodings = tokenizer.encode_batch(read_lines(input_data_path))
write_lines([' '.join(encoding.tokens) for encoding in encodings], tokenized_data_path)
with log_action('Training language model'):
kenlm_path = input('Please provide the path to the lmplz script (install at https://github.com/kpu/kenlm): ')
command = (
f'cat {" ".join([str(path) for path in tokenized_data_paths])} | {kenlm_path} -o 3 > {output_model_path}'
)
run_command(command, mute=False)
[path.unlink() for path in tokenized_data_paths]
return output_model_dir
class TextProcessor:
def __init__(self, tok_model_path: Optional[str] = None):
self.languages = {}
if tok_model_path is not None:
self.tokenizer = SentencePieceBPETokenizer(
tok_model_path + "/vocab.json",
tok_model_path + "/merges.txt",
)
with open(os.path.join(tok_model_path, "langs"), "rb") as fp:
self.languages: Dict[str, int] = pickle.load(fp)
self.init_properties(self.languages)
def init_properties(self, languages: Dict[str, int] = None):
self.max_len = 512
self.pad_token = "<pad>"
self.mask_token = "<mask>"
self.unk_token = "<unk>"
self.sep_token = "</s>"
self.bos = "<s>"
self.special_tokens = [
self.pad_token, self.bos, self.unk_token, self.mask_token,
self.sep_token
] + list(languages.keys())
self.languages = languages
def train_tokenizer(self, paths: List[str], vocab_size: int,
to_save_dir: str, languages: Dict[str, int]):
self.tokenizer = SentencePieceBPETokenizer()
self.init_properties(languages)
self.tokenizer.train(files=paths,
vocab_size=vocab_size,
min_frequency=5,
special_tokens=self.special_tokens)
self.save(directory=to_save_dir)
def _tokenize(self, line) -> Encoding:
return self.tokenizer.encode(line)
def save(self, directory):
self.tokenizer.save(directory)
with open(os.path.join(directory, "langs"), "wb") as fp:
pickle.dump(self.languages, fp)
def tokenize_one_line(self,
line,
ignore_middle_eos: bool = False) -> List[int]:
tokenized = []
spl = [sen for sen in line.split("</s>") if len(sen.strip()) > 0]
if spl[0].startswith("<"):
words = spl[0].strip().split(" ")
spl[0] = " ".join(words[1:])
tokenized += [self.token_id(words[0])]
for sen in spl:
tokenized += self._tokenize(sen).ids
if not ignore_middle_eos:
tokenized += [self.sep_token_id()]
if ignore_middle_eos:
tokenized += [self.sep_token_id()]
return tokenized
def tokenize_one_sentence(self, line) -> List[int]:
"""
Assume that the sentence has language id in the first token and end of sentence as the end!
:param line:
:return:
"""
spl = line.strip().split(" ")
lang_id, sen, eos = spl[0], " ".join(spl[1:-1]), spl[-1]
tokenized = [self.token_id(lang_id)
] + self._tokenize(sen).ids + [self.token_id(eos)]
return tokenized
def tokenize_lines(self,
line,
blind_split: bool = False,
split_len: int = 512) -> List[List[int]]:
"""
:param line:
:param blind_split: If True, just splits the tokenized data into chunks without considering that every vector
should start with a first word in sentence.
:return:
"""
tokenized = []
if len(self.languages) > 0:
spl = [sen for sen in line.split("</s>") if len(sen.strip()) > 0]
lang_id = []
if spl[0].startswith("<"):
words = spl[0].strip().split(" ")
lang_id = [self.token_id(words[0])]
spl[0] = " ".join(words[1:])
max_len = 0
for sen in spl:
toks = self._tokenize(sen).ids
tokenized += lang_id + toks + [self.sep_token_id()]
max_len = max(max_len, len(toks) + 1)
else:
tokenized = self._tokenize(line.strip()).ids
if blind_split:
num_pads = (split_len - (len(tokenized) % split_len))
pad_arr = [self.pad_token_id()] * num_pads
tokenized = np.array(tokenized + pad_arr)
reshaped = tokenized.reshape((-1, split_len))
return reshaped
else:
return self.split_tokenized(tokenized, min(max_len, self.max_len))
def tokenize(self, lines) -> List[List[int]]:
lines = [
line.strip() for line in lines.strip().split("\n")
if len(line.strip()) > 0
]
tokenized = self.tokenizer.encode_batch(lines)
return [tok.ids for tok in tokenized]
def pad_token_id(self) -> int:
return self.tokenizer.token_to_id(self.pad_token)
def mask_token_id(self) -> int:
return self.tokenizer.token_to_id(self.mask_token)
def unk_token_id(self) -> int:
return self.tokenizer.token_to_id(self.unk_token)
def bos_token_id(self) -> int:
return self.tokenizer.token_to_id(self.bos)
def sep_token_id(self) -> int:
return self.tokenizer.token_to_id(self.sep_token)
def token_id(self, token: str) -> int:
tok_id = self.tokenizer.token_to_id(token)
if tok_id is None:
return 0
return tok_id
def id2token(self, id: int) -> str:
return self.tokenizer.id_to_token(id)
def vocab_size(self) -> int:
return self.tokenizer.get_vocab_size()
def is_lang(self, id) -> bool:
return self.tokenizer.id_to_token(id) in self.languages
def lang_id(self, tok):
if tok in self.languages:
return self.languages[tok]
return 0
def split_tokenized(self,
tokenized: List[int],
max_length: int = 512) -> List[List[int]]:
"""
Based on self.max_len, splits very long sequences to smaller ones.
Here we assume to not have any overlapping sequences.
If the first token is a language, we add it to every new sequence.
:return:
"""
if len(tokenized) <= max_length:
sequences = [tokenized]
sequences[-1] = sequences[-1] + (
max_length - len(sequences[-1])) * [self.pad_token_id()]
return sequences
has_lang = self.is_lang(tokenized[0])
sequence = tokenized[0:] if has_lang else tokenized
seq_len = len(sequence)
sep_id = self.sep_token_id()
max_len = max_length - 1 if has_lang else max_length
cur_start = 0
sequences = []
built_seq = []
truncated = False # Shows if previous sequence is truncated due to its length.
used_ends = set()
while cur_start < seq_len:
if not truncated or not has_lang:
cur_end = min(seq_len, cur_start + max_len)
else:
cur_end = min(seq_len, cur_start + max_len + 1)
subseq = sequence[cur_start:cur_end]
built_seq += subseq
sep_positions = [
i for i, id in enumerate(built_seq) if id == sep_id
]
if len(sep_positions) > 0:
if sep_positions[-1] in used_ends:
truncated = True
else:
built_seq = built_seq[:sep_positions[-1] + 1]
truncated = False
else:
truncated = True
assert built_seq[-1] == sequence[len(built_seq) - 1]
if has_lang and len(subseq) < max_len + 1:
subseq = [tokenized[0]] + subseq
sequences.append(subseq)
cur_start = len(built_seq)
used_ends.add(cur_start - 1)
if len(sequences[-1]) < max_length:
sequences[-1] = sequences[-1] + (
max_length - len(sequences[-1])) * [self.pad_token_id()]
assert built_seq[-1] == sequence[len(built_seq) - 1]
return sequences