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""" |
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from https://github.com/openai/gpt-2/, changed for chinese |
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""" |
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import json |
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import os |
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import sentencepiece as spm |
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""" |
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SentencePiece is an unsupervised text tokenizer and detokenizer mainly for Neural Network-based text generation |
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systems where the vocabulary size is predetermined prior to the neural model training. SentencePiece implements |
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subword units (e.g., byte-pair-encoding (BPE) [Sennrich et al.]) and unigram language model [Kudo.]) with the |
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extension of direct training from raw sentences. SentencePiece allows us to make a purely end-to-end |
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system that does not depend on language-specific pre/postprocessing. |
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https://github.com/google/sentencepiece |
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pip install sentencepiece |
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or git clone https://github.com/google/sentencepiece.git |
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python setup.py install |
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""" |
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CURRENT_DIR = os.path.dirname(os.path.abspath(__file__)) |
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PRETRAINED_MODEL_FILE = os.path.join(CURRENT_DIR, "chinese_sentencepiece/cog-pretrain.model") |
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def get_pairs(word): |
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pairs = set() |
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prev_char = word[0] |
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for char in word[1:]: |
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pairs.add((prev_char, char)) |
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prev_char = char |
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return pairs |
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class Encoder: |
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def __init__(self, encoder, bpe_merges): |
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self.encoder = encoder |
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self.decoder = {v: k for k, v in self.encoder.items()} |
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self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges)))) |
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self.cache = {} |
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self.max_len = 0 |
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def bpe(self, token): |
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if token in self.cache: |
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return self.cache[token] |
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word = tuple(token) |
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pairs = get_pairs(word) |
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if not pairs: |
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return token |
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while True: |
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bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf'))) |
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if bigram not in self.bpe_ranks: |
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break |
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first, second = bigram |
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new_word = [] |
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i = 0 |
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while i < len(word): |
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try: |
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j = word.index(first, i) |
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new_word.extend(word[i:j]) |
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i = j |
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except: |
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new_word.extend(word[i:]) |
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break |
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if word[i] == first and i < len(word) - 1 and word[i + 1] == second: |
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new_word.append(first + second) |
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i += 2 |
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else: |
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new_word.append(word[i]) |
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i += 1 |
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new_word = tuple(new_word) |
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word = new_word |
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if len(word) == 1: |
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break |
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else: |
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pairs = get_pairs(word) |
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word = ' '.join(word) |
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self.cache[token] = word |
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return word |
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def encode(self, text): |
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return [self.encoder.get(token, 1) for token in self.tokenize(text)] |
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def decode(self, tokens): |
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text = ''.join([self.decoder[token] for token in tokens]) |
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return text |
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def tokenize(self, text): |
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bpe_tokens = [] |
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bpe_tokens.extend(bpe_token for bpe_token in self.bpe(text).split(' ')) |
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return bpe_tokens |
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def convert_tokens_to_ids(self, tokens): |
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return [self.encoder.get(token, 1) for token in tokens] |
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class Encoder_SP: |
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def __init__(self, model_path): |
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self.sp = spm.SentencePieceProcessor() |
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self.sp.Load(model_path) |
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def encode(self, text): |
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""" |
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text="...." |
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""" |
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return self.sp.EncodeAsIds(text) |
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def decode(self, tokens): |
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""" |
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tokens=[x1,x2,...] |
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""" |
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text = [int(token) for token in tokens] |
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return self.sp.DecodeIds(text) |
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def tokenize(self, text): |
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return self.sp.EncodeAsPieces(text) |
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def convert_tokens_to_ids(self, tokens): |
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return [self.sp.PieceToId(token) for token in tokens] |
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def convert_token_to_id(self, token): |
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return self.sp.PieceToId(token) |
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def convert_id_to_token(self, idx): |
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return self.sp.IdToPiece(idx) |
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def get_encoder(encoder_file, bpe_file): |
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filepath, filename = os.path.split(encoder_file) |
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shotname, extension = os.path.splitext(filename) |
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if (".model" == extension) and (bpe_file == ""): |
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return Encoder_SP(encoder_file) |
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else: |
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with open(encoder_file, 'r', encoding="utf-8") as f: |
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encoder = json.load(f) |
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with open(bpe_file, 'r', encoding="utf-8") as f: |
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bpe_data = f.read() |
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bpe_merges = [tuple(merge_str.split()) for merge_str in bpe_data.split('\n')[1:-1]] |
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return Encoder( |
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encoder=encoder, |
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bpe_merges=bpe_merges, |
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) |
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def from_pretrained(): |
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return get_encoder(PRETRAINED_MODEL_FILE, "") |