163 lines
5.3 KiB
Python
163 lines
5.3 KiB
Python
import random
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from tqdm import tqdm
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from transformers import AutoTokenizer
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import json
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from datasets import load_dataset
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from tokenizers import (
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decoders,
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models,
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normalizers,
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pre_tokenizers,
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processors,
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trainers,
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Tokenizer,
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)
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import os
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random.seed(42)
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def train_tokenizer():
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# 读取JSONL文件并提取文本数据
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def read_texts_from_jsonl(file_path):
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with open(file_path, 'r', encoding='utf-8') as f:
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for line in f:
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data = json.loads(line)
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yield data['text']
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data_path = './dataset/tokenizer_train.jsonl'
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# 初始化tokenizer
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tokenizer = Tokenizer(models.BPE())
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tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False)
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# 定义特殊token
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special_tokens = ["<unk>", "<s>", "</s>"]
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# 设置训练器并添加特殊token
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trainer = trainers.BpeTrainer(
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vocab_size=6400,
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special_tokens=special_tokens, # 确保这三个token被包含
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show_progress=True,
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initial_alphabet=pre_tokenizers.ByteLevel.alphabet()
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)
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# 读取文本数据
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texts = read_texts_from_jsonl(data_path)
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# 训练tokenizer
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tokenizer.train_from_iterator(texts, trainer=trainer)
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# 设置解码器
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tokenizer.decoder = decoders.ByteLevel()
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# 检查特殊token的索引
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assert tokenizer.token_to_id("<unk>") == 0
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assert tokenizer.token_to_id("<s>") == 1
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assert tokenizer.token_to_id("</s>") == 2
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# 保存tokenizer
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tokenizer_dir = "./minimind_tokenizer"
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os.makedirs(tokenizer_dir, exist_ok=True)
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tokenizer.save(os.path.join(tokenizer_dir, "tokenizer.json"))
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tokenizer.model.save("./minimind_tokenizer")
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# 手动创建配置文件
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config = {
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"add_bos_token": False,
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"add_eos_token": False,
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"add_prefix_space": True,
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"added_tokens_decoder": {
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"0": {
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"content": "<unk>",
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"lstrip": False,
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"normalized": False,
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"rstrip": False,
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"single_word": False,
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"special": True
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},
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"1": {
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"content": "<s>",
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"lstrip": False,
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"normalized": False,
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"rstrip": False,
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"single_word": False,
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"special": True
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},
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"2": {
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"content": "</s>",
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"lstrip": False,
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"normalized": False,
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"rstrip": False,
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"single_word": False,
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"special": True
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}
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},
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"additional_special_tokens": [],
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"bos_token": "<s>",
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"clean_up_tokenization_spaces": False,
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"eos_token": "</s>",
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"legacy": True,
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": None,
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"sp_model_kwargs": {},
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"spaces_between_special_tokens": False,
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"tokenizer_class": "PreTrainedTokenizerFast",
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"unk_token": "<unk>",
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"use_default_system_prompt": False,
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"chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ system_message }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<s>user\\n' + content + '</s>\\n<s>assistant\\n' }}{% elif message['role'] == 'assistant' %}{{ content + '</s>' + '\\n' }}{% endif %}{% endfor %}"
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}
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# 保存配置文件
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with open(os.path.join(tokenizer_dir, "tokenizer_config.json"), "w", encoding="utf-8") as config_file:
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json.dump(config, config_file, ensure_ascii=False, indent=4)
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print("Tokenizer training completed and saved.")
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def eval_tokenizer():
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from transformers import AutoTokenizer
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# 加载预训练的tokenizer
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tokenizer = AutoTokenizer.from_pretrained("./model/minimind_tokenizer")
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messages = [
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{"role": "system", "content": "你是一个优秀的聊天机器人，总是给我正确的回应！"},
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{"role": "user", "content": '是椭圆形的'},
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{"role": "assistant", "content": '456'},
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{"role": "user", "content": '456'},
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{"role": "assistant", "content": '789'}
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]
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new_prompt = tokenizer.apply_chat_template(
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messages,
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tokenize=False
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)
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print(new_prompt)
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# 获取词汇表大小（不包括特殊符号）
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print('tokenizer词表大小：', tokenizer.vocab_size)
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# 获取实际词汇表长度（包括特殊符号）
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actual_vocab_size = len(tokenizer)
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print('qwen实际词表长度：', actual_vocab_size)
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new_prompt = 'wenjie，椭圆和⚪的关系是什么呢？因为明天下午要带家人去下医院，所以申请上午在家办公，因为明天下午要带家人去下医院，所以申请上午在家办公，因为明天下午要带家人去下医院，所以申请上午在家办公，下午请半天假~@LWJWe '
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print(new_prompt)
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model_inputs = tokenizer(new_prompt)
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print(model_inputs)
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print('长度：', len(model_inputs['input_ids']))
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input_ids_ = model_inputs['input_ids']
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response = tokenizer.decode(input_ids_)
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print(response, end='')
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def main():
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# train_tokenizer()
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eval_tokenizer()
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if __name__ == '__main__':
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main()
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