Qwen2大模型微调入门实战-命名实体识别（NER）任务

swanlab
modelscope
transformers
datasets
peft
accelerate
pandas

pip install swanlab modelscope transformers datasets peft pandas accelerate

{
  'text':'今天亚太经合组织第十二届部长级会议在这里开幕，中国外交部部长唐家璇、外经贸部部长石广生出席了会议。',
'entities':[
{
'start_idx':23,
'end_idx':25,
'entity_text':'中国',
'entity_label':'GPE',
'entity_names':['地缘政治实体','政治实体','地理实体','社会实体']},
{
'start_idx':25,
'end_idx':28,
'entity_text':'外交部',
'entity_label':'ORG',
'entity_names':['组织','团体','机构']
},
{
'start_idx':30,
'end_idx':33,
'entity_text':'唐家璇',
'entity_label':'PER',
'entity_names':['人名','姓名']
},
...
],
'data_source':'CCFBDCI'
}

输入：今天亚太经合组织第十二届部长级会议在这里开幕，中国外交部部长唐家璇、外经贸部部长石广生出席了会议。

大模型输出：{'entity_text':'中国', 'entity_label':'组织'}{'entity_text':'唐家璇', 'entity_label':'人名'}...

from modelscope import snapshot_download,AutoTokenizer
from transformers importAutoModelForCausalLM,TrainingArguments,Trainer,DataCollatorForSeq2Seq

model_id ='qwen/Qwen2-1.5B-Instruct'
model_dir ='./qwen/Qwen2-1___5B-Instruct'

# 在modelscope上下载Qwen模型到本地目录下
model_dir = snapshot_download(model_id, cache_dir='./', revision='master')

# Transformers加载模型权重
tokenizer =AutoTokenizer.from_pretrained(model_dir, use_fast=False, trust_remote_code=True)
model =AutoModelForCausalLM.from_pretrained(model_dir, device_map='auto', torch_dtype=torch.bfloat16)
model.enable_input_require_grads()  # 开启梯度检查点时，要执行该方法

from swanlab.integration.huggingface import SwanLabCallback

swanlab_callback = SwanLabCallback(...)

trainer = Trainer(
    ...
    callbacks=[swanlab_callback],
)

|--- train.py
|--- ccfbdci.jsonl

import json
import pandas as pd
import torch
from datasets importDataset
from modelscope import snapshot_download,AutoTokenizer
from swanlab.integration.huggingface importSwanLabCallback
from peft importLoraConfig,TaskType, get_peft_model
from transformers importAutoModelForCausalLM,TrainingArguments,Trainer,DataCollatorForSeq2Seq
import os
import swanlab

def dataset_jsonl_transfer(origin_path, new_path):
'''
    将原始数据集转换为大模型微调所需数据格式的新数据集
    '''
    messages =[]

# 读取旧的JSONL文件
with open(origin_path,'r')as file:
for line in file:
# 解析每一行的json数据
            data = json.loads(line)
            input_text = data['text']
            entities = data['entities']
            match_names =['地点','人名','地理实体','组织']

            entity_sentence =''
for entity in entities:
                entity_json = dict(entity)
                entity_text = entity_json['entity_text']
                entity_names = entity_json['entity_names']
for name in entity_names:
if name in match_names:
                        entity_label = name
break

                entity_sentence += f'''{{'entity_text': '{entity_text}', 'entity_label': '{entity_label}'}}'''

if entity_sentence =='':
                entity_sentence ='没有找到任何实体'

            message ={
'instruction':'''你是一个文本实体识别领域的专家，你需要从给定的句子中提取 地点; 人名; 地理实体; 组织 实体. 以 json 格式输出, 如 {'entity_text': '南京', 'entity_label': '地理实体'} 注意: 1. 输出的每一行都必须是正确的 json 字符串. 2. 找不到任何实体时, 输出'没有找到任何实体'. ''',
'input': f'文本:{input_text}',
'output': entity_sentence,
}

            messages.append(message)

# 保存重构后的JSONL文件
with open(new_path,'w', encoding='utf-8')as file:
for message in messages:
            file.write(json.dumps(message, ensure_ascii=False)+'\n')


def process_func(example):
'''
    将数据集进行预处理
    '''

    MAX_LENGTH =384
    input_ids, attention_mask, labels =[],[],[]
    system_prompt ='''你是一个文本实体识别领域的专家，你需要从给定的句子中提取 地点; 人名; 地理实体; 组织 实体. 以 json 格式输出, 如 {'entity_text': '南京', 'entity_label': '地理实体'} 注意: 1. 输出的每一行都必须是正确的 json 字符串. 2. 找不到任何实体时, 输出'没有找到任何实体'.'''

    instruction = tokenizer(
        f'<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{example['input']}<|im_end|>\n<|im_start|>assistant\n',
        add_special_tokens=False,
)
    response = tokenizer(f'{example['output']}', add_special_tokens=False)
    input_ids = instruction['input_ids']+ response['input_ids']+[tokenizer.pad_token_id]
    attention_mask =(
        instruction['attention_mask']+ response['attention_mask']+[1]
)
    labels =[-100]* len(instruction['input_ids'])+ response['input_ids']+[tokenizer.pad_token_id]
if len(input_ids)> MAX_LENGTH:# 做一个截断
        input_ids = input_ids[:MAX_LENGTH]
        attention_mask = attention_mask[:MAX_LENGTH]
        labels = labels[:MAX_LENGTH]
return{'input_ids': input_ids,'attention_mask': attention_mask,'labels': labels}


def predict(messages, model, tokenizer):
    device ='cuda'
    text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
)
    model_inputs = tokenizer([text], return_tensors='pt').to(device)

    generated_ids = model.generate(
        model_inputs.input_ids,
        max_new_tokens=512
)
    generated_ids =[
        output_ids[len(input_ids):]for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

print(response)

return response


model_id ='qwen/Qwen2-1.5B-Instruct'
model_dir ='./qwen/Qwen2-1___5B-Instruct'

# 在modelscope上下载Qwen模型到本地目录下
model_dir = snapshot_download(model_id, cache_dir='./', revision='master')

# Transformers加载模型权重
tokenizer =AutoTokenizer.from_pretrained(model_dir, use_fast=False, trust_remote_code=True)
model =AutoModelForCausalLM.from_pretrained(model_dir, device_map='auto', torch_dtype=torch.bfloat16)
model.enable_input_require_grads()# 开启梯度检查点时，要执行该方法

# 加载、处理数据集和测试集
train_dataset_path ='ccfbdci.jsonl'
train_jsonl_new_path ='ccf_train.jsonl'

ifnot os.path.exists(train_jsonl_new_path):
    dataset_jsonl_transfer(train_dataset_path, train_jsonl_new_path)

# 得到训练集
total_df = pd.read_json(train_jsonl_new_path, lines=True)
train_df = total_df[int(len(total_df)*0.1):]
train_ds =Dataset.from_pandas(train_df)
train_dataset = train_ds.map(process_func, remove_columns=train_ds.column_names)


config =LoraConfig(
    task_type=TaskType.CAUSAL_LM,
    target_modules=['q_proj','k_proj','v_proj','o_proj','gate_proj','up_proj','down_proj'],
    inference_mode=False,# 训练模式
    r=8,# Lora 秩
    lora_alpha=32,# Lora alaph，具体作用参见 Lora 原理
    lora_dropout=0.1,# Dropout 比例
)

model = get_peft_model(model, config)

args =TrainingArguments(
    output_dir='./output/Qwen2-NER',
    per_device_train_batch_size=4,
    per_device_eval_batch_size=4,
    gradient_accumulation_steps=4,
    logging_steps=10,
    num_train_epochs=2,
    save_steps=100,
    learning_rate=1e-4,
    save_on_each_node=True,
    gradient_checkpointing=True,
    report_to='none',
)

swanlab_callback =SwanLabCallback(
    project='Qwen2-NER-fintune',
    experiment_name='Qwen2-1.5B-Instruct',
    description='使用通义千问Qwen2-1.5B-Instruct模型在NER数据集上微调，实现关键实体识别任务。',
    config={
'model': model_id,
'model_dir': model_dir,
'dataset':'qgyd2021/chinese_ner_sft',
},
)

trainer =Trainer(
    model=model,
    args=args,
    train_dataset=train_dataset,
    data_collator=DataCollatorForSeq2Seq(tokenizer=tokenizer, padding=True),
    callbacks=[swanlab_callback],
)

trainer.train()

# 用测试集的随机20条，测试模型
# 得到测试集
test_df = total_df[:int(len(total_df)*0.1)].sample(n=20)

test_text_list =[]
for index, row in test_df.iterrows():
    instruction = row['instruction']
    input_value = row['input']

    messages =[
{'role':'system','content': f'{instruction}'},
{'role':'user','content': f'{input_value}'}
]

    response = predict(messages, model, tokenizer)
    messages.append({'role':'assistant','content': f'{response}'})
    result_text = f'{messages[0]}\n\n{messages[1]}\n\n{messages[2]}'
    test_text_list.append(swanlab.Text(result_text, caption=response))

swanlab.log({'Prediction': test_text_list})
swanlab.finish()

import torch
from transformers importAutoModelForCausalLM,AutoTokenizer
from peft importPeftModel

def predict(messages, model, tokenizer):
    device ='cuda'

    text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    model_inputs = tokenizer([text], return_tensors='pt').to(device)

    generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=512)
    generated_ids =[output_ids[len(input_ids):]for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]
    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

return response


# 加载原下载路径的tokenizer和model
tokenizer =AutoTokenizer.from_pretrained('./qwen/Qwen2-1___5B-Instruct/', use_fast=False, trust_remote_code=True)
model =AutoModelForCausalLM.from_pretrained('./qwen/Qwen2-1___5B-Instruct/', device_map='auto', torch_dtype=torch.bfloat16)

# 加载训练好的Lora模型，将下面的[checkpoint-XXX]替换为实际的checkpoint文件名名称
model =PeftModel.from_pretrained(model, model_id='./output/Qwen2-NER/checkpoint-1700')

input_text ='西安电子科技大学的陈志明爱上了隔壁西北工业大学苏春红，他们约定好毕业后去中国的苏州定居。'
test_texts ={
'instruction':'''你是一个文本实体识别领域的专家，你需要从给定的句子中提取 地点; 人名; 地理实体; 组织 实体. 以 json 格式输出, 如; {'entity_text': '南京', 'entity_label': '地理实体'} 注意: 1. 输出的每一行都必须是正确的 json 字符串. 2. 找不到任何实体时, 输出'没有找到任何实体'. ''',
'input': f'文本:{input_text}'
}

instruction = test_texts['instruction']
input_value = test_texts['input']

messages =[
{'role':'system','content': f'{instruction}'},
{'role':'user','content': f'{input_value}'}
]

response = predict(messages, model, tokenizer)
print(response)

{'entity_text':'西安电子科技大学','entity_label':'组织'}
{'entity_text':'陈志明','entity_label':'人名'}
{'entity_text':'西北工业大学','entity_label':'组织'}
{'entity_text':'苏春红','entity_label':'人名'}
{'entity_text':'中国','entity_label':'地理实体'}
{'entity_text':'苏州','entity_label':'地理实体'}