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Transformers实战——预训练模型_transformers gptq 如何导入数据集
作者：我家小花儿 | 2024-06-16 05:16:45
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transformers gptq 如何导入数据集
!pip install transformers datasets evaluate accelerate
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一、掩码语言模型
1.导入相关包
from datasets import load_dataset, Dataset
from transformers import AutoTokenizer, AutoModelForMaskedLM, DataCollatorForLanguageModeling, TrainingArguments, Trainer
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2.加载数据集
ds = Dataset.load_from_disk("./wiki_cn_filtered/")
# 同上
# ds = load_from_disk("./wiki_cn_filtered/")
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ds[0]
'''
{'source': 'wikipedia.zh2307',
'completion': "西安交通大学博物馆（Xi'an Jiaotong University Museum）是一座位于西安交通大学的博物馆，馆长是锺明善。\n历史\n2004年9月20日开始筹建，2013年4月8日正式建成开馆，位于西安交通大学兴庆校区陕西省西安市咸宁西路28号。建筑面积6,800平米，展厅面积4,500平米，馆藏文物4,900余件。包括历代艺术文物馆、碑石书法馆、西部农民画馆、邢良坤陶瓷艺术馆、陕西秦腔博物馆和书画展厅共五馆一厅。\n营业时间\n* 周一至周六：上午九点至十二点，下午一点至五点\n* 周日闭馆"}
'''
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3.数据集处理
- 注意：这里指定不指定
padding=True都可以，DataCollatorForLanguageModeling会padding
tokenizer = AutoTokenizer.from_pretrained("hfl/chinese-macbert-base")
def process_func(examples):
return tokenizer(examples["completion"], max_length=384, truncation=True)
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tokenized_ds = ds.map(process_func, batched=True, remove_columns=ds.column_names)
tokenized_ds
'''
Dataset({
features: ['input_ids', 'token_type_ids', 'attention_mask'],
num_rows: 10000
})
'''
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- mask 以及 label 部分
DataCollatorForLanguageModeling都给处理了
from torch.utils.data import DataLoader
# mlm：是否要做MLM任务，默认为True
# mlm_probability：指定mlm任务中的mask token的概率
dl = DataLoader(tokenized_ds, batch_size=2,
collate_fn=DataCollatorForLanguageModeling(tokenizer,
mlm=True,
mlm_probability=0.15))
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next(enumerate(dl))
'''
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-100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-100, -100, -100, -100, -100, 833, -100, -100, -100, 2347,
-100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-100, -100, 5102, -100, -100, -100, -100, -100, -100, -100,
-100, -100, -100, -100, 833, -100, -100, -100, -100, -100,
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3791, -100, -100, -100, 8020, -100, -100, -100, -100, -100,
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6158, -100, -100, -100, -100, -100, 1730, 8024, 4294, -100,
3221, -100, 754, -100, -100, -100, -100, -100, -100, -100,
-100, -100, 3136, -100, -100, -100, -100, -100, -100, -100,
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-100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-100, -100, -100, -100, -100, -100, 678, -100, 712, -100,
-100, -100, -100, -100, -100, -100, 1168, -100, -100, 4638,
-100, -100, -100, -100, 702, 712, -100, -100, -100, 3123,
-100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-100, -100, -100, 5326, -100, -100, -100, -100, -100, 1392,
-100, -100, 3136, -100, -100, -100, -100, -100, -100, -100,
-100, -100, 8166, 2399, -100, -100, -100, -100, -100, 518,
-100, -100, -100, -100]])})
'''
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tokenizer.mask_token, tokenizer.mask_token_id
'''
('[MASK]', 103)
'''
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4.创建模型
- 使用
AutoModelForMaskedLM
model = AutoModelForMaskedLM.from_pretrained("hfl/chinese-macbert-base")
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5.配置训练参数
args = TrainingArguments(
output_dir="./masked_lm",
per_device_train_batch_size=32,
logging_steps=10,
num_train_epochs=1
)
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6.创建训练器
trainer = Trainer(
args=args,
model=model,
train_dataset=tokenized_ds,
data_collator=DataCollatorForLanguageModeling(tokenizer,
mlm=True,
mlm_probability=0.15)
)
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7.模型训练
trainer.train()
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8.模型推理
from transformers import pipeline
pipe = pipeline("fill-mask", model=model, tokenizer=tokenizer, device=0)
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pipe("西安交通[MASK][MASK]博物馆（Xi'an Jiaotong University Museum）是一座位于西安交通大学的博物馆")
'''
[[{'score': 0.9948391318321228,
'token': 1920,
'token_str': '大',
'sequence': "[CLS] 西 安 交 通 大 [MASK] 博 物 馆 （ xi'an jiaotong university museum ） 是 一 座 位 于 西 安 交 通 大 学 的 博 物 馆 [SEP]"},
{'score': 0.0021944143809378147,
'token': 2110,
'token_str': '学',
'sequence': "[CLS] 西 安 交 通 学 [MASK] 博 物 馆 （ xi'an jiaotong university museum ） 是 一 座 位 于 西 安 交 通 大 学 的 博 物 馆 [SEP]"},
{'score': 0.0005357894115149975,
'token': 2339,
'token_str': '工',
'sequence': "[CLS] 西 安 交 通 工 [MASK] 博 物 馆 （ xi'an jiaotong university museum ） 是 一 座 位 于 西 安 交 通 大 学 的 博 物 馆 [SEP]"},
{'score': 0.0003660529328044504,
'token': 7770,
'token_str': '高',
'sequence': "[CLS] 西 安 交 通 高 [MASK] 博 物 馆 （ xi'an jiaotong university museum ） 是 一 座 位 于 西 安 交 通 大 学 的 博 物 馆 [SEP]"},
{'score': 0.0003039983275812119,
'token': 4906,
'token_str': '科',
'sequence': "[CLS] 西 安 交 通 科 [MASK] 博 物 馆 （ xi'an jiaotong university museum ） 是 一 座 位 于 西 安 交 通 大 学 的 博 物 馆 [SEP]"}],
[{'score': 0.9971689581871033,
'token': 2110,
'token_str': '学',
'sequence': "[CLS] 西 安 交 通 [MASK] 学 博 物 馆 （ xi'an jiaotong university museum ） 是 一 座 位 于 西 安 交 通 大 学 的 博 物 馆 [SEP]"},
{'score': 0.0011966773308813572,
'token': 7368,
'token_str': '院',
'sequence': "[CLS] 西 安 交 通 [MASK] 院 博 物 馆 （ xi'an jiaotong university museum ） 是 一 座 位 于 西 安 交 通 大 学 的 博 物 馆 [SEP]"},
{'score': 0.0008860519737936556,
'token': 1920,
'token_str': '大',
'sequence': "[CLS] 西 安 交 通 [MASK] 大 博 物 馆 （ xi'an jiaotong university museum ） 是 一 座 位 于 西 安 交 通 大 学 的 博 物 馆 [SEP]"},
{'score': 0.0001224309962708503,
'token': 3318,
'token_str': '术',
'sequence': "[CLS] 西 安 交 通 [MASK] 术 博 物 馆 （ xi'an jiaotong university museum ） 是 一 座 位 于 西 安 交 通 大 学 的 博 物 馆 [SEP]"},
{'score': 8.154550596373156e-05,
'token': 4289,
'token_str': '物',
'sequence': "[CLS] 西 安 交 通 [MASK] 物 博 物 馆 （ xi'an jiaotong university museum ） 是 一 座 位 于 西 安 交 通 大 学 的 博 物 馆 [SEP]"}]]
'''
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二、因果语言模型
1.导入相关包
from datasets import load_dataset, Dataset
from transformers import AutoTokenizer, AutoModelForCausalLM, DataCollatorForLanguageModeling, TrainingArguments, Trainer, BloomForCausalLM
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2.加载数据集
ds = Dataset.load_from_disk("./wiki_cn_filtered/")
ds
'''
Dataset({
features: ['source', 'completion'],
num_rows: 10000
})
'''
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ds[0]
'''
{'source': 'wikipedia.zh2307',
'completion': "西安交通大学博物馆（Xi'an Jiaotong University Museum）是一座位于西安交通大学的博物馆，馆长是锺明善。\n历史\n2004年9月20日开始筹建，2013年4月8日正式建成开馆，位于西安交通大学兴庆校区陕西省西安市咸宁西路28号。建筑面积6,800平米，展厅面积4,500平米，馆藏文物4,900余件。包括历代艺术文物馆、碑石书法馆、西部农民画馆、邢良坤陶瓷艺术馆、陕西秦腔博物馆和书画展厅共五馆一厅。\n营业时间\n* 周一至周六：上午九点至十二点，下午一点至五点\n* 周日闭馆"}
'''
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3.数据集处理
需要加 end_token（eos），告诉模型什么时候结束生成
不需要 padding，DataCollatorForLanguageModeling做因果语言模型时，自动左填充，即使增加了 padding=True还是会左填充
tokenizer = AutoTokenizer.from_pretrained("Langboat/bloom-389m-zh")
def process_func(examples):
contents = [e + tokenizer.eos_token for e in examples["completion"]]
return tokenizer(contents, max_length=384, truncation=True)
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tokenized_ds = ds.map(process_func, batched=True, remove_columns=ds.column_names)
tokenized_ds
'''
Dataset({
features: ['input_ids', 'attention_mask'],
num_rows: 10000
})
'''
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from torch.utils.data import DataLoader
dl = DataLoader(tokenized_ds, batch_size=2, collate_fn=DataCollatorForLanguageModeling(tokenizer, mlm=False))
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next(enumerate(dl))
'''
(0,
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904, 9783, 355, 22464, 9658, 355, 8417, 2300, 13561, 2054,
4983, 4829, 30800, 7262, 420, 11394, 8417, 35143, 11937, 15682,
8417, 2300, 7283, 10875, 355, 1016, 4179, 5039, 14027, 26215,
26835, 671, 15095, 189, 1165, 15095, 11900, 6184, 1125, 3244,
3687, 622, 8785, 1121, 891, 13765, 671, 10199, 189, 13,
210, 6940, 3728, 9552, 6082, 8417, 2300, 916, 4375, 714,
3679, 1806, 10567, 915, 189, 13, 210, 16131, 11835, 8417,
2300, 189, 13, 24075, 3728, 8417, 2300, 916, 4829, 3687,
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8417, 2300, 13224, 4829, 3687, 15964, 915, 189, 13, 27340,
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8417, 2300, 189, 13, 210, 23928, 168, 117, 245, 6279,
114, 240, 170, 100, 124, 168, 117, 228, 6279, 100,
124, 168, 117, 228, 171, 238, 224, 41356, 236, 24175,
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'attention_mask': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]), 'labels': tensor([[ -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
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124, 168, 117, 228, 171, 238, 224, 41356, 236, 24175,
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'''
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tokenizer.pad_token, tokenizer.pad_token_id
'''
('<pad>', 3)
'''
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tokenizer.eos_token, tokenizer.eos_token_id
'''
('</s>', 2)
'''
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4.创建模型
model = AutoModelForCausalLM.from_pretrained("Langboat/bloom-389m-zh")
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5.配置训练参数
args = TrainingArguments(
output_dir="./causal_lm",
per_device_train_batch_size=4, # 相当于per_device_train_batch_size=32，只不过慢
gradient_accumulation_steps=8,
logging_steps=10,
num_train_epochs=1
)
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6.创建 Trainer
trainer = Trainer(
args=args,
model=model,
train_dataset=tokenized_ds,
data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False)
)
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7.模型训练
trainer.train()
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8.模型推理
from transformers import pipeline
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0)
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pipe("西安交通大学博物馆（Xi'an Jiaotong University Museum）是一座位于西安", max_length=128, do_sample=True)
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