文本完成数据集¶
文本完成数据集通常用于持续的预训练范式，其中包括 以自我监督的方式在非结构化、未标记的数据集上微调基础模型。
在 torchtune 中使用文本完成数据集进行微调的主要入口点。
文本完成数据集只需包含一列“text”，其中包含每个样本的文本。text_completion()
示例本地文本完成数据集¶
.json格式¶
# odyssey.json
[
{
"input": "After we were clear of the river Oceanus, and had got out into the open sea, we went on till we reached the Aeaean island where there is dawn and sunrise as in other places. We then drew our ship on to the sands and got out of her on to the shore, where we went to sleep and waited till day should break."
},
{
"input": "Then, when the child of morning, rosy-fingered Dawn, appeared, I sent some men to Circe's house to fetch the body of Elpenor. We cut firewood from a wood where the headland jutted out into the sea, and after we had wept over him and lamented him we performed his funeral rites. When his body and armour had been burned to ashes, we raised a cairn, set a stone over it, and at the top of the cairn we fixed the oar that he had been used to row with."
}
]
from torchtune.models.llama3 import llama3_tokenizer
from torchtune.datasets import text_completion_dataset
m_tokenizer = llama3_tokenizer(
path="/tmp/Meta-Llama-3.1-8B/original/tokenizer.model",
max_seq_len=8192
)
ds = text_completion_dataset(
tokenizer=m_tokenizer,
source="json",
column="input",
data_files="odyssey.json",
split="train",
)
tokenized_dict = ds[0]
print(m_tokenizer.decode(tokenized_dict["tokens"]))
# After we were clear of the river Oceanus, and had got out into the open sea,\
# we went on till we reached the Aeaean island where there is dawn and sunrise \
# as in other places. We then drew our ship on to the sands and got out of her on \
# to the shore, where we went to sleep and waited till day should break.
print(tokenized_dict["labels"])
# [128000, 6153, 584, 1051, 2867, 315, 279, 15140, 22302, 355, 11, 323, 1047, \
# 2751, 704, 1139, 279, 1825, 9581, 11, 584, 4024, 389, 12222, 584, 8813, 279, \
# 362, 12791, 5420, 13218, 1405, 1070, 374, 39493, 323, 64919, 439, 304, 1023, \
# 7634, 13, 1226, 1243, 24465, 1057, 8448, 389, 311, 279, 70163, 323, 2751, 704, \
# 315, 1077, 389, 311, 279, 31284, 11, 1405, 584, 4024, 311, 6212, 323, 30315, \
# 12222, 1938, 1288, 1464, 13, 128001]
这也可以通过 yaml 配置来完成：
# In config
tokenizer:
_component_: torchtune.models.llama3.llama3_tokenizer
path: /tmp/Meta-Llama-3.1-8B/original/tokenizer.model
max_seq_len: 8192
dataset:
_component_: torchtune.datasets.text_completion_dataset
source: json
data_files: odyssey.json
column: input
split: train
.txt格式¶
# odyssey.txt
After we were clear of the river Oceanus, and had got out into the open sea, we went on till we reached the Aeaean island where there is dawn and sunrise as in other places. We then drew our ship on to the sands and got out of her on to the shore, where we went to sleep and waited till day should break.
Then, when the child of morning, rosy-fingered Dawn, appeared, I sent some men to Circe's house to fetch the body of Elpenor. We cut firewood from a wood where the headland jutted out into the sea, and after we had wept over him and lamented him we performed his funeral rites. When his body and armour had been burned to ashes, we raised a cairn, set a stone over it, and at the top of the cairn we fixed the oar that he had been used to row with.
from torchtune.models.llama3 import llama3_tokenizer
from torchtune.datasets import text_completion_dataset
m_tokenizer = llama3_tokenizer(
path="/tmp/Meta-Llama-3.1-8B/original/tokenizer.model",
max_seq_len=8192
)
ds = text_completion_dataset(
tokenizer=m_tokenizer,
source="text",
data_files="odyssey.txt",
split="train",
)
# the outputs here are identical to above
同样，这也可以通过 yaml 配置来完成：
# In config
tokenizer:
_component_: torchtune.models.llama3.llama3_tokenizer
path: /tmp/Meta-Llama-3.1-8B/original/tokenizer.model
max_seq_len: 8192
dataset:
_component_: torchtune.datasets.text_completion_dataset
source: text
data_files: odyssey.txt
split: train
从 Hugging Face 加载文本完成数据集¶
要从 Hugging Face 加载文本完成数据集，您需要将数据集存储库名称传递给 。对于大多数 HF 数据集，您还需要指定 .sourcesplit
from torchtune.models.gemma import gemma_tokenizer
from torchtune.datasets import text_completion_dataset
g_tokenizer = gemma_tokenizer("/tmp/gemma-7b/tokenizer.model")
ds = text_completion_dataset(
tokenizer=g_tokenizer,
source="wikimedia/wikipedia",
split="train",
)
# Tokenizer is passed into the dataset in the recipe so we don't need it here
dataset:
_component_: torchtune.datasets.text_completion_dataset
source: wikimedia/wikipedia
split: train