Chonky Distilbert Base Uncased 1

🚀 分块式蒸馏伯特基础模型（无大小写区分）v1

分块式蒸馏伯特基础模型 是一个能够智能地将文本分割成有意义语义块的Transformer模型。该模型可用于检索增强生成（RAG）系统。

🚀 快速开始

本模型可用于将文本处理并分割成语义连贯的片段。这些片段随后可作为RAG流程的一部分，输入到基于嵌入的检索系统或语言模型中。

✨ 主要特性

• 智能地将文本分割成有意义的语义块。
• 可应用于RAG系统。

📦 安装指南

你可以使用作者开发的小型Python库 chonky 来使用此模型。

💻 使用示例

基础用法

from chonky import ParagraphSplitter

# 首次运行时会下载Transformer模型
splitter = ParagraphSplitter(device="cpu")

text = """Before college the two main things I worked on, outside of school, were writing and programming. I didn't write essays. I wrote what beginning writers were supposed to write then, and probably still are: short stories. My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep. The first programs I tried writing were on the IBM 1401 that our school district used for what was then called "data processing." This was in 9th grade, so I was 13 or 14. The school district's 1401 happened to be in the basement of our junior high school, and my friend Rich Draves and I got permission to use it. It was like a mini Bond villain's lair down there, with all these alien-looking machines — CPU, disk drives, printer, card reader — sitting up on a raised floor under bright fluorescent lights."""

for chunk in splitter(text):
  print(chunk)
  print("--")

高级用法

你也可以使用标准的命名实体识别（NER）流程来使用此模型：

from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_name = "mirth/chonky_distilbert_uncased_1"

tokenizer = AutoTokenizer.from_pretrained(model_name)

id2label = {
    0: "O",
    1: "separator",
}
label2id = {
    "O": 0,
    "separator": 1,
}

model = AutoModelForTokenClassification.from_pretrained(
    model_name,
    num_labels=2,
    id2label=id2label,
    label2id=label2id,
)

pipe = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

text = """Before college the two main things I worked on, outside of school, were writing and programming. I didn't write essays. I wrote what beginning writers were supposed to write then, and probably still are: short stories. My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep. The first programs I tried writing were on the IBM 1401 that our school district used for what was then called "data processing." This was in 9th grade, so I was 13 or 14. The school district's 1401 happened to be in the basement of our junior high school, and my friend Rich Draves and I got permission to use it. It was like a mini Bond villain's lair down there, with all these alien-looking machines — CPU, disk drives, printer, card reader — sitting up on a raised floor under bright fluorescent lights."""

pipe(text)

# 输出

[
  {'entity_group': 'separator', 'score': 0.89515704, 'word': 'deep.', 'start': 333, 'end': 338},
  {'entity_group': 'separator', 'score': 0.61160326, 'word': '.', 'start': 652, 'end': 653}
]

📚 详细文档

训练数据

该模型使用来自书籍语料库（bookcorpus）数据集的段落进行训练。

评估指标

硬件环境

该模型在2块1080ti显卡上进行了微调。

📄 许可证

本项目采用MIT许可证。

信息表格