Squad En Bert Base

🚀 英文抽取式问答模型

本模型用于英文抽取式问答任务，基于预训练模型构建，能有效从文本中提取答案，为英文问答场景提供支持。

🚀 快速开始

你可以通过 🤗 Transformers 库的管道直接使用该模型：

>>> from transformers.pipelines import pipeline
>>> from transformers import AutoTokenizer, AutoModelForQuestionAnswering

>>> tokenizer = AutoTokenizer.from_pretrained("zhufy/squad-en-bert-base")
>>> model = AutoModelForQuestionAnswering.from_pretrained("zhufy/squad-en-bert-base")
>>> nlp = pipeline("question-answering", model=model, tokenizer=tokenizer)

>>> context = "A problem is regarded as inherently difficult if its \
              solution requires significant resources, whatever the \
              algorithm used. The theory formalizes this intuition, \
              by introducing mathematical models of computation to \
              study these problems and quantifying the amount of \
              resources needed to solve them, such as time and storage. \
              Other complexity measures are also used, such as the \
              amount of communication (used in communication complexity), \
              the number of gates in a circuit (used in circuit \
              complexity) and the number of processors (used in parallel \
              computing). One of the roles of computational complexity \
              theory is to determine the practical limits on what \
              computers can and cannot do."
              
>>> question = "What are two basic primary resources used to \
              guage complexity?"

>>> inputs = {"question": question, \
            "context":context }
            
>>> nlp(inputs)

{'score': 0.8589141368865967,
 'start': 305,
 'end': 321,
 'answer': 'time and storage'}

✨ 主要特性

• 该模型用于英文抽取式问答。
• 基于 bert-base-cased 模型，并且区分大小写，能区分 “english” 和 “English”。

📦 安装指南

文档未提及具体安装步骤，可参考 🤗 Transformers 库的安装说明。

💻 使用示例

基础用法

>>> from transformers.pipelines import pipeline
>>> from transformers import AutoTokenizer, AutoModelForQuestionAnswering

>>> tokenizer = AutoTokenizer.from_pretrained("zhufy/squad-en-bert-base")
>>> model = AutoModelForQuestionAnswering.from_pretrained("zhufy/squad-en-bert-base")
>>> nlp = pipeline("question-answering", model=model, tokenizer=tokenizer)

>>> context = "A problem is regarded as inherently difficult if its \
              solution requires significant resources, whatever the \
              algorithm used. The theory formalizes this intuition, \
              by introducing mathematical models of computation to \
              study these problems and quantifying the amount of \
              resources needed to solve them, such as time and storage. \
              Other complexity measures are also used, such as the \
              amount of communication (used in communication complexity), \
              the number of gates in a circuit (used in circuit \
              complexity) and the number of processors (used in parallel \
              computing). One of the roles of computational complexity \
              theory is to determine the practical limits on what \
              computers can and cannot do."
              
>>> question = "What are two basic primary resources used to \
              guage complexity?"

>>> inputs = {"question": question, \
            "context":context }
            
>>> nlp(inputs)

{'score': 0.8589141368865967,
 'start': 305,
 'end': 321,
 'answer': 'time and storage'}

高级用法

文档未提及高级用法相关代码示例。

📚 详细文档

模型信息