Squad En Bert Base

🚀 English Extractive Question Answering Model

This model is designed for English extractive question answering, leveraging the power of the BERT architecture to provide accurate answers.

🚀 Quick Start

You can use this model directly from the 🤗 Transformers library with a pipeline. Here's how:

>>> 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'}

✨ Features

• English Extractive Question Answering: Specifically designed to handle English extractive question answering tasks.
• Case-Sensitive: Based on the bert-base-cased model, it differentiates between lowercase and uppercase words.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

The above code example demonstrates the basic usage of the model for question answering. You can provide a context and a question, and the model will return the answer along with its score and position in the context.

📚 Documentation

Model Description

This model is for English extractive question answering. It is based on the bert-base-cased model, and it is case-sensitive: it makes a difference between english and English.

Training data

English SQuAD v2.0

🔧 Technical Details

The model uses the BERT architecture, which is a pre-trained language model. It has been fine-tuned on the English SQuAD v2.0 dataset for extractive question answering tasks. The case-sensitivity feature allows it to better understand the context and provide more accurate answers.

📄 License

No license information is provided in the original document.