登录查看更多内容

How do you apply BERT for question answering and knowledge extraction?

由人工智能和领英社区提供技术支持

BERT, or Bidirectional Encoder Representations from Transformers, is a powerful neural network model that can learn from large amounts of text and generate meaningful representations for natural language processing tasks. One of the most popular applications of BERT is question answering and knowledge extraction, where you can use BERT to find relevant information from a given document or corpus. In this article, you will learn how to apply BERT for question answering and knowledge extraction in four steps.

此文章中的业界达人

由社区从 2 条内容中精选。了解更多

Aldo Segnini

AI-Powered Digital Transformation Strategist | Empowering Executives with Data-Driven Insights | +25 Years of Proven…

1 Step 1: Prepare the data

The first step is to prepare the data that you want to use for question answering and knowledge extraction. You need to have a set of questions and answers, or queries and responses, that you want to train and test your BERT model on. You also need to have a collection of documents or passages that contain the information that you want to extract or answer. You can use any source of data that is relevant to your domain or task, such as Wikipedia articles, news articles, books, or reports. You can also use existing datasets, such as SQuAD, Natural Questions, or HotpotQA, that have been created for question answering and knowledge extraction tasks.

添加您的观点

Aldo Segnini

AI-Powered Digital Transformation Strategist | Empowering Executives with Data-Driven Insights | +25 Years of Proven Success in Implementing Tech Solutions | Startup Advisor | EaaS
举报内容
In Step 1, data preparation is crucial for question answering and knowledge extraction using BERT. You gather the necessary questions or queries and assemble a relevant document corpus. The quality and diversity of your data greatly influence the model's performance, setting the stage for subsequent stages.

已翻译

赞

2 Step 2: Fine-tune BERT

The second step is to fine-tune BERT on your data. Fine-tuning means adjusting the parameters of the pre-trained BERT model to fit your specific task and data. You can use any framework or library that supports BERT, such as HuggingFace Transformers, PyTorch, or TensorFlow. You need to define a task-specific architecture that adds a layer or head on top of the BERT encoder, such as a linear layer for classification, a span extraction layer for extracting answer spans, or a pointer network for generating answers. You also need to define a loss function, an optimizer, and a learning rate for training your model. You can use the default settings or experiment with different hyperparameters to optimize your model's performance.

添加您的观点

3 Step 3: Evaluate BERT

The third step is to evaluate BERT on your data. Evaluation means measuring how well your model performs on your task and data. You can use different metrics, such as accuracy, F1-score, recall, or precision, depending on your task and data. You can also use qualitative methods, such as inspecting the outputs or errors of your model, to understand its strengths and weaknesses. You can use a validation set or a test set to evaluate your model, or you can use cross-validation or other methods to reduce the variance of your results. You can also compare your model with other models or baselines to benchmark your performance.

添加您的观点

Nikita Parate

Applied Scientist | Amazon, Cisco | IISc Bangalore
举报内容
To evaluate BERT model on question-answering task you can use Exact match (EM) score, human equivalence score for questions (HEQ-Q), and F1-score. These metrics significantly enhance the evaluation of the model's effectiveness in question-answering tasks.

已翻译

赞

4 Step 4: Apply BERT

The fourth step is to apply BERT on your data. Application means using your model to answer questions or extract knowledge from new or unseen data. You can use your model as a standalone tool or integrate it with other systems or platforms, such as chatbots, search engines, or knowledge bases. You can also use your model to generate new questions or queries, or to enrich or augment your data with additional information or insights. You can also use your model to explore or analyze your data, such as finding patterns, trends, or anomalies, or to discover new knowledge or facts.

添加您的观点

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

添加您的观点

Artificial Intelligence

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

查看全部

How do you apply BERT for question answering and knowledge extraction?

1

2

3

4

5

1 Step 1: Prepare the data

2 Step 2: Fine-tune BERT

3 Step 3: Evaluate BERT

4 Step 4: Apply BERT

5 Here’s what else to consider

Artificial Intelligence

给文章评分

感谢您的反馈

更多Artificial Intelligence相关文章

更多相关阅读内容

How do you apply BERT for question answering and knowledge extraction?

1

2

3

4

5

1 Step 1: Prepare the data

2 Step 2: Fine-tune BERT

3 Step 3: Evaluate BERT

4 Step 4: Apply BERT

5 Here’s what else to consider

Artificial Intelligence

给文章评分

感谢您的反馈

查看其他技能