{"id":36201,"date":"2024-11-01T09:46:35","date_gmt":"2024-11-01T09:46:35","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=36201"},"modified":"2024-11-01T09:46:35","modified_gmt":"2024-11-01T09:46:35","slug":"huggingface-transformers-tutorial-classification-accuracy","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/36201\/","title":{"rendered":"huggingface transformers tutorial, classification accuracy"},"content":{"rendered":"

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The advancement of deep learning and natural language processing (NLP) is one of the key elements driving today\u2019s technological innovation. While there are several libraries and frameworks available, the Hugging Face<\/strong> Transformers<\/strong> library is particularly designed to be intuitive and user-friendly. This article will discuss how to build a document classification model using Hugging Face’s Transformers and evaluate the model’s performance.<\/p>\n

1. What is Hugging Face Transformers?<\/h2>\n

The Hugging Face Transformers library supports various model architectures and makes it easy to use pre-trained models. Transformers are models that have revolutionized natural language processing, based on the Attention Mechanism<\/strong>. These models are pre-trained on large datasets and can be fine-tuned for specific tasks.<\/p>\n

2. Installing Required Libraries<\/h2>\n

We will install the necessary libraries to use Hugging Face Transformers. The primary libraries we will use are transformers<\/strong>, torch<\/strong>, and datasets<\/strong>. Use the following command to install them:<\/p>\n

!pip install transformers torch datasets<\/code><\/pre>\n
3. Preparing the Dataset<\/h2>\n
We will prepare the dataset for document classification. Here, we will use the AG News<\/strong> dataset. AG News is a dataset for news article classification, which has four classes:<\/p>\n