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from transformers import BartTokenizer, BartForConditionalGeneration\n\n# Load the BART model and tokenizer\ntokenizer = BartTokenizer.from_pretrained('facebook\/bart-large-cnn')\nmodel = BartForConditionalGeneration.from_pretrained('facebook\/bart-large-cnn')\n\n# Text to summarize\ntext = \"\"\"\nDeep learning is a subset of machine learning that is based on artificial neural networks.\nIt is used for various applications such as image recognition, natural language processing,\nand more. Deep learning allows computers to learn from data in a hierarchical manner,\nenabling them to achieve high accuracy in various tasks.\n\"\"\"\n\n# Tokenize the text and input it to the summarization model\ninputs = tokenizer(text, return_tensors='pt', max_length=1024, truncation=True)\n\n# Generate the summary\nsummary_ids = model.generate(inputs['input_ids'], max_length=50, min_length=25, length_penalty=2.0, num_beams=4, early_stopping=True)\n\n# Decode the summarized text\nsummary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)\nprint(\"Summary:\", summary)\n<\/code><\/pre>\n<\/div>\nCode Explanation<\/h3>\n\nImport Libraries:<\/strong> Import the BART model and tokenizer from the `transformers` library.<\/li>\n
Loading the Model and Tokenizer:<\/strong> Retrieve the pre-trained model and tokenizer from `facebook\/bart-large-cnn`.<\/li>\n
Input Text:<\/strong> Set the long text to be summarized.<\/li>\n
Tokenizing:<\/strong> Tokenize the input text and convert it to a tensor.<\/li>\n
Generating Summary:<\/strong> Use the `generate` method to create the summary.<\/li>\n
Output Result:<\/strong> Decode and print the generated summary.<\/li>\n<\/ol>\nExplanation of Control Parameters<\/h2>\nThe above code allows for adjusting the quality of the summary through various control parameters. Each parameter plays the following role:<\/p>\n
\nmax_length:<\/strong> The maximum length of the generated summary.<\/li>\n
min_length:<\/strong> The minimum length of the generated summary.<\/li>\n
length_penalty:<\/strong> A penalty for the length of the summary, giving lower scores for longer outputs to adjust the length.<\/li>\n
num_beams:<\/strong> The number of beams to use in beam search, with higher values exploring more candidates.<\/li>\n
early_stopping:<\/strong> Determines whether to stop the process when the optimal summary is generated.<\/li>\n<\/ul>\nVarious Applications of BART<\/h2>\nBART can be used for various NLP tasks beyond summarization. Here are some key use cases of BART:<\/p>\n
1. Machine Translation<\/h3>\nBART is effectively used in translation tasks to convert input text into another language, allowing users to perform translations from the source language to the target language.<\/p>\n
2. Question Answering<\/h3>\nBART demonstrates strong performance in generating answers to given questions.<\/p>\n
3. Text Generation<\/h3>\nBART can also be used to generate high-quality text in free-form text generation tasks.<\/p>\n
Evaluating Model Performance<\/h2>\nVarious metrics can be used to evaluate the performance of the BART model. The ROUGE metric is commonly used. ROUGE measures the similarity between machine-generated summaries and human summaries, providing various metrics such as F1 score and recall.<\/p>\n
Calculating ROUGE Scores<\/h3>\nBelow is an example of how to calculate ROUGE scores using Python:<\/p>\n
\nfrom rouge import Rouge\n\n# Human summary and model summary\nhuman_summary = \"Deep learning is a subset of machine learning.\"\nmodel_summary = summary  # The model summary generated above\n\n# Create a ROUGE evaluator object\nrouge = Rouge()\n\n# Calculate ROUGE scores\nscores = rouge.get_scores(model_summary, human_summary)\nprint(\"ROUGE Scores:\", scores)\n<\/code><\/pre>\n<\/div>\nConclusion<\/h2>\nThe BART model is a highly useful tool applicable to various natural language processing tasks such as effective text summarization, translation, and question answering. It can be easily used through the Hugging Face Transformers library, and many researchers and developers are leveraging it to achieve innovative results in the field of NLP. Through this tutorial, I hope you gain an understanding of the basic concepts of BART and its usage, and acquire experience in performing text summarization.<\/p>\n
References<\/h2>\n\nHugging Face BART Documentation<\/a><\/li>\n
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Processing<\/a><\/li>\n<\/ul>\n<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"With the advancement of machine learning, particularly in natural language processing (NLP), transformer models have shown innovative results in word embedding, sentence generation, and various other tasks. Among them, BART (Bidirectional and Auto-Regressive Transformers) has garnered attention as a model that demonstrates excellent performance across multiple NLP tasks such as text generation, summarization, and translation. … \ub354 \ubcf4\uae30 “huggingface transformer training course, BART inference”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[108],"tags":[],"class_list":["post-36057","post","type-post","status-publish","format-standard","hentry","category---en"],"yoast_head":"\nhuggingface transformer training course, BART inference - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/atmokpo.com\/w\/36057\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"huggingface transformer training course, BART inference - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"With the advancement of machine learning, particularly in natural language processing (NLP), transformer models have shown innovative results in word embedding, sentence generation, and various other tasks. 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Example of Using the BART Model<\/h2>\n
This section will explain how to perform text summarization using the BART model. Below is a simple example code:<\/p>\n

Various Applications of BART<\/h2>\n
BART can be used for various NLP tasks beyond summarization. Here are some key use cases of BART:<\/p>\n

1. Machine Translation<\/h3>\n
BART is effectively used in translation tasks to convert input text into another language, allowing users to perform translations from the source language to the target language.<\/p>\n

2. Question Answering<\/h3>\n
BART demonstrates strong performance in generating answers to given questions.<\/p>\n

3. Text Generation<\/h3>\n
BART can also be used to generate high-quality text in free-form text generation tasks.<\/p>\n

Evaluating Model Performance<\/h2>\n
Various metrics can be used to evaluate the performance of the BART model. The ROUGE metric is commonly used. ROUGE measures the similarity between machine-generated summaries and human summaries, providing various metrics such as F1 score and recall.<\/p>\n

Example of Using the BART Model<\/h2>\nThis section will explain how to perform text summarization using the BART model. Below is a simple example code:<\/p>\n

Various Applications of BART<\/h2>\nBART can be used for various NLP tasks beyond summarization. Here are some key use cases of BART:<\/p>\n

1. Machine Translation<\/h3>\nBART is effectively used in translation tasks to convert input text into another language, allowing users to perform translations from the source language to the target language.<\/p>\n

2. Question Answering<\/h3>\nBART demonstrates strong performance in generating answers to given questions.<\/p>\n

3. Text Generation<\/h3>\nBART can also be used to generate high-quality text in free-form text generation tasks.<\/p>\n

Evaluating Model Performance<\/h2>\nVarious metrics can be used to evaluate the performance of the BART model. The ROUGE metric is commonly used. ROUGE measures the similarity between machine-generated summaries and human summaries, providing various metrics such as F1 score and recall.<\/p>\n

Example of Using the BART Model<\/h2>\n
This section will explain how to perform text summarization using the BART model. Below is a simple example code:<\/p>\n

Various Applications of BART<\/h2>\n
BART can be used for various NLP tasks beyond summarization. Here are some key use cases of BART:<\/p>\n

1. Machine Translation<\/h3>\n
BART is effectively used in translation tasks to convert input text into another language, allowing users to perform translations from the source language to the target language.<\/p>\n

2. Question Answering<\/h3>\n
BART demonstrates strong performance in generating answers to given questions.<\/p>\n

3. Text Generation<\/h3>\n
BART can also be used to generate high-quality text in free-form text generation tasks.<\/p>\n

Evaluating Model Performance<\/h2>\n
Various metrics can be used to evaluate the performance of the BART model. The ROUGE metric is commonly used. ROUGE measures the similarity between machine-generated summaries and human summaries, providing various metrics such as F1 score and recall.<\/p>\n