Python 3.6 or higher installed on your machine<\/li>\n<\/ul>\nStep 1: Install LLMs<\/h2>\n
To get started, you’ll need to install an LLM library. There are several popular options available, such as Hugging Face’s Transformers library and SpaCy’s implementation of LLMs. For this tutorial, we’ll use SpaCy.<\/p>\n
You can install SpaCy by running the following command:<\/p>\n
pip install spacy\n<\/code><\/pre>\nAfter installing SpaCy, you’ll also need to download a language model. SpaCy provides a variety of pre-trained models for different languages. These models are trained on large corpora and can be used to perform various natural language processing tasks.<\/p>\n
For example, to download the English language model, you can run the following command:<\/p>\n
python -m spacy download en_core_web_sm\n<\/code><\/pre>\nStep 2: Load the Language Model<\/h2>\n
Once you have installed SpaCy and downloaded a language model, you can load the model into your Python script or interactive session. The following code snippet demonstrates how to load the English language model:<\/p>\n
import spacy\n\nnlp = spacy.load(\"en_core_web_sm\")\n<\/code><\/pre>\nStep 3: Text Extraction<\/h2>\n
Now that we have loaded the language model, we can use it to extract useful information from a given text. SpaCy’s language models provide a wide range of annotations, including named entities, part-of-speech tags, and syntactic dependencies.<\/p>\n
To extract these annotations, we need to process the text using the loaded model. Here’s an example of how to process a text string using SpaCy:<\/p>\n
text = \"Apple is looking at buying U.K. startup for $1 billion\"\n\ndoc = nlp(text)\n<\/code><\/pre>\nAfter processing the text, you can access the extracted annotations from the doc<\/code> object.<\/p>\nFor example, to extract the named entities from the text, you can iterate over the ents<\/code> attribute of the doc<\/code> object:<\/p>\nfor entity in doc.ents:\n print(entity.text, entity.label_)\n<\/code><\/pre>\nThis will print the named entities along with their corresponding entity types.<\/p>\n
Similarly, you can access other annotations such as part-of-speech tags and syntactic dependencies using the respective attributes of the Token<\/code> objects in the doc<\/code> object.<\/p>\nfor token in doc:\n print(token.text, token.pos_, token.dep_)\n<\/code><\/pre>\nStep 4: Text Annotation<\/h2>\n
LLMs can also be used to annotate texts with custom information. You can add your own annotations to the Token<\/code> objects of a Doc<\/code> object.<\/p>\nFor example, let’s say we want to annotate the sentiment of each sentence in a given text. We can define a custom attribute on the Token<\/code> objects called sentiment<\/code>, and assign a sentiment value to each token.<\/p>\nfrom spacy.tokens import Token\n\nToken.set_extension(\"sentiment\", default=None)\n\ntext = \"I love SpaCy. It's an amazing library.\"\n\ndoc = nlp(text)\n\nfor sentence in doc.sents:\n sentence_sentiment = 0\n\n for token in sentence:\n if token.text.lower() in [\"love\", \"amazing\"]:\n sentence_sentiment += 1\n elif token.text.lower() in [\"hate\", \"terrible\"]:\n sentence_sentiment -= 1\n\n for token in sentence:\n token._.sentiment = sentence_sentiment \/ len(sentence)\n<\/code><\/pre>\nIn this example, we iterate over each sentence in the text and calculate a sentiment value for each sentence. Then, we assign the sentiment value to each token within the sentence using the custom attribute sentiment<\/code>.<\/p>\nAfter annotating the text, you can access the custom annotations using the custom attribute, _.attribute_name<\/code>.<\/p>\nfor token in doc:\n print(token.text, token._.sentiment)\n<\/code><\/pre>\nThis will print the sentiment value for each token in the text.<\/p>\n
Conclusion<\/h2>\n
LLMs are powerful tools for text extraction and annotation. In this tutorial, we learned how to use LLMs to extract annotations from text using SpaCy, as well as how to add custom annotations to texts. With these techniques, you can leverage the power of LLMs to perform a wide range of natural language processing tasks.<\/p>\n","protected":false},"excerpt":{"rendered":"
How to Use Language Model Libraries (LLMs) for Text Extraction and Annotation Language Model Libraries (LLMs) are powerful tools for text extraction and annotation. They leverage pre-trained language models to perform a wide range of natural language processing tasks, such as named entity recognition, part-of-speech tagging, and dependency parsing. In Continue Reading<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_import_markdown_pro_load_document_selector":0,"_import_markdown_pro_submit_text_textarea":"","footnotes":""},"categories":[1],"tags":[1878,1879,740,325,355,504,245,41,40,761,1877],"yoast_head":"\nHow to use LLMs for text extraction and annotation - Pantherax Blogs<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"http:\/\/localhost:10003\/how-to-use-llms-for-text-extraction-and-annotation\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How to use LLMs for text extraction and annotation\" \/>\n<meta property=\"og:description\" content=\"How to Use Language Model Libraries (LLMs) for Text Extraction and Annotation Language Model Libraries (LLMs) are powerful tools for text extraction and annotation. They leverage pre-trained language models to perform a wide range of natural language processing tasks, such as named entity recognition, part-of-speech tagging, and dependency parsing. 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