Hugging face introduction in python 11 2
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hugging face is a popular library for natural language processing (nlp) that provides easy access to state-of-the-art machine learning models. the library is built on top of pytorch and tensorflow and offers a simple interface for various tasks such as text classification, translation, summarization, and more.
installation
to get started with hugging face in python, you first need to install the `transformers` library. you can do this using pip:
basic concepts
1. **models**: hugging face provides a large collection of pre-trained models that can be used for various tasks.
2. **tokenizers**: tokenization is the process of converting text into a format that can be fed into a model. the `transformers` library provides tokenizers that are specifically designed for the models.
3. **datasets**: the library also provides tools to easily load and manage datasets.
example: text classification
in this tutorial, we will use a pre-trained model to perform text classification on a sample sentence. we'll use the `distilbert` model for this example.
#### step 1: import libraries
#### step 2: initialize the pipeline
the `pipeline` function allows you to quickly create a pipeline for a specific task. here, we will create a text classification pipeline.
#### step 3: use the pipeline
now we can use the classifier to predict the class of a given text.
explanation
1. **importing**: we import the `pipeline` function from the `transformers` library.
2. **creating a pipeline**: we create a text classification pipeline. the model is automatically downloaded if it’s not available locally.
3. **making predictions**: we pass a text string to the classifier, and it returns the predicted class along with a confidence score.
additional features
**using different models**: you can specify a different pre-trained model by adding the model name as an argument to the `pipeline` function.
**multi-label classification**: for multi-label classificati ...
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