Hugging face 101 your first steps coding with transformers
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sure! hugging face is a popular open-source library that provides a wide range of pre-trained models for natural language processing (nlp) tasks, including text classification, translation, question answering, and more. the library is called `transformers`, and it is built on top of pytorch and tensorflow.
here are the basic steps to get started with hugging face transformers:
1. install the transformers library:
you can install the library using pip by running the following command:
2. load a pre-trained model:
you can load a pre-trained model using the `from_pretrained` method provided by hugging face. here's an example of loading a pre-trained distilbert model for text classification:
3. perform inference with the model:
you can use the loaded model to make predictions on text inputs. here's an example of classifying a text input using the loaded distilbert model:
4. fine-tune a pre-trained model (optional):
you can fine-tune a pre-trained model on your own dataset to improve its performance on specific tasks. hugging face provides tools and examples to support fine-tuning.
this is just a basic overview of getting started with hugging face transformers. the library offers a wide range of models and functionalities for nlp tasks, and you can explore further by referring to the official documentation and examples on the hugging face website: https://huggingface.co/transformers
i hope this tutorial helps you get started with coding using hugging face transformers! let me know if you have any questions or need further assistance.
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