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Examples. We host a wide range of example scripts for multiple learning frameworks. Simply choose your favorite: TensorFlow, PyTorch or JAX/Flax. We also have some research projects, as well as some legacy examples.
- Transformers
🤗 Transformers If you are looking for custom support from...
- Transformers
🤗 Transformers If you are looking for custom support from the Hugging Face team Contents Supported models and frameworks. We’re on a journey to advance and democratize artificial intelligence through open source and open science.
- Introduction
- Recurrent Network — The Shining Era Before Transformers
- What Are Transformers in NLP?
- Transfer Learning in NLP
- An Introduction to Hugging Face Transformers
- Hugging Face Tutorial : Edition in Progress …
- Conclusion
The extensive contribution of researchers in NLP, short for Natural Language Processing, during the last decades has been generating innovative results in different domains. Below are some of the examples of Natural Language Processing in practice: 1. Apple’s Siri personal assistant, which can help users in their day-to-day activities such as setti...
Before diving into the core concept of transformers, let’s briefly understand what recurrent models are and their limitations. Recurrent networks employ the encoder-decoder architecture, and we mainly use them when dealing with tasks where both the input and outputs are sequences in some defined ordering. Some of the greatest applications of recurr...
Transformers is the new simple yet powerful neural network architecture introduced by Google Brain in 2017 with their famous research paper “Attention is all you need.” It is based on the attention mechanism instead of the sequential computation as we might observe in recurrent networks.
Training deep neural networks such as transformers from scratch is not an easy task, and might present the following challenges: 1. Finding the required amount of data for the target problem can be time-consuming 2. Getting the necessary computation resources like GPUs to train such deep networks can be very costly. Using transfer learning can have...
Hugging Face is an AI community and Machine Learning platform created in 2016 by Julien Chaumond, Clément Delangue, and Thomas Wolf. It aims to democratize NLP by providing Data Scientists, AI practitioners, and Engineers immediate access to over 20,000 pre-trained models based on the state-of-the-art transformer architecture. These models can be a...
Now that you have a better understanding of Transformers, and the Hugging Face platform, we will walk you through the following real-world scenarios: language translation, sequence classification with zero-shot classification, sentiment analysis, and question answering.
In this article, we’ve covered the evolution of natural language technology from recurrent networks to transformers and how Hugging Face has democratized the use of NLP through its platform. If you are still hesitant about using transformers, we believe it is time to give them a try and add value to your business cases.
State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow. 🤗 Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. These models can be applied on: 📝 Text, for tasks like text classification, information extraction, question answering, summarization ...
Jul 24, 2024 · Throughout this tutorial, you’ll gain a conceptual understanding of Hugging Face’s AI offerings and learn how to work with the Transformers library through hands-on examples. When you finish, you’ll have the knowledge and tools you need to start using models for your own use cases.
Jul 4, 2022 · Introduction. Automatic summarization is one of the central problems in Natural Language Processing (NLP). It poses several challenges relating to language understanding (e.g. identifying important content) and generation (e.g. aggregating and rewording the identified content into a summary).
Jan 31, 2024 · Then you'll see a practical example of how to use it. What is the Hugging Face Transformer Library? The Hugging Face Transformer Library is an open-source library that provides a vast array of pre-trained models primarily focused on NLP. It’s built on PyTorch and TensorFlow, making it incredibly versatile and powerful.
