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🤗 Transformers provides APIs and tools to easily download and train state-of-the-art pretrained models. Using pretrained models can reduce your compute costs, carbon footprint, and save you the time and resources required to train a model from scratch.
- Time Series Transformer
Parameters . past_values (torch.FloatTensor of shape...
- Bert
Parameters . vocab_size (int, optional, defaults to 30522) —...
- Tokenizer
Parameters . new_tokens (str, tokenizers.AddedToken or a...
- Train With a Script
The example script downloads and preprocesses a dataset from...
- Trainer
Trainer is a simple but feature-complete training and eval...
- Training on One GPU
PyTorch’s torch.nn.functional.scaled_dot_product_attention...
- Pipelines
torch_dtype (str or torch.dtype, optional) — Sent directly...
- Installation
Install 🤗 Transformers for whichever deep learning library...
- Time Series Transformer
Transformers. State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0. 🤗 Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural Language Generation (NLG ...
🤗 transformers is a library maintained by Hugging Face and the community, for state-of-the-art Machine Learning for Pytorch, TensorFlow and JAX. It provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.
Transformers - All Media Types, Transformers: Shattered Glass, The Transformers (IDW Generation One), Transformers Generation One, Transformers: Prime, Transformers: Beast Wars, Transformers Animated (2007), Transformers: Cyberverse. Request fills of various characters/reader that I posted to tumblr and thought I would collect here as well ...
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. - huggingface/transformers
The course teaches you about applying Transformers to various tasks in natural language processing and beyond. Along the way, you'll learn how to use the Hugging Face ecosystem — 🤗 Transformers , 🤗 Datasets , 🤗 Tokenizers , and 🤗 Accelerate — as well as the Hugging Face Hub .
9 oct. 2019 · Transformer architectures have facilitated building higher-capacity models and pretraining has made it possible to effectively utilize this capacity for a wide variety of tasks. \textit {Transformers} is an open-source library with the goal of opening up these advances to the wider machine learning community.
