1/🛠Tooling Tuesday🛠
Let's talk about an open source absolute unit: 🤗
@huggingface provides open-source implementations of most Transformer architectures, in both PyTorch and TF.
Better yet, they have many pre-trained NLP models and datasets.
And recently, an inference API.
Let's talk about an open source absolute unit: 🤗
@huggingface provides open-source implementations of most Transformer architectures, in both PyTorch and TF.
Better yet, they have many pre-trained NLP models and datasets.
And recently, an inference API.
2/ The 🤗 transformers library implements many architectures: BERT, GPT, XLNet, etc.
It works for both PyTorch and Tensorflow, and provides three main modules, all nicely interoperable:
- Data configuration and tokenization
- Model architecture
- Training
It works for both PyTorch and Tensorflow, and provides three main modules, all nicely interoperable:
- Data configuration and tokenization
- Model architecture
- Training
3/ All of the transformers library code is compatible with pre-trained model weights available on Hugging Face Hub.
This enables rapid progress: no matter what your NLP task/language is, there is probably a pre-trained model for you to start fine-tuning with.
This enables rapid progress: no matter what your NLP task/language is, there is probably a pre-trained model for you to start fine-tuning with.
4/ Did we mention that all of the above is absolutely free?
It is!
There is also a premium feature: inference API.
With a paid plan, you can upload your fine-tuned models privately, and run inference using them using a REST API, and Hugging Face will scale for you.
It is!
There is also a premium feature: inference API.
With a paid plan, you can upload your fine-tuned models privately, and run inference using them using a REST API, and Hugging Face will scale for you.
5/ Hugging Face has powered an incredible amount of progress in NLP research and production use over the last couple of years. Give them a follow at @huggingface to stay up to date -- they move fast!
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