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LysandreJik
2019-09-26 08:04:54 -04:00
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@@ -13,17 +13,20 @@ Features
- High performance on NLU and NLG tasks - High performance on NLU and NLG tasks
- Low barrier to entry for educators and practitioners - Low barrier to entry for educators and practitioners
State-of-the-art NLP for everyone State-of-the-art NLP for everyone:
- Deep learning researchers - Deep learning researchers
- Hands-on practitioners - Hands-on practitioners
- AI/ML/NLP teachers and educators - AI/ML/NLP teachers and educators
Lower compute costs, smaller carbon footprint Lower compute costs, smaller carbon footprint:
- Researchers can share trained models instead of always retraining - Researchers can share trained models instead of always retraining
- Practitioners can reduce compute time and production costs - Practitioners can reduce compute time and production costs
- 8 architectures with over 30 pretrained models, some in more than 100 languages - 8 architectures with over 30 pretrained models, some in more than 100 languages
Choose the right framework for every part of a model's lifetime Choose the right framework for every part of a model's lifetime:
- Train state-of-the-art models in 3 lines of code - Train state-of-the-art models in 3 lines of code
- Deep interoperability between TensorFlow 2.0 and PyTorch models - Deep interoperability between TensorFlow 2.0 and PyTorch models
- Move a single model between TF2.0/PyTorch frameworks at will - Move a single model between TF2.0/PyTorch frameworks at will