Faster TensorFlow models in Hugging Face Transformers

1. 1Improve computational performance of TensorFlow models like BERT and RoBERTa
2. 2Use TensorFlow Serving to deploy models and benefit from computational performance gains
3. 3Benchmark model performance using tools like GPU V100 and sequence length of 128

Machine learning engineers and data scientists can benefit from this improvement to deploy faster and more robust models, while developers can utilize TensorFlow Serving for efficient model deployment

💡 Hugging Face's improvements to TensorFlow models and integration with TensorFlow Serving enable faster and more robust model deployment

🚀 Faster #TensorFlow models in #HuggingFace Transformers! 🤖