An Open Source Machine Learning Framework for Everyone
In this work, we take the first step to exploit the pre-trained (unfine-tuned) weights to mitigate backdoors in fine-tuned language models.
We first cast constituent and dependency parsing as sequence tagging.
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Models and examples built with TensorFlow
Tensors and Dynamic neural networks in Python with strong GPU acceleration
The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model.
Ranked #2 on Domain Generalization on VizWiz-Classification (using extra training data)
The ResNet and its variants have achieved remarkable successes in various computer vision tasks.
Ranked #3 on Medical Image Classification on NCT-CRC-HE-100K
We also turn the feed-forward layer in DNN model into a mixture of addictive and multiplicative feature interactions by proposing MaskBlock in this paper.
Ranked #2 on Click-Through Rate Prediction on Criteo
We worked to increase classification accuracy and mitigate algorithmic biases on our baseline model trained on the augmented benchmark database.