Fine Tuning T5 for Summary Generation with PyTorch Lightning
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My Colab notebook on fine tuning T5 model for summarization task using Trenasformers + PyTorch Lightning
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Naacl2021
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Here are the summary of some of talks from NAACL 2021. So far, I only summarized the following papers, I will be summarizing more. So, I will either append it here or a new blog post. Feel free to check back soon.
ICLR 2021
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ICLR 2021
Edge Probing
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In the past couple of years, Transformers has acheived state of art results in a variety of natural language tasks. In order to better understand Transformers and what they are learning in practice, researchers have done layer-wise analysis of Transformer’s hidden states to understand what the Transformer is learning in each layer. A wave of recent work has started to “prob” the state of the art Tranformers to inspect the structure of the network to assess whether there exist localizable regions associated with distinct types of linguistic decisions, both syntactic and semantic information. Researchers examine the hidden states between encoder layers directly and use those hidden states in a linear layer + softmax to predict what kind of information in encoded in each hidden state.
Transformers 2
5 minute read
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This blog post is the continuation of my previous blog post, Transformers. In my previous blog post, I explained original Transformer paper, BERT, GPT, XLNet, RoBERTa, ALBERT, BART, and AMBER. In this blog post, I will explain MARGE, ConveRT, Generalization through Memorization, AdapterHub, and T5. Images and content used in this blogpost, otherwise mentioned, are all taken from the papers on each model.