simpleT5
ModelZoo.pytorch
| simpleT5 | ModelZoo.pytorch | |
|---|---|---|
| 2 | 1 | |
| 381 | 48 | |
| - | - | |
| 2.5 | 0.0 | |
| 12 months ago | over 3 years ago | |
| Python | Python | |
| MIT License | MIT License |
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simpleT5
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Transformers: How to compare performance to base model?
Currently I just took ~42000 samples and trained a translation task directly on codeT5 with https://github.com/Shivanandroy/simpleT5. Validation loss and at least the qualitative results are not to bad. Im now going to try to compare it to the base codeT5-model with the *.loss function as suggested above.
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[P] SimpleT5 : Train T5 models in just 3 lines of code
🌟GitHub: https://github.com/Shivanandroy/simpleT5 🌟Medium: https://snrspeaks.medium.com/simplet5-train-t5-models-in-just-3-lines-of-code-by-shivanand-roy-2021-354df5ae46ba 🌟Colab Notebook: https://colab.research.google.com/drive/1JZ8v9L0w0Ai3WbibTeuvYlytn0uHMP6O?usp=sharing
ModelZoo.pytorch
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[D] Any good resources/repositories to get ImageNet trained models?
Google for 'model zoo' and the terms you want. Here's one that comes up with label smoothing: https://github.com/PistonY/ModelZoo.pytorch
What are some alternatives?
reformer-pytorch - Reformer, the efficient Transformer, in Pytorch
mmpretrain - OpenMMLab Pre-training Toolbox and Benchmark
datatap-python - Focus on Algorithm Design, Not on Data Wrangling
pytorch2keras - PyTorch to Keras model convertor
frame-semantic-transformer - Frame Semantic Parser based on T5 and FrameNet
models - Models and examples built with TensorFlow
KeyPhraseTransformer - KeyPhraseTransformer lets you quickly extract key phrases, topics, themes from your text data with T5 transformer | Keyphrase extraction | Keyword extraction
segmentation_models - Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
TencentPretrain - Tencent Pre-training framework in PyTorch & Pre-trained Model Zoo
MEAL-V2 - MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks. In NeurIPS 2020 workshop.
fastT5 - ⚡ boost inference speed of T5 models by 5x & reduce the model size by 3x.