simpleT5
frame-semantic-transformer
| simpleT5 | frame-semantic-transformer | |
|---|---|---|
| 2 | 1 | |
| 381 | 49 | |
| - | - | |
| 2.5 | 6.2 | |
| 12 months ago | 8 months 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
frame-semantic-transformer
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Frame Semantic Transformer: Open-source T5-based Semantic Frame Parser for FrameNet
Frame-Semantic-Transformer Github
What are some alternatives?
reformer-pytorch - Reformer, the efficient Transformer, in Pytorch
fastT5 - ⚡ boost inference speed of T5 models by 5x & reduce the model size by 3x.
datatap-python - Focus on Algorithm Design, Not on Data Wrangling
nlu - 1 line for thousands of State of The Art NLP models in hundreds of languages The fastest and most accurate way to solve text problems.
ModelZoo.pytorch - Hands on Imagenet training. Unofficial ModelZoo project on Pytorch. MobileNetV3 Top1 75.64🌟 GhostNet1.3x 75.78🌟
KeyPhraseTransformer - KeyPhraseTransformer lets you quickly extract key phrases, topics, themes from your text data with T5 transformer | Keyphrase extraction | Keyword extraction
zeroshot_topics - Topic Inference with Zeroshot models
TencentPretrain - Tencent Pre-training framework in PyTorch & Pre-trained Model Zoo
RATransformers - RATransformers 🐭- Make your transformer (like BERT, RoBERTa, GPT-2 and T5) Relation Aware!