frame-semantic-transformer
fastT5
| frame-semantic-transformer | fastT5 | |
|---|---|---|
| 1 | 5 | |
| 49 | 540 | |
| - | - | |
| 6.2 | 0.0 | |
| 8 months ago | about 1 year ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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frame-semantic-transformer
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Frame Semantic Transformer: Open-source T5-based Semantic Frame Parser for FrameNet
Frame-Semantic-Transformer Github
fastT5
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Speeding up T5
I've tried https://github.com/Ki6an/fastT5 but it works with CPU only.
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Convert Pegasus model to ONNX
I am working on a project where I fine-tuned a Pegasus model on the Reddit dataset. Now, I need to convert the fine-tuned model to ONNX for the deployment stage. I have followed this guide from Huggingface to convert to the ONNX model for unsupported architects. I got it done but the ONNX model can't generate text. Turned out that Pegasus is an encoder-decoder model and most guides are for either encoder-model (e.g. BERT) or decoder-model (e.g. GPT2). I found the only example of converting an encoder-decoder model to ONNX from here https://github.com/Ki6an/fastT5.
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[P] What we learned by making T5-large 2X faster than Pytorch (and any autoregressive transformer)
Microsoft Onnx Runtime T5 export tool / FastT5: to support caching, it exports 2 times the decoder part, one with cache, and one without (for the first generated token). So the memory footprint is doubled, which makes the solution difficult to use for these large transformer models.
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Conceptually, what are the "Past key values" in the T5 Decoder?
Here is the fastT5 model code for reference code:https://github.com/Ki6an/fastT5/blob/master/fastT5/onnx_models.py
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[P] boost T5 models speed up to 5x & reduce the model size by 3x using fastT5.
for more information on the project refer to the repository here.
What are some alternatives?
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.
Questgen.ai - Question generation using state-of-the-art Natural Language Processing algorithms
simpleT5 - simpleT5 is built on top of PyTorch-lightning⚡️ and Transformers🤗 that lets you quickly train your T5 models.
mt5-M2M-comparison - Comparing M2M and mT5 on a rare language pairs, blog post: https://medium.com/@abdessalemboukil/comparing-facebooks-m2m-to-mt5-in-low-resources-translation-english-yoruba-ef56624d2b75
KeyPhraseTransformer - KeyPhraseTransformer lets you quickly extract key phrases, topics, themes from your text data with T5 transformer | Keyphrase extraction | Keyword extraction
json-translate - Translate json files with DeepL or AWS
zeroshot_topics - Topic Inference with Zeroshot models
OpenSeeFace - Robust realtime face and facial landmark tracking on CPU with Unity integration
RATransformers - RATransformers 🐭- Make your transformer (like BERT, RoBERTa, GPT-2 and T5) Relation Aware!
FasterTransformer - Transformer related optimization, including BERT, GPT
sparktorch - Train and run Pytorch models on Apache Spark.
transformer-deploy - Efficient, scalable and enterprise-grade CPU/GPU inference server for 🤗 Hugging Face transformer models 🚀