fastT5
Questgen.ai
| fastT5 | Questgen.ai | |
|---|---|---|
| 5 | 3 | |
| 540 | 872 | |
| - | - | |
| 0.0 | 6.3 | |
| about 1 year ago | 5 months ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
fastT5
-
Speeding up T5
I've tried https://github.com/Ki6an/fastT5 but it works with CPU only.
-
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.
-
[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.
-
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
-
[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.
Questgen.ai
-
Yes/No style Question and Answer Generation
I have tried to do some searching for models but there don't seem to be ones that do what I am looking for. The closest I found was Questgen, but it only generated the questions and they, more often than, not did not make sense - especially for the types of questions I was looking to generate.
- [D] How to create a question answering system with a (potentially very large) corpus of text?
-
Creating a Wikipedia Question/Answer generator
This library might be of help https://github.com/ramsrigouthamg/Questgen.ai
What are some alternatives?
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
FARM - :house_with_garden: Fast & easy transfer learning for NLP. Harvesting language models for the industry. Focus on Question Answering.
json-translate - Translate json files with DeepL or AWS
haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
frame-semantic-transformer - Frame Semantic Parser based on T5 and FrameNet
simpletransformers - Transformers for Information Retrieval, Text Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI
OpenSeeFace - Robust realtime face and facial landmark tracking on CPU with Unity integration
kiri - Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.
FasterTransformer - Transformer related optimization, including BERT, GPT
kiri - Kiri is a visual tool designed for reviewing schematics and layouts of KiCad projects that are version-controlled with Git.
sparktorch - Train and run Pytorch models on Apache Spark.
MLH-Quizzet - This is a smart Quiz Generator that generates a dynamic quiz from any uploaded text/PDF document using NLP. This can be used for self-analysis, question paper generation, and evaluation, thus reducing human effort.