elasticsearch-learning-to-rank
similarity
| elasticsearch-learning-to-rank | similarity | |
|---|---|---|
| 2 | 7 | |
| 1,462 | 996 | |
| 0.2% | 0.2% | |
| 5.4 | 6.5 | |
| 5 days ago | about 1 month ago | |
| Java | Python | |
| Apache License 2.0 | Apache License 2.0 |
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elasticsearch-learning-to-rank
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New free tool that uses fine-tuned BERT model to surface answers from research papers
I worked on a learning-to-rank problem at a previous job (which unfortunately never got deployed womp, womp). This was early days, so at the time I was looking at using LambdaMART with solr or elasticsearch for reranking with a Bayesian click model to get pseudo-labels for relevance.
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Add explain text for custom Elasticsearch plugin
I'm modifying learning to rank plugin to add some custom BM25 algo and I want to add explain like that, but I can't find documents/tutorial that clearly show how it is done.
similarity
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New free tool that uses fine-tuned BERT model to surface answers from research papers
Tensorflow Ranking and Tensorflow similarity (maybe relevant/irrelevant contrastive learning?) look like they could be useful.
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Non-Machine Learning Image Matching with a Vector DB
There is the metric learning problem to learn a hash for similarity https://github.com/tensorflow/similarity
That said, I don't see many good models available for download on tfhub or huggingface optimized for it, but you can always programmatically modify your images (if you truly mean identical to humans) - change white balance, crop, rotate, select adjacent frames from videos, etc. and optimize a network that is small enough for you to be satisfied and see if that works, as a possible alternative.
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Face Detection for 520 People
Metric learning has great implementations inside Tensorflow Similarity library: https://github.com/tensorflow/similarity Although the documentation is quite bad, but the jupyter notebooks are great.
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[P] TensorFlow Similarity 0.16 is out
Just a quick note that TensorFlow Similarity 0.16 is out -- this release beside adding the XMB loss is mostly focus on refactoring and optimizing the core components to ensure everything works smoothly and accurately. Details are in the changelog as usual and a simple pip install -U tensorflow_similarity should just work.
- Self-supervised learning added to TensorFlow Similarity
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[P] TensorFlow Similarity now self-supervised training
Very happy to announce that as part of the 0.15 release, TensorFlow Similarity now support self-supervised learning using STOA algorithms. To help you get started we included in the release a detailed getting started notebook that you can run in Colab. This notebook shows you how to use SimSiam self-supervised pre-training to almost double the accuracy compared to a model trained from scratch on CIFAR 10.
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TensorFlow Introduces ‘TensorFlow Similarity’, An Easy And Fast Python Package To Train Similarity Models Using TensorFlow
Github: https://github.com/tensorflow/similarity
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