Data Science by ODS.ai 🦜

2023-04-04 22:38:49 Open / Comment

2023-04-04 20:42:06 Kandinsky 2.1
by Sber & AIRI

The main features:

- 3.3B parameters
- generation resolution - 768x768
- image prior transformer
- new MoVQ image autoencoder
- doing a cleaner set of 172M text-image pairs
- work modes: generate by text, blend image, generate images by pattern, change images by text, inpainting/outpainting

The FID on the COCO_30k dataset reaches 8.21

Few posts where compare Kandinsky 2.1 with another similar models

- https://t.me/dushapitona/643
- https://t.me/antidigital/6153


Habr: https://habr.com/ru/companies/sberbank/articles/725282/
Telegram-bot: https://t.me/kandinsky21_bot
ruDALL-E: https://rudalle.ru/
MLSpace: https://sbercloud.ru/ru/datahub/rugpt3family/kandinsky-2-1
GH: https://github.com/ai-forever/Kandinsky-2
HF model: https://huggingface.co/ai-forever/Kandinsky_2.1
HF space: https://huggingface.co/spaces/ai-forever/Kandinsky2.1
FusionBrain: https://fusionbrain.ai/diffusion Open / Comment

2023-04-04 15:50:37 Pandas v2.0.0

The main enhancements:

- installing optional dependencies with pip extras
- index can now hold numpy numeric dtypes
- argument dtype_backend, to return pyarrow-backed or numpy-backed nullable dtypes
- copy-on-write improvements
- ..
+ other notable bug fixes

Full list of changes: https://pandas.pydata.org/docs/whatsnew/v2.0.0.html Open / Comment

2023-04-04 00:53:49 Stanford 2023 AI Index Report is published!

The section on machine translation is based on Intento data as usual :)

https://aiindex.stanford.edu/report/ Open / Comment

2023-04-03 17:18:15 ​​BloombergGPT: A Large Language Model for Finance

The realm of financial technology involves a wide range of NLP applications, such as sentiment analysis, named entity recognition, and question answering. Although Large Language Models (LLMs) have demonstrated effectiveness in various tasks, no LLM specialized for the financial domain has been reported so far. This work introduces BloombergGPT, a 50-billion-parameter language model trained on an extensive range of financial data. The researchers have created a massive 363-billion-token dataset using Bloomberg's data sources, supplemented with 345 billion tokens from general-purpose datasets, potentially creating the largest domain-specific dataset to date.

BloombergGPT has been validated on standard LLM benchmarks, open financial benchmarks, and a suite of internal benchmarks that accurately reflect its intended usage. The mixed dataset training results in a model that significantly outperforms existing models on financial tasks without sacrificing performance on general LLM benchmarks. The paper also discusses modeling choices, training processes, and evaluation methodology. As a next step, the researchers plan to release training logs (Chronicles) detailing their experience in training BloombergGPT.

Paper: https://arxiv.org/abs/2303.17564

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-bloomberggpt

#deeplearning #nlp #transformer #sota #languagemodel #finance Open / Comment

2023-04-03 16:01:22 Reliable ML track at Data Fest Online 2023
Call for Papers

Friends, we are glad to inform you that the largest Russian-language conference on Data Science - Data Fest - from the Open Data Science community will take place in 2023 (at the end of May).

And it will again have a section from Reliable ML community. We are waiting for your applications for reports: write directly to me or Dmitry.

Track Info

The concept of Reliable ML is about what to do so that the result of the work of data teams would be, firstly, applicable in the business processes of the customer company and, secondly, brought benefits to this company.

For this you need to be able to:

- correctly build a portfolio of projects (#business)
- think over the system design of each project (#ml_system_design)
- overcome various difficulties when developing a prototype (#tech #causal_inference #metrics)
- explain to the business that your MVP deserves a pilot (#interpretable_ml)
- conduct a pilot (#causal_inference #ab_testing)
- implement your solution in business processes (#tech #mlops #business)
- set up solution monitoring in the productive environment (#tech #mlops)

If you have something to say on the topics above, write to us! If in doubt, write anyway. Many of the coolest reports of previous Reliable ML tracks have come about as a result of discussion and collaboration on the topic.

If you are not ready to make a report but want to listen to something interesting, you can still help! Repost to a relevant community / forward to a friend = participate in the creation of good content.

Registration and full information about Data Fest 2023 is here.

@Reliable ML Open / Comment

2023-04-02 19:28:09 When you stack enough layers, them can explain the meme about stacking more layers.

#memelearning Open / Comment

2023-04-02 11:13:01 ​​Interactive and explorable explanations Collection of links to different explanations of how things work. Link: https://explorabl.es How network effect (ideas, diseases) works: https://meltingasphalt.com/interactive/going-critical/ How trust works: h… Open / Comment

2023-04-01 19:18:03 CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Evaluations on HumanEval-X

CodeGeeX is a multilingual model with 13 billion parameters for code generation. It is pre-trained on 850 billion tokens of 23 programming languages.

- Multilingual Code Generation: CodeGeeX has good performance for generating executable programs in several mainstream programming languages, including Python, C++, Java, JavaScript, Go, etc.
- Crosslingual Code Translation: CodeGeeX supports the translation of code snippets between different languages.
- Customizable Programming Assistant: CodeGeeX is available in the VS Code extension marketplace for free. It supports code completion, explanation, summarization and more, which empower users with a better coding experience.
- Open-Source and Cross-Platform: All codes and model weights are publicly available for research purposes. CodeGeeX supports both Ascend and NVIDIA platforms. It supports inference in a single Ascend 910, NVIDIA V100 or A100.

GitHub Open / Comment

2023-03-31 22:38:05 Twitter Recommendation Algorithm

#Twitter disclosed the sources of its recommendation engine.

GitHub: https://github.com/twitter/the-algorithm
Blog post: https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm

#recommenders #recsys #recommendation Open / Comment