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psimm

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The Grug Brained Data Scientist

simmering.dev
1 points·by psimm·قبل 3 سنوات·0 comments

Terraform Cloud Updates Plans

hashicorp.com
2 points·by psimm·قبل 3 سنوات·1 comments

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psimm
·قبل 3 سنوات·discuss
Demos look good! Could you please explain the advantages of Taipy over Streamlit and Shiny for Python?
psimm
·قبل 3 سنوات·discuss
I'm working on a Python package for this: https://github.com/qagentur/texttunnel

It's a wrapper for OpenAI's Python sample script plus adjacent functionality like cost estimation and binpacking multiple inputs into one request.
psimm
·قبل 4 سنوات·discuss
I feel the same. The closest to tidyverse in Python I've seen is siuba, a neat wrapper around pandas. Tidypolars is great too.

Lately, I've used DuckDB to write SQL that manipulates pandas data frames.
psimm
·قبل 4 سنوات·discuss
This article compares dplyr syntax with pandas, siuba, polars, ibis and duckdb: https://simmering.dev/blog/dataframes/

As other have said, escaping pandas is hard. Many visualization and data manipulation, validation and analysis libraries expect pandas input.

Siuba is really cool in that it offers a convenient syntax on top of pandas (and SQL databases) without requiring its own data format.
psimm
·قبل 5 سنوات·discuss
Very cool! I signed up and uploaded data for a text classifier. 3000 examples of social media posts on a binary annotation task. Got 91% initially, then looked through the annotations and corrected a few errors that had snuck in. The UI for that is great. That got it to 92%.

Easy to use UI, easy data upload and the training was quick. A great tool for testing new ideas for classifiers. For bigger projects I'd be concerned about long term cost with pay per invocation.

Is weak labeling via labeling functions (snorkel, skweak) something that's on the roadmap for Nyckel? Also, do you plan to add named entity recognition?
psimm
·قبل 5 سنوات·discuss
It's a cool challenge! I tried it at 90wpm and cleared most words. Some I had to do 2 or 3 times to do fast enough. Then I hit my nemesis: I can't type "necessary" fast enough for 90wpm. Tried it 20 times.
psimm
·قبل 5 سنوات·discuss
Agree, I didn't understand that one can change the WPM before reading this thread.
psimm
·قبل 5 سنوات·discuss
Q Insight Agency | Data Scientist | Remote or Mannheim, Germany

Q Insight Agency is a market research agency. We help our clients in consumer goods and pharma understand their customers. Our background is in qualitative research (interviews, focus groups, workshops) and now we are expanding into data science with a focus on social media. As a new team in an established company, the data science team enjoys stability and resources but also has the freedom to build.

We just launched Cosmention, an AI-powered social media monitoring tool specialized for cosmetics. It analyzes millions of social media posts from all platforms and detects mentions of brands, products, ingredients and other entities. Our stack: R, Python, Shiny, AWS, Snowflake, Docker.

Learn more at: https://teamq.de/blog/103/datascientist21
psimm
·قبل 5 سنوات·discuss
The UI looks great! As others have said, I like how compact it is. It doesn't get in the way as much as MS Teams and Discord do. I also like that it is lightweight. It's important to me that the app stays performant while sharing the screen. MS Teams is too laggy.

Have you looked at Tuple? Noor seems quite similar. Could you please explain a bit more about the differences between Noor and Tuple?

Finally, as others have said: lack of Windows compatibility is a dealbreaker for me for now. A performant Windows app would be fantastic. That's also something that Tuple doesn't have.
psimm
·قبل 5 سنوات·discuss
I use these team features in Prodigy: I start annotation sessions with different session_id and with the feed_overlap flag. I run Prodigy from an EC2 instance that annotators connect to.

The Prodigy team is working on a new version called Prodigy Scale with more team features. I'm looking forward to that release! For now it feels like a hack to use Prodigy in a team.

Inter-annotator agreement is key! You could consider making that highly visible in your tool. It's something that every team should measure and strive to maximize.

For developers who use spaCy in production (like me), I imagine it would be very hard for your tool to come out on top of Prodigy. But there could be an opportunity with price-sensitive hobby users or devs who use a different NLP library.
psimm
·قبل 5 سنوات·discuss
I'm in the market for a tool like this. At the moment I'm using Prodigy but interested in other options. Features that I'd be willing to pay for (or rather my employer):

  1 team functionality with multiple user accounts

  2 easy to use workflow for double annotation where each text is annotated by exactly two annotators. The software should make sure that a text is never shown to more than 2 annotators and never shown to the same annotator twice

  3 make it easy to review the 2 versions and solve conflicts

  4 smarter alternative to review would be a warning system that identifies annotations that may have errors (because a model trained on the other data predicts a different result) and automatically flags it for review by another annotator

  5 stats on the annotators: speed, accuracy, statistics on how frequently they assign different labels to detect potential misunderstandings of the annotation schema

  6 GUI with overview of all annotation datasets, with stats like % finished annotating (with stages for double annotation and review), the types of annotation done, frequencies of labels to detect imbalances

 7 functions to mass-edit the annotations, like renaming or removing an entity type
Another thing I'd be interested in is some integration with a third party annotation provider. There are companies that offer annotation as a service and it's also available on Google Cloud and AWS. Having that integrated into an annotation tool would make it very easy to get large amounts of well annotated training material.

But finally, and much more importantly: The workflow for annotators has to be perfected first, so they can work as efficiently and consistently as possible. Getting this right is more important to me than any of the other features I listed.