Timeline
Begin
2021/07/03
2021/07/03
Finish
2021/09/03
2021/09/03
Complete
2021/09/10
2021/09/10
Competition start: 2021/07/03 00:01:00
Competition closes on: 2021/09/03 23:59:00
Final Submission Limit: 2021/09/10 23:59:00
This competition has a total duration of 2 months, within which you will be able to make your submissions and obtain results automatically. Once the first part of the competition is over, you will have one week to choose your best model and submit it to be scored and considered for the cash prize.
Description
Welcome to our next exciting competition!
For this challenge, we have teamed up with Battelle Memorial Institute - one of the most respected names in the global scientific & research community - to launch a Data Science competition that can help to dramatically accelerate the pace of global innovation. The goal of this project is to break down several barriers that currently stand in the way of advanced research publications getting noticed, and receiving prompt recognition from the world's brightest minds. This competition will also offer cash prizes to the authors of the top two ML models, as determined by our platform's evaluation algorithm. Please read on for more details, and good luck!
About Battelle (battelle.org)
For this challenge, we have teamed up with Battelle Memorial Institute - one of the most respected names in the global scientific & research community - to launch a Data Science competition that can help to dramatically accelerate the pace of global innovation. The goal of this project is to break down several barriers that currently stand in the way of advanced research publications getting noticed, and receiving prompt recognition from the world's brightest minds. This competition will also offer cash prizes to the authors of the top two ML models, as determined by our platform's evaluation algorithm. Please read on for more details, and good luck!
About Battelle (battelle.org)
Battelle is solving the world’s most pressing challenges. We deliver when others can’t. We conduct research and development, manage laboratories, design and manufacture products, and deliver critical services for our clients – whether you are a multi-national corporation, a small start-up organization or a government agency. We are valued for our independence and ability to innovate.
We are part of a community working to encourage the discovery of new and interesting research in Artificial Intelligence and Machine Learning, especially in languages other than English. Much of the research being done in these fields is easily available on the web through sites like Arxiv.org, but many interesting discoveries are happening every day in different corners of the internet that may take time to identify and bring to the attention of the rest of the community.
This is especially true of research that is in a language other than English, which may easily missed by much of the community. We are passionate about finding the best current research, and identifying trends so that the cutting edge can continue to be pushed. In order to push in that direction, we devised a problem that attempts to measure when new ideas are being discussed, in any language. Based on a metric for recency of key words, how can we identify when a research paper is bringing forth new ideas so that we can better isolate them?
The Problem
The data is a collection of 42.912 abstracts from recent publications, along with the language and year of publication. The abstracts have author given keywords associated with them, and they have been given scores based on the average number of years that those keywords show up in our database. The goal of this competition is to build a model that is able to take in the abstract, the language, and the publication year, and predict the recency score. These models will be scored based on the accuracy of their predictions.
Evaluation
The evaluation of the model will be done using the RMSLE (Root Mean Squared Logarithmic Error). What we do is to calculate the Square Root to the MLSE metric that implements Scikit-learn.
If you want to know more about the MLSE metric that Scikit Learn calculates, you can find it here: https://scikit-learn.org/stable/modules/model_evaluation.html#mean-squared-log-error
Where:
RMSLE
If you want to know more about the MLSE metric that Scikit Learn calculates, you can find it here: https://scikit-learn.org/stable/modules/model_evaluation.html#mean-squared-log-error
Where:
RMSLE
N = Number of rows of the dataset Test.csv= true value
= predicted value
Rules
Competition Rules
- The code should not be shared privately. Any code that is shared, must be available to all participants of the competition through the platform
- The solution should use only publicly available open source libraries
- If two solutions get identical scores in the ranking table, the tie-breaker will be the date and time of the submission (the first solution submitted will win).
- We reserve the right to request any user's code at any time during a challenge. You will have 72 hours to submit your code following the code review rules.
- We reserve the right to update these rules at any time.
- Your solution must not infringe the rights of any third party and you must be legally authorized to assign ownership of all copyrights in and to the winning solution code to the competition host/sponsor.
- Competitors may register and submit solutions as individuals (not as teams, at least for now).
- Maximum 50 solutions submitted per day.
At the end of the competition you must submit the complete model in .ipynb (Jupyter Notebook) format - no other formats will be accepted. Normally, you'll have 1 week after the end of the competition to send it through our "Submit Final Model" button - This model will help us to get the real final evaluations, so the Private Leaderboard could change when the final private evaluation is shown.
Thanks for reaching out to us. We were inspecting the file named: "submission_df (5).csv" and we found a number with scientific notation in line 6.579 (we sent you an email with the evidence) inside your submitted file. Please be aware of this kind of notation, because it contains letters or dashes (1e-04), and that means that these are not numeric values, so the evaluation metric cannot compute a result.
If you have any other questions, please let us know
Regards!