In-person @ the NPD: Language modelling Hackathon 2023
The Data Analytics, Machine Learning and data centric workflows network group is inviting to a new edition of the previously successful Stavanger hackathons with public oil and gas industry data. The aim of the hackathon is to use the datasets to build interesting applications or explore the depth of the data using language modelling techniques and machine learning. Compute power will be provided.
| Date | Time | Duration | Register by | Location |
| 30 Nov- 1 Dec 2023 | 08:30 | 2 Days | 27 November at 09.00 | Valhall - Ptil |
There will be a social event at Bergeland bydelssenter Wednesday 29 November.
University members can apply for travel bursary. Please contact Peter Bormann for more information
Sponsors:
We have prepared a very large dataset of text from public oil and gas documents (Diskos, NLOG, NSTA) in a readily accessible format. In addition oil and gas specific named entity recognition (NER) on the data is available. The data can be used for many kids of natural language modelling tasks or semantic search implementations.
FORCE seminars have previously been fully booked with waiting lists so you are encouraged to sign up as soon as you know you will attend.
Participation fees:
FORCE members: NOK 1500,-
Non-members: NOK 3000,-
University/Students: NOK 500,-
Important information:
You can register as a FORCE member and pay "FORCE member" price if you are an employee of a member company.
All FORCE member companies are listed here.
Payment is made online by credit card or VIPPS. Please note that no refunds will be given after you have signed up.
If you for any reason cannot attend the workshop, you are welcome to send a representative, just inform Linn Smerud as soon as there are changes.
If you need to cancel your registration, please use the confirmation e-mail you received when you signed up.
If the seminar/webinar is cancelled, your payment will be refunded.
For other questions, please contact Linn Smerud or Tone Mydland.
Partners:
Norwegian Petroleum Directorate org.no: 870917732
Updated: 22/01/2024