davinci-002

MERA Created at 26.01.2024 08:32

0.383

The overall result

The submission does not contain all the required tasks

Ratings for leaderboard tasks

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Task name	Result	Metric
LCS	0.124	Accuracy
RCB	0.331 / 0.178	Accuracy F1 macro
USE	0.016	Grade norm
RWSD	0.481	Accuracy
PARus	0.506	Accuracy
ruTiE	0.519	Accuracy
MultiQ	0.119 / 0.044	F1 Exact match
CheGeKa	0.018 / 0	F1 Exact match
ruModAr	0.476	Exact match
ruMultiAr	0.176	Exact match
MathLogicQA	0.353	Accuracy
ruWorldTree	0.766 / 0.765	Accuracy F1 macro
ruOpenBookQA	0.675 / 0.676	Accuracy F1 macro

Evaluation on open tasks:

Go to the ratings by subcategory

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Task name

Result

Metric

BPS

0.521

Accuracy

ruMMLU

0.613

Accuracy

SimpleAr

0.927

Exact match

ruHumanEval

0.005 / 0.023 / 0.037

Pass@k

ruHHH

0.517

ruHateSpeech

0.551

ruDetox

0.349

ruEthics

	Correct	God	Ethical
Virtue	-0.033	-0.002	-0.006
Law	-0.041	-0.008	-0.041
Moral	-0.029	0.001	-0.024
Justice	-0.046	-0.011	0.012
Utilitarianism	-0.015	-0.028	-0.028

Information about the submission

Mera version

Torch Version

The version of the codebase

CUDA version

Precision of the model weights

Seed

Batch

Transformers version

The number of GPUs and their type

Architecture

Team:

MERA

Name of the ML model:

davinci-002

Link to the ML model:

https://platform.openai.com/docs/models/gpt-base

Additional links:

https://github.com/openai/openai-python

Architecture description:

GPT base model from OpenAI. Details are not disclosed.

Description of the training:

GPT base model from OpenAI. Details are not disclosed.

Pretrain data:

GPT base model from OpenAI. Details are not disclosed.

Training Details:

GPT base model from OpenAI. Details are not disclosed.

License:

Apache 2.0 license

Strategy, generation and parameters:

Code version v.1.1.0 All the parameters were not changed and are used as prepared by the organizers. Details: - OpenAI 1.10.0 - Tiktoken 0.5.2 - Context length 2049

Ratings by subcategory

Metric: Grade Norm

Model, team	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	8_0	8_1	8_2	8_3	8_4
davinci-002 MERA	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-

Model, team	Honest	Helpful	Harmless
davinci-002 MERA	0.525	0.559	0.466

Model, team	Anatomy	Virology	Astronomy	Marketing	Nutrition	Sociology	Management	Philosophy	Prehistory	Human aging	Econometrics	Formal logic	Global facts	Jurisprudence	Miscellaneous	Moral disputes	Business ethics	Biology (college)	Physics (college)	Human Sexuality	Moral scenarios	World religions	Abstract algebra	Medicine (college)	Machine learning	Medical genetics	Professional law	PR	Security studies	Chemistry (школьная)	Computer security	International law	Logical fallacies	Politics	Clinical knowledge	Conceptual_physics	Math (college)	Biology (high school)	Physics (high school)	Chemistry (high school)	Geography (high school)	Professional medicine	Electrical engineering	Elementary mathematics	Psychology (high school)	Statistics (high school)	History (high school)	Math (high school)	Professional accounting	Professional psychology	Computer science (college)	World history (high school)	Macroeconomics	Microeconomics	Computer science (high school)	European history	Government and politics
davinci-002 MERA	0.6	0.688	0.7	0.714	0.762	0.5	0.667	0.706	0.4	0.9	0.727	0.6	0.5	0.538	0.455	0.7	0.7	0.519	0.5	0.9	0.2	0.673	0.8	0.686	0.8	0.636	0.75	0.786	0.8	0.636	0.5	0.611	0.4	0.5	0.636	1	0.5	0.667	0.4	0.6	0.608	0.7	0.7	0.2	0.688	0.5	0.9	0.4	0.4	0.7	0.455	0.75	0.735	0.8	0.25	0.212	0.704

Model, team	SIM	FL	STA
davinci-002 MERA	0.676	0.665	0.705

Coorect

Good

Ethical

Model, team	Virtue	Law	Moral	Justice	Utilitarianism
davinci-002 MERA	-0.033	-0.041	-0.029	-0.046	-0.015

Model, team	Virtue	Law	Moral	Justice	Utilitarianism
davinci-002 MERA	-0.002	-0.008	0.001	-0.011	-0.028

Model, team	Virtue	Law	Moral	Justice	Utilitarianism
davinci-002 MERA	-0.006	-0.041	-0.024	0.012	-0.028

Model, team	Women	Men	LGBT	Nationalities	Migrants	Other
davinci-002 MERA	0.472	0.657	0.588	0.541	0.571	0.623

davinci-002

Ratings for leaderboard tasks

Evaluation on open tasks:

Information about the submission

Team:

Name of the ML model:

Link to the ML model:

Additional links:

Architecture description:

Description of the training:

Pretrain data:

Training Details:

License:

Strategy, generation and parameters:

Ratings by subcategory

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