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MyAwesomeModel-best

ASDCZX12DS/MyAwesomeModel-best

MyAwesomeModel-best is a feature extraction model from ASDCZX12DS. Use it when you need embeddings to search or compare text. It is set up for transformers. The card lists the license as mit.

Selected checkpoint: step1000 — the checkpoint with the highest weighted evaluation score across all 15 benchmarks (overall score = 0.710).

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At a glance

Task: Feature Extraction
Library: transformers
License: mit
Model type: bert
Access: Public
Created: Sep 10, 2026
Updated: Sep 10, 2026
SHA: 28f56066

Model card

From the Hugging Face model README

MyAwesomeModel (best checkpoint)

Selected checkpoint: step_1000 — the checkpoint with the highest weighted evaluation score across all 15 benchmarks (overall score = 0.710).

Evaluation Results

Scores below are reported to 3 decimal places.

Per-benchmark scores for the selected checkpoint (`step_1000`)

Benchmark	Score
math_reasoning	0.550
code_generation	0.650
text_classification	0.828
sentiment_analysis	0.792
question_answering	0.607
logical_reasoning	0.819
common_sense	0.736
reading_comprehension	0.700
dialogue_generation	0.644
summarization	0.767
translation	0.804
knowledge_retrieval	0.676
creative_writing	0.610
instruction_following	0.758
safety_evaluation	0.739
Overall (weighted)	0.710

All checkpoints — full benchmark matrix

Checkpoint	math_reasoning	code_generation	text_classification	sentiment_analysis	question_answering	logical_reasoning	common_sense	reading_comprehension	dialogue_generation	summarization	translation	knowledge_retrieval	creative_writing	instruction_following	safety_evaluation	Overall
step_100	0.345	0.350	0.517	0.617	0.475	0.319	0.530	0.475	0.438	0.517	0.640	0.529	0.328	0.550	0.628	0.480
step_200	0.383	0.421	0.603	0.675	0.510	0.375	0.583	0.529	0.493	0.600	0.700	0.570	0.388	0.610	0.650	0.535
step_300	0.415	0.475	0.667	0.710	0.533	0.445	0.621	0.569	0.530	0.650	0.733	0.596	0.436	0.650	0.668	0.576
step_400	0.443	0.517	0.714	0.733	0.550	0.525	0.650	0.600	0.557	0.683	0.755	0.615	0.475	0.679	0.683	0.608
step_500	0.467	0.550	0.750	0.750	0.564	0.605	0.672	0.625	0.579	0.707	0.769	0.631	0.507	0.700	0.696	0.635
step_600	0.487	0.577	0.776	0.762	0.575	0.675	0.690	0.645	0.596	0.725	0.780	0.643	0.534	0.717	0.707	0.656
step_700	0.506	0.600	0.795	0.772	0.584	0.731	0.705	0.663	0.611	0.739	0.788	0.653	0.557	0.730	0.717	0.674
step_800	0.522	0.619	0.809	0.780	0.593	0.773	0.717	0.677	0.624	0.750	0.795	0.662	0.577	0.741	0.725	0.689
step_900	0.537	0.636	0.820	0.786	0.600	0.801	0.727	0.689	0.634	0.759	0.800	0.670	0.595	0.750	0.732	0.700
step_1000	0.550	0.650	0.828	0.792	0.607	0.819	0.736	0.700	0.644	0.767	0.804	0.676	0.610	0.758	0.739	0.710

How the scores are computed

Each benchmark score is a deterministic function of the training step number (evaluated via the workspace evaluation/ suite), and the overall score is their weighted average (weights defined in evaluation/eval.py). All 15 benchmark functions are monotonically increasing with training step, so the latest checkpoint step_1000 achieves the highest overall score.

Task: Feature Extraction
Library: transformers
Type: bert
License: mit
Created: Sep 10, 2026
Updated: Sep 10, 2026