datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
SlimPajama-Meta-rater-Reasoning-30B
Top 30B token SlimPajama Subset selected by the Reasoning rater
This repository contains the dataset described in the paper Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models.
Code: https://github.com/opendatalab/Meta-rater
Dataset Description
This dataset contains the top 30B tokens from the SlimPajama-627B corpus, selected using the Reasoning dimension of the PRRC (Professionalism, Readability, Reasoning, Cleanliness) framework. Each… See the full description on the dataset page: https://huggingface.co/datasets/opendatalab/SlimPajama-Meta-rater-Reasoning-30B.code-meta-reasoning-cleaned-final-string-idOCR-MetaReasoning
OCR-MetaReasoning Benchmark: Evaluating the Meta-Reasoning Ability of MLLMs in Text-Rich Image Understanding
Gengxu Li1, Yuan Wu1*, Yi Chang1,2,3
1 School of Artificial Intelligence, Jilin University 2 Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, MOE, China
3 International Center of Future Science, Jilin University
Overview ·
Task ·
Dataset ·
Evaluation ·
Citation
OCR-MetaReasoning is a controlled benchmark for testing… See the full description on the dataset page: https://huggingface.co/datasets/GengxuLi123/OCR-MetaReasoning.code-meta-reasoning-filteredmeta_chat_reasoning_25_75_100kmeta_chat_reasoning_75_25meta_chat_reasoning_25_75_systemmeta_chat_reasoning_50_50_system_100kmeta_chat_reasoning_75_25_100kmeta_chat_reasoning_50_50_systemmeta_chat_reasoning_25_75_system_100kmeta_chat_reasoning_50_50_100kmeta_chat_reasoning_25_75meta_chat_reasoning_75_25_system_100kmeta_chat_reasoning_25_75_system_eval_bba0
mlfoundations-dev/meta_chat_reasoning_25_75_system_eval_bba0
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
MMLUPro
JEEBench
GPQADiamond
LiveCodeBench
CodeElo
CodeForces
Accuracy
23.7
65.8
82.2
0.4
18.6
20.4
13.5
6.8
11.4
AIME24
Average Accuracy: 23.67% ± 1.73%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions
1
23.33%
7
30
2
26.67%
8
30
3
33.33%
10
30… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/meta_chat_reasoning_25_75_system_eval_bba0.meta_chat_reasoning_50_50meta_chat_reasoning_100_0meta_chat_reasoning_100_0_systemmath-meta-reasoning-filtered-longmath-meta-reasoning-filteredmeta_chat_reasoning_75_25_systemcode-meta-reasoning-filtered-longmeta_chat_reasoning_0_100_system_100kmeta_chat_reasoning_0_100t1-musr-prompt-enhancement-together_ai-meta-llama-meta-llama-3-1-8b
t1-musr-prompt-enhancement-together_ai-meta-llama-meta-llama-3-1-8b
MuSR Prompt Enhancement via Knowledge Synthesis + Compliance Tracking.
Performance
Eval Set
pass@1
Original (base)
0.7450
Original (enhanced prompt)
0.7300
Heldout (base prompt)
0.7050
Heldout (enhanced prompt)
0.7050
Strategies
Natural strategy: Means‑Motive‑Opportunity Heuristic
Enhanced strategy: Means-Motive-Opportunity Matrix
Synthesized Facts
Always list… See the full description on the dataset page: https://huggingface.co/datasets/reasoning-degeneration-dev/t1-musr-prompt-enhancement-together_ai-meta-llama-meta-llama-3-1-8b.meta_chat_reasoning_0_100_systemmeta_chat_reasoning_0_100_100kmath-meta-reasoning-cleanedREASONING_evalchemy_64shards_Meta-Llama-3-8B-Instructmeta_chat_reasoning_100_0_system_100k