text stringlengths 100 2.55M โ | label class label 11
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A Global Information Based Adaptive Threshold for Grouping
Large Scale Optimization Problems
An Chen1, Yipeng Zhang
Zhigang Ren
Autocontrol Institute, Xiโan Jiaotong University
Xiโan, Shaanxi, 710049, P.R. China
chenan123@stu.xjtu.edu.cn1
Autocontrol Institute, Xiโan Jiaotong University
Xiโan, Shaanxi, 710049, P.R. ... | 9cs.NE |
FROM DISSIPATIVITY THEORY TO COMPOSITIONAL CONSTRUCTION OF FINITE
MARKOV DECISION PROCESSES
arXiv:1712.07793v1 [cs.SY] 21 Dec 2017
ABOLFAZL LAVAEI1 , SADEGH SOUDJANI2 , AND MAJID ZAMANI1
Abstract. This paper is concerned with a compositional approach for constructing finite Markov decision
processes of interconnecte... | 3cs.SY |
"Probabilistic Integration: A Role in Statistical\nComputation?\nFrancฬงois-Xavier Briol1,2 , Chris.(...TRUNCATED) | 10math.ST |
"Cooperative control of multi-agent systems to locate\nsource of an odor\n\narXiv:1711.03819v1 [cs.S(...TRUNCATED) | 3cs.SY |
"Control design and analysis of a stochastic network control system\nMohammad Soltani1 , Abhyudai Si(...TRUNCATED) | 3cs.SY |
"A Parameterized Algorithm for Bounded-Degree\nVertex Deletion\n\narXiv:1601.00163v2 [cs.DS] 20 Aug (...TRUNCATED) | 8cs.DS |
"SPECIALIZATION AND INTEGRAL CLOSURE\n\narXiv:math/0611773v3 [math.AC] 6 Apr 2014\n\nJOOYOUN HONG AN(...TRUNCATED) | 0math.AC |
"Optimal Distance Labeling Schemes for Trees\nOfer Freedmanโ1 , Paweล Gawrychowskiโ1 , Patrick (...TRUNCATED) | 8cs.DS |
"ON MAXIMAL GREEN SEQUENCES FOR TYPE A QUIVERS\n\narXiv:1403.6149v3 [math.CO] 18 May 2016\n\nALEXAND(...TRUNCATED) | 0math.AC |
"International Journal of Computer Applications (0975 โ 8887)\nVolume 34โ No.6, November 2011\n\(...TRUNCATED) | 9cs.NE |
YAML Metadata Warning:The task_categories "conversational" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
๐ป Mesosfer Bear AI - Multi-Stage Foundation Corpus
This repository contains the curated, structured-shuffled (seed=42), and 95% Train / 5% Validation split corpus used to pretrain and align Mesosfer Bear AI (a 16-layer transformer LLM optimized for high-efficiency training on AMD Instinct MI300X).
๐ Dataset Structure & Stage Breakdown
Stage 1: Pre-training (Foundational Language & General Knowledge)
| Domain | Proposer / Source Repo | Weight | Target Format | Focus & Scope |
|---|---|---|---|---|
bahasa_umum_id |
wikimedia/wikipedia |
25% | Parquet / JSONL | Indonesian Wikipedia full encyclopedic raw articles |
bahasa_umum_en |
karpathy/climbmix-400b-shuffle |
25% | Parquet / JSONL | High-quality curated pre-training corpus by NVIDIA & Andrej Karpathy |
code_python |
codeparrot/codeparrot-clean-valid |
3% | Parquet / JSONL | Raw Python repository source files from GitHub |
code_typescript |
petrpan26/typescript-code |
3% | Parquet / JSONL | Raw TypeScript (.ts, .tsx) codebase source files |
code_javascript |
code_search_net |
3% | Parquet / JSONL | JavaScript (.js) source files and modules |
code_php |
code_search_net |
3% | Parquet / JSONL | PHP (.php) backend and web application code |
code_cpp |
AlgorithmicResearchGroup/arxiv_cplusplus_research_code |
3% | Parquet / JSONL | C++ (.cpp, .hpp) algorithms, scientific libraries, and system code |
code_c |
kye/all-torvalds-c-code-1 |
2% | Parquet / JSONL | C (.c, .h) systems programming and kernel algorithms |
code_csharp |
microsoft/LCC_csharp |
2% | Parquet / JSONL | C# (.cs) enterprise and application codebases |
matematika |
open-web-math/open-web-math |
20% | Parquet / JSONL | Mathematical documents, LaTeX proofs, equations, and reasoning |
terminal |
SaeedRahmani/codeparrot_github_code_powershell |
10% | Parquet / JSONL | PowerShell scripts, system administration commands, and CLI automation |
Stage 2: Continued Pre-Training (Domain Specialization)
| Domain | Proposer / Source Repo | Weight | Target Format | Focus & Scope |
|---|---|---|---|---|
bahasa_umum |
ccdv/arxiv-classification |
10% | Parquet / JSONL | High-density scientific and technical literature |
code_multilang |
code_search_net |
40% | Parquet / JSONL | Multi-language deep source code (Python, JS, PHP, Go, Java, Ruby) |
matematika |
open-web-math/open-web-math |
30% | Parquet / JSONL | Mathematical proofs, formal expressions, and symbolic logic |
terminal |
SaeedRahmani/codeparrot_github_code_powershell |
20% | Parquet / JSONL | Advanced systems automation, scripting, and shell operations |
Stage 3: Supervised Fine-Tuning (Instruction & Dialogue)
| Domain | Proposer / Source Repo | Weight | Target Format | Focus & Scope |
|---|---|---|---|---|
percakapan_id |
FreedomIntelligence/alpaca-gpt4-indonesian |
40% | Parquet / JSONL | Indonesian natural conversations and instruction responses |
percakapan_en |
HuggingFaceH4/ultrachat_200k |
40% | Parquet / JSONL | Multi-turn conversational dialogue and informative interactions |
instruksi |
garage-bAInd/Open-Platypus |
10% | Parquet / JSONL | STEM, logic, and chain-of-thought instruction-following tasks |
tooling_calls |
glaiveai/glaive-function-calling-v2 |
10% | Parquet / JSONL | Agent tool invocation, JSON arguments, and execution schemas |
Stage 4: Safety & Guardrails (Harm & Crime Refusal)
| Domain | Proposer / Source Repo | Weight | Target Format | Focus & Scope |
|---|---|---|---|---|
safety_pku |
PKU-Alignment/PKU-SafeRLHF |
50% | Parquet / JSONL | Refusal of illegal acts, cyberattacks, weapons, privacy violations, and harm |
safety_anthropic |
Anthropic/hh-rlhf |
30% | Parquet / JSONL | Red-teaming dialogues and harmless response alignment |
safety_jailbreak |
walledai/JailbreakHub |
20% | Parquet / JSONL | Adversarial prompts, DAN jailbreak defenses, and robust refusals |
๐ ๏ธ Data Preprocessing & Splitting Protocol
- Structured Deterministic Shuffling: Every individual shard is shuffled with
seed=42using zero-copy PyArrow array indexing to prevent gradient correlation and eliminate loss spikes. - 95% Train / 5% Validation Split: Every single domain shard is partitioned into
train/andval/directories. - Sequence Length Target: Built for
max_seq_len = 4096tokens with<|endoftext|>sequence packing.
๐ Quickstart: Loading Data in Python
from datasets import load_dataset
# Load Pretrain Bahasa Indonesia Train Split
dataset = load_dataset('Dummy9898/bear-dataset', data_dir='pretrain/bahasa_umum_id/train')
print(dataset)
๐ Licensing & Attribution
All constituent datasets belong to their respective original authors and maintainers. Distributed under Apache-2.0 in compliance with upstream open-source licenses.
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