datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
EpistemeAI__Reasoning-Llama-3.1-CoT-RE1-NMT-details
Dataset Card for Evaluation run of EpistemeAI/Reasoning-Llama-3.1-CoT-RE1-NMT
Dataset automatically created during the evaluation run of model EpistemeAI/Reasoning-Llama-3.1-CoT-RE1-NMT
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/EpistemeAI__Reasoning-Llama-3.1-CoT-RE1-NMT-details.cleand_moremilk_CoT_Reasoning_Quantom_Physics_And_Computing元データ: https://huggingface.co/datasets/moremilk/CoT_Reasoning_Quantom_Physics_And_Computing
使用したコード: https://github.com/LLMTeamAkiyama/0-data_prepare/tree/master/src/CoT_Reasoning_Quantom_Physics_And_Computing
データ件数: 2,862
平均トークン数: 1,110
最大トークン数: 2,334
合計トークン数: 3,175,666
ファイル形式: JSONL
ファイル分割数: 1
合計ファイルサイズ: 15.5 MB
加工内容：
メタデータ列の解析と新列生成: metadata列（辞書型）を解析し、その中のreasoningをthought列に、difficultyをdifficulty列に展開しました。解析に失敗した行は除外されました。また、元のmetadata列は削除されました。
難易度によるフィルタリング:… See the full description on the dataset page: https://huggingface.co/datasets/LLMTeamAkiyama/cleand_moremilk_CoT_Reasoning_Quantom_Physics_And_Computing.cleand_moremilk_CoT_Reasoning_Scientific_Discovery_and_Research元データ: https://huggingface.co/datasets/moremilk/CoT_Reasoning_Scientific_Discovery_and_Research
使用したコード: https://github.com/LLMTeamAkiyama/0-data_prepare/tree/master/src/CoT_Reasoning_Scientific_Discovery_and_Research
データ件数: 3,733
平均トークン数: 1,193
最大トークン数: 2,489
合計トークン数: 4,453,517
ファイル形式: JSONL
ファイル分割数: 1
合計ファイルサイズ: 23.2 MB
加工内容：
メタデータ列の解析と新列生成: metadata列（辞書型）を解析し、その中のreasoningをthought列に、difficultyをdifficulty列に展開しました。解析に失敗した行は除外されました。また、元のmetadata列は削除されました。
難易度によるフィルタリング:… See the full description on the dataset page: https://huggingface.co/datasets/LLMTeamAkiyama/cleand_moremilk_CoT_Reasoning_Scientific_Discovery_and_Research.cot-oracle-reasoning-termination-balancedEpistemeAI__Reasoning-Llama-3.1-CoT-RE1-NMT-V2-ORPO-details
Dataset Card for Evaluation run of EpistemeAI/Reasoning-Llama-3.1-CoT-RE1-NMT-V2-ORPO
Dataset automatically created during the evaluation run of model EpistemeAI/Reasoning-Llama-3.1-CoT-RE1-NMT-V2-ORPO
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/EpistemeAI__Reasoning-Llama-3.1-CoT-RE1-NMT-V2-ORPO-details.Quant-CoT-Factor-Reasoning-PreviewQuantitative Factor Generation: Chain-of-Thought (CoT) Trajectories
Dataset Description
This is a 100-episode preview of a proprietary Reinforcement Learning from Environment Feedback (RLEF) dataset. It is designed to fine-tune Large Language Models (LLMs) for institutional quantitative finance, specifically systematic factor discovery and vectorized Python execution.
The Architecture
The data captures multi-turn agentic loops where the LLM:
Formulates a cross-sectional equity factor… See the full description on the dataset page: https://huggingface.co/datasets/1Happy-neuron/Quant-CoT-Factor-Reasoning-Preview.