eklav
Datasets
All datasets matching “eklav”offline-india-mapEklav-Math-Data
HETU-MathReasoning-CotCond-Data
Training data for the HETU (Hints Enable True Understanding) paper.
Task: math reasoning (AIME, GSM8K, MATH-500, Omni-MATH, GPQA-Diamond, MMLU)
Method: CotCond
Examples: 3,481 train / 35-36 held-out val
Format: ShareGPT (system + conversations: [{from, value}]), used for LoRA SFT via LLaMA-Factory.
Single-turn ShareGPT conversations. Each row: the math/logic problem plus a partial excerpt of the teacher's reasoning as a hint (human turn), and… See the full description on the dataset page: https://huggingface.co/datasets/AdarshSingh7647/Eklav-Math-Data.Eklav-Math-CotGen-Data
HETU-MathReasoning-CotGen-Data
Training data for the HETU (Hints Enable True Understanding) paper.
Task: math reasoning (AIME, GSM8K, MATH-500, Omni-MATH, GPQA-Diamond, MMLU)
Method: CotGen
Examples: 3,481 train / 35-36 held-out val
Format: ShareGPT (system + conversations: [{from, value}]), used for LoRA SFT via LLaMA-Factory.
Single-turn ShareGPT conversations, curated DeepSeek-R1-style math reasoning distillation. Each row: the raw math/logic problem (human turn) and the… See the full description on the dataset page: https://huggingface.co/datasets/AdarshSingh7647/Eklav-Math-CotGen-Data.Eklav-Reranker-CotGen-Data
HETU-PassageReranking-CotGen-Data
Training data for the HETU (Hints Enable True Understanding) paper.
Task: passage reranking (BRIGHT / NevIR benchmarks)
Method: CotGen
Examples: 381,934 train / held-out val
Format: ShareGPT (system + conversations: [{from, value}]), used for LoRA SFT via LLaMA-Factory.
Single-turn ShareGPT conversations. Each row: a query+passage relevance-judgment prompt (human turn) and the model's full chain-of-thought plus true/false judgment (gpt turn)… See the full description on the dataset page: https://huggingface.co/datasets/AdarshSingh7647/Eklav-Reranker-CotGen-Data.Eklav-Reranker-Data
HETU-PassageReranking-CotCond-Data
Training data for the HETU (Hints Enable True Understanding) paper.
Task: passage reranking (BRIGHT / NevIR benchmarks)
Method: CotCond
Examples: 381,934 train / held-out val
Format: ShareGPT (system + conversations: [{from, value}]), used for LoRA SFT via LLaMA-Factory.
Single-turn ShareGPT conversations. Each row: a query+passage relevance-judgment prompt plus a partial reasoning hint (human turn), and a continuation plus true/false… See the full description on the dataset page: https://huggingface.co/datasets/AdarshSingh7647/Eklav-Reranker-Data.ott-viewer-dropoff-retention
🎬 OTT Viewer Drop-Off & Retention Risk Dataset (v1.0)
📌 Overview
This dataset provides episode-level viewer behavior data for OTT (streaming) TV series, focused on drop-off patterns, retention risk, and engagement dynamics across episodes and seasons.
Unlike traditional catalog datasets (genres, ratings, cast), this dataset is designed to support realistic retention analysis, similar to how streaming platforms study when and why viewers stop watching.
Each row… See the full description on the dataset page: https://huggingface.co/datasets/Eklavya16/ott-viewer-dropoff-retention.