Datasets:

HIT-TMG
/

JevEmbed-Data

pretty_name: JevEmbed-Data
task_categories:
  - text-classification
size_categories:
  - 1M<n<10M
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*.parquet
      - split: test
        path: data/test-*.parquet
tags:
  - JevEmbed
  - system-one
  - synthetic
  - typed-decisions
  - probability-estimation
  - control
  - noul
  - choice
  - score
language:
  - en
license:
  - apache-2.0
  - cc0-1.0
  - cc-by-4.0

JevEmbed-Data

This dataset contains 1,601,157 training and 66,482 test questions for JevEmbed. Here, test is the source corpus's validation split. The ten Parquet files each hold at most 200,000 rows.

Fields

Each row is one decision. request_json and answers_json are JSON strings; parse them with json.loads.

Field	Meaning
`id`	Unique question ID.
`group`	Case ID. Related questions stay in the same split.
`request_json`	Input: `state` plus a `decision` question with its type, instructions, and optional criteria.
`answers_json`	Reference answer for `decision`. This is a training label, not model input.
`question_type`	`choice`, `score`, or `noul`. Also present inside `request_json`.
`source`	Short source name.
`source_repo`	Source dataset or project generator.
`source_revision`	Source version.
`source_row`	Row ID in that source.
`domain`	Topic tag for filtering or sampling.
`license`	License recorded for that source.
`original_split`	`train` or `validation`; the latter is published here as `test`.

Inside request_json, state is the case to judge. questions.decision has the task type, instructions, and any candidate descriptions (criteria). answers_json has the matching label. For example, a Choice answer can name one option or give a probability for each option. Score uses ordered levels; Noul gives a yes probability between 0 and 1. Hard and soft labels are kept.

Use with JevEmbed

JevEmbed uses one encoder for the question and its candidates. It compares their embeddings, then learns from the answer:

Task	What the encoder compares	Training target
Choice	State and instruction against each named option.	Chosen option or option probabilities.
Score	State and instruction against ordered score descriptions.	Chosen level, level probabilities, or expected score.
Noul	State against the question and, when given, its true/false descriptions.	Yes probability.

Choice and level-based Score use cosine similarity and a softmax loss. A numeric Score uses the expected level and squared error. Noul uses a yes/no loss on cosine similarity or the difference between true and false similarities. The answer and source fields are not put into the encoder input.

The current JevEmbed trainer reads JSONL. Convert the Parquet rows before training:

import json
from datasets import load_dataset

data = load_dataset("YOUR_NAMESPACE/JevEmbed-Data", streaming=True)
for split, path in (("train", "train.jsonl"), ("test", "validation.jsonl")):
    with open(path, "w", encoding="utf-8") as out:
        for row in data[split]:
            record = dict(id=row["id"], group=row["group"],
                          request=json.loads(row["request_json"]),
                          answers=json.loads(row["answers_json"]))
            out.write(json.dumps(record, ensure_ascii=False) + "\n")

In the JevEmbed repository, copy configs/training/lora.yaml to lora-2048.yaml, set max_input_tokens: 2048, and train KaLM v2.5 with:

python -m jevembed.training \
  --config configs/kalm-embedding-v2.5.yaml \
  --training-config ./lora-2048.yaml \
  --train-data train.jsonl \
  --eval-data validation.jsonl \
  --output artifacts/jevembed-data-kalm

See the training guide for setup and other models. Input lengths were checked with the KaLM tokenizer; check them again for a different model.

See sources and checks. This dataset has mixed licenses; check each row's license and the source report before reuse or redistribution.

Citation

If you find JevEmbed-Data useful, please consider citing the following papers:

@misc{zhao2025kalmembeddingv2,
      title={KaLM-Embedding-V2: Superior Training Techniques and Data Inspire A Versatile Embedding Model},
      author={Xinping Zhao and Xinshuo Hu and Zifei Shan and Shouzheng Huang and Yao Zhou and Xin Zhang and Zetian Sun and Zhenyu Liu and Dongfang Li and Xinyuan Wei and Youcheng Pan and Yang Xiang and Meishan Zhang and Haofen Wang and Jun Yu and Baotian Hu and Min Zhang},
      year={2025},
      eprint={2506.20923},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2506.20923},
}

@misc{hu2025kalmembedding,
      title={KaLM-Embedding: Superior Training Data Brings A Stronger Embedding Model},
      author={Xinshuo Hu and Zifei Shan and Xinping Zhao and Zetian Sun and Zhenyu Liu and Dongfang Li and Shaolin Ye and Xinyuan Wei and Qian Chen and Baotian Hu and Haofen Wang and Jun Yu and Min Zhang},
      year={2025},
      eprint={2501.01028},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2501.01028},
}