datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
rag-mini-bioasq-with-metadataThis dataset is an extension of the rag-mini-bioasq dataset.
Its difference resides in the text-corpus part of the aforementioned set where the metadata was added for each passage.
Metadata contains six separate categories, each in a dedicated column:
Year of the publication (publish_year)
Type of the publication (publish_type)
Country of the publication - often correlated with the homeland of the authors (country)
Number of pages (no_pages)
Authors (authors)
Keywords (keywords)
MetaRAG_Cross-Issue_OSSQA
MetaRAG Cross-Issue OSSQA
Dataset page: https://huggingface.co/datasets/MapleBi/MetaRAG_Cross-Issue_OSSQA
MetaRAG Cross-Issue OSSQA is an English open-source software issue question-answering and retrieval benchmark. Each example asks a question grounded in one GitHub issue and requires evidence from a related issue. The data contains explicit cross-issue references and a three-document silver evidence path.
Dataset configurations
Configuration
Splits
Rows… See the full description on the dataset page: https://huggingface.co/datasets/MapleBi/MetaRAG_Cross-Issue_OSSQA.cleand_meta-math_MetaMathQA元データ: https://huggingface.co/datasets/meta-math/MetaMathQA
データ件数: 394,369
平均トークン数: 233
最大トークン数: 2,874
合計トークン数: 91,798,611
ファイル形式: JSONL
ファイルサイズ: 297.9 MB
=================== 以下、加工内容をclaudeでまとめ。
MetaMathQAデータセット加工内容
データ読み込み・準備
HuggingFace Datasetsからmeta-math/MetaMathQAの訓練データ（395,000件）を読み込み
DeepSeek-R1-Distill-Qwen-32Bトークナイザーを使用してトークン数を計算
データ構造の理解・分析
全てのresponseが"The answer is:"で終わる統一フォーマットであることを確認
original_questionとresponseを結合してトークン数計算用テキストを作成… See the full description on the dataset page: https://huggingface.co/datasets/LLMTeamAkiyama/cleand_meta-math_MetaMathQA.single-turn-eval-meta_feedback_qwen3-4b_step2_gpt-5.4_gepa-n32
Single-turn eval — violetxi/meta_feedback_qwen3-4b_step2_gpt-5.4_gepa
Generated by teaching/inference/single_turn_eval_vllm.py. One row per problem; samples is the list of model responses, scores is per-sample correctness, and mean/best/worst are the aggregates used by mean@N / best@N / worst@N.
Eval results (n_samples_per_example = 32)
Overall
metric
value
n_examples
1006
mean@32
0.1796
best@32
0.3588
worst@32
0.0477
pass_rate
0.3588… See the full description on the dataset page: https://huggingface.co/datasets/violetxi/single-turn-eval-meta_feedback_qwen3-4b_step2_gpt-5.4_gepa-n32.apigen-inferred
apigen-inferred
A verified, GPT-5.5-distilled subset of the
argilla/apigen-function-calling
dataset (109k rows in the upstream), with every golden tool-call argument
labelled as literal or dependency-derived to enable a clean
function-calling benchmark.
Pipeline
Filter the upstream to rows where every called API actually works
(replay each tool call against the real implementation — distilabel
Python functions or live RapidAPI / cached responses) → 45,984 rows.
Distill… See the full description on the dataset page: https://huggingface.co/datasets/gdgc-metacong/apigen-inferred.mini-bioasq-with-metadataThis dataset is an extension of the rag-mini-bioasq dataset.
Its difference resides in the text-corpus part of the aforementioned set where the metadata was added for each passage.
Metadata contains six separate categories, each in a dedicated column:
Year of the publication (publish_year)
Type of the publication (publish_type)
Country of the publication - often correlated with the homeland of the authors (country)
Number of pages (no_pages)
Authors (authors)
Keywords (keywords)
single-turn-eval-meta_feedback_qwen3-4b_step2_gpt-5-nano_gepa-n32
Single-turn eval — violetxi/meta_feedback_qwen3-4b_step2_gpt-5-nano_gepa
Generated by teaching/inference/single_turn_eval_vllm.py. One row per problem; samples is the list of model responses, scores is per-sample correctness, and mean/best/worst are the aggregates used by mean@N / best@N / worst@N.
Eval results (n_samples_per_example = 32)
Overall
metric
value
n_examples
1006
mean@32
0.1804
best@32
0.3569
worst@32
0.0398
pass_rate… See the full description on the dataset page: https://huggingface.co/datasets/violetxi/single-turn-eval-meta_feedback_qwen3-4b_step2_gpt-5-nano_gepa-n32.