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librarian-bots

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dataset_cards_with_metadata

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Librarian Bots 71

Vishwaksen/AppointmentAssistant-V2

Vishwaksen

Invalid username or password.

YuchenLi01/ultrafeedback_binarized_ArmoRM

YuchenLi01

--- license: mit ---

piyushpradhan22/tt

piyushpradhan22

Invalid username or password.

ychen/mi-persona-alcohol-5k-diverse

ychen

--- dataset_info: features: - name: persona dtype: string - name: background_sketch dtype: string - name: profile dtype: string - name: index dtype: int64 - name: embedding sequence: float64 splits: - name: train num_bytes: 48409536 num_examples: 5000 download_size: 32436062 dataset_size: 48409536 configs: - config_name: default data_files: - split: train path: data/train-* ---

YuchenLi01/ultrafeedback_binarized_Skywork

YuchenLi01

--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: score_chosen dtype: float64 - name: score_rejected dtype: float64 - name: all_rm_scores sequence: float64 splits: - name: train num_bytes: 406911362 num_examples: 61135 - name: test num_bytes: 13201585 num_examples: 2000 download_size: 240918273 dataset_size: 420112947 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

sociate/H_and_M_VLM

sociate

--- dataset_info: features: - name: image dtype: image - name: description dtype: string - name: id dtype: string splits: - name: train num_bytes: 2195648097.625 num_examples: 6467 download_size: 2193536714 dataset_size: 2195648097.625 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/argilla-server-dataset-test-67ca7239-e9a8-4837-b370-1ac5b5075d73

argilla-internal-testing

Invalid username or password.

HuggingFaceH4/Llama-3.2-3B-Instruct-DVTS-completions

HuggingFaceH4

--- dataset_info: - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-0--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: pred dtype: string - name: completion_tokens dtype: int64 - name: scores sequence: sequence: float64 - name: agg_scores sequence: float64 - name: pred_weighted@1 dtype: string - name: pred_maj@1 dtype: string - name: pred_naive@1 dtype: string - name: pred_weighted@2 dtype: string - name: pred_maj@2 dtype: string - name: pred_naive@2 dtype: string - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string - name: pred_weighted@8 dtype: string - name: pred_maj@8 dtype: string - name: pred_naive@8 dtype: string - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string - name: pred_weighted@32 dtype: string - name: pred_maj@32 dtype: string - name: pred_naive@32 dtype: string - name: pred_weighted@64 dtype: string - name: pred_maj@64 dtype: string - name: pred_naive@64 dtype: string - name: pred_weighted@128 dtype: string - name: pred_maj@128 dtype: string - name: pred_naive@128 dtype: string - name: pred_weighted@256 dtype: string - name: pred_maj@256 dtype: string - name: pred_naive@256 dtype: string splits: - name: train num_bytes: 195371894 num_examples: 500 download_size: 28527714 dataset_size: 195371894 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-0--agg_strategy-last--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 splits: - name: train num_bytes: 128 num_examples: 4 download_size: 2049 dataset_size: 128 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-1--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: pred dtype: string - name: completion_tokens dtype: int64 - name: scores sequence: sequence: float64 - name: agg_scores sequence: float64 - name: pred_weighted@1 dtype: string - name: pred_maj@1 dtype: string - name: pred_naive@1 dtype: string - name: pred_weighted@2 dtype: string - name: pred_maj@2 dtype: string - name: pred_naive@2 dtype: string - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string - name: pred_weighted@8 dtype: string - name: pred_maj@8 dtype: string - name: pred_naive@8 dtype: string - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string - name: pred_weighted@32 dtype: string - name: pred_maj@32 dtype: string - name: pred_naive@32 dtype: string - name: pred_weighted@64 dtype: string - name: pred_maj@64 dtype: string - name: pred_naive@64 dtype: string - name: pred_weighted@128 dtype: string - name: pred_maj@128 dtype: string - name: pred_naive@128 dtype: string - name: pred_weighted@256 dtype: string - name: pred_maj@256 dtype: string - name: pred_naive@256 dtype: string splits: - name: train num_bytes: 193830170 num_examples: 500 download_size: 28245361 dataset_size: 193830170 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-1--agg_strategy-last--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 splits: - name: train num_bytes: 128 num_examples: 4 download_size: 2059 dataset_size: 128 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-2--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: pred dtype: string - name: completion_tokens dtype: int64 - name: scores sequence: sequence: float64 - name: agg_scores sequence: float64 - name: pred_weighted@1 dtype: string - name: pred_maj@1 dtype: string - name: pred_naive@1 dtype: string - name: pred_weighted@2 dtype: string - name: pred_maj@2 dtype: string - name: pred_naive@2 dtype: string - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string - name: pred_weighted@8 dtype: string - name: pred_maj@8 dtype: string - name: pred_naive@8 dtype: string - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string - name: pred_weighted@32 dtype: string - name: pred_maj@32 dtype: string - name: pred_naive@32 dtype: string - name: pred_weighted@64 dtype: string - name: pred_maj@64 dtype: string - name: pred_naive@64 dtype: string - name: pred_weighted@128 dtype: string - name: pred_maj@128 dtype: string - name: pred_naive@128 dtype: string - name: pred_weighted@256 dtype: string - name: pred_maj@256 dtype: string - name: pred_naive@256 dtype: string splits: - name: train num_bytes: 193878761 num_examples: 500 download_size: 28242668 dataset_size: 193878761 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-2--agg_strategy-last--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 splits: - name: train num_bytes: 128 num_examples: 4 download_size: 2056 dataset_size: 128 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-3--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: pred dtype: string - name: completion_tokens dtype: int64 - name: scores sequence: sequence: float64 - name: agg_scores sequence: float64 - name: pred_weighted@1 dtype: string - name: pred_maj@1 dtype: string - name: pred_naive@1 dtype: string - name: pred_weighted@2 dtype: string - name: pred_maj@2 dtype: string - name: pred_naive@2 dtype: string - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string - name: pred_weighted@8 dtype: string - name: pred_maj@8 dtype: string - name: pred_naive@8 dtype: string - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string - name: pred_weighted@32 dtype: string - name: pred_maj@32 dtype: string - name: pred_naive@32 dtype: string - name: pred_weighted@64 dtype: string - name: pred_maj@64 dtype: string - name: pred_naive@64 dtype: string - name: pred_weighted@128 dtype: string - name: pred_maj@128 dtype: string - name: pred_naive@128 dtype: string - name: pred_weighted@256 dtype: string - name: pred_maj@256 dtype: string - name: pred_naive@256 dtype: string splits: - name: train num_bytes: 195238447 num_examples: 500 download_size: 28529254 dataset_size: 195238447 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-3--agg_strategy-last--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 splits: - name: train num_bytes: 128 num_examples: 4 download_size: 2060 dataset_size: 128 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-4--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: pred dtype: string - name: completion_tokens dtype: int64 - name: scores sequence: sequence: float64 - name: agg_scores sequence: float64 - name: pred_weighted@1 dtype: string - name: pred_maj@1 dtype: string - name: pred_naive@1 dtype: string - name: pred_weighted@2 dtype: string - name: pred_maj@2 dtype: string - name: pred_naive@2 dtype: string - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string - name: pred_weighted@8 dtype: string - name: pred_maj@8 dtype: string - name: pred_naive@8 dtype: string - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string - name: pred_weighted@32 dtype: string - name: pred_maj@32 dtype: string - name: pred_naive@32 dtype: string - name: pred_weighted@64 dtype: string - name: pred_maj@64 dtype: string - name: pred_naive@64 dtype: string - name: pred_weighted@128 dtype: string - name: pred_maj@128 dtype: string - name: pred_naive@128 dtype: string - name: pred_weighted@256 dtype: string - name: pred_maj@256 dtype: string - name: pred_naive@256 dtype: string splits: - name: train num_bytes: 193859741 num_examples: 500 download_size: 28256691 dataset_size: 193859741 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-4--agg_strategy-last--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 splits: - name: train num_bytes: 128 num_examples: 4 download_size: 2058 dataset_size: 128 configs: - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-0--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-0--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-0--agg_strategy-last--evals data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-0--agg_strategy-last--evals/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-1--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-1--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-1--agg_strategy-last--evals data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-1--agg_strategy-last--evals/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-2--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-2--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-2--agg_strategy-last--evals data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-2--agg_strategy-last--evals/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-3--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-3--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-3--agg_strategy-last--evals data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-3--agg_strategy-last--evals/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-4--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-4--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-4--agg_strategy-last--evals data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-4--agg_strategy-last--evals/train-* ---

HuggingFaceH4/Llama-3.2-3B-Instruct-best-of-N-completions

HuggingFaceH4

--- dataset_info: - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-2048--max_tokens-2048--bsz-8--seed-0--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: scores sequence: sequence: float64 - name: pred dtype: string - name: completion_tokens sequence: int64 splits: - name: train num_bytes: 1544368640 num_examples: 500 download_size: 468618769 dataset_size: 1544368640 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-2048--max_tokens-2048--bsz-8--seed-0--agg_strategy-last--levels features: - name: problem dtype: string - name: solution dtype: string - name: level dtype: int64 - name: level_oracle_first dtype: int64 - name: level_oracle_last dtype: int64 - name: level_oracle_all dtype: int64 - name: level_model_first dtype: int64 - name: level_model_last dtype: int64 - name: level_model_all dtype: int64 splits: - name: train num_bytes: 395590 num_examples: 500 download_size: 206919 dataset_size: 395590 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-0--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: scores sequence: sequence: float64 - name: pred dtype: string - name: completion_tokens sequence: int64 - name: agg_scores sequence: float64 - name: pred_weighted@1 dtype: string - name: pred_maj@1 dtype: string - name: pred_naive@1 dtype: string - name: pred_weighted@2 dtype: string - name: pred_maj@2 dtype: string - name: pred_naive@2 dtype: string - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string - name: pred_weighted@8 dtype: string - name: pred_maj@8 dtype: string - name: pred_naive@8 dtype: string - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string - name: pred_weighted@32 dtype: string - name: pred_maj@32 dtype: string - name: pred_naive@32 dtype: string - name: pred_weighted@64 dtype: string - name: pred_maj@64 dtype: string - name: pred_naive@64 dtype: string - name: pred_weighted@128 dtype: string - name: pred_maj@128 dtype: string - name: pred_naive@128 dtype: string - name: pred_weighted@256 dtype: string - name: pred_maj@256 dtype: string - name: pred_naive@256 dtype: string splits: - name: train num_bytes: 194998809 num_examples: 500 download_size: 59335978 dataset_size: 194998809 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-0--agg_strategy-last--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 splits: - name: train num_bytes: 288 num_examples: 9 download_size: 2564 dataset_size: 288 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-1--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: scores sequence: sequence: float64 - name: pred dtype: string - name: completion_tokens sequence: int64 - name: agg_scores sequence: float64 - name: pred_weighted@1 dtype: string - name: pred_maj@1 dtype: string - name: pred_naive@1 dtype: string - name: pred_weighted@2 dtype: string - name: pred_maj@2 dtype: string - name: pred_naive@2 dtype: string - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string - name: pred_weighted@8 dtype: string - name: pred_maj@8 dtype: string - name: pred_naive@8 dtype: string - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string - name: pred_weighted@32 dtype: string - name: pred_maj@32 dtype: string - name: pred_naive@32 dtype: string - name: pred_weighted@64 dtype: string - name: pred_maj@64 dtype: string - name: pred_naive@64 dtype: string - name: pred_weighted@128 dtype: string - name: pred_maj@128 dtype: string - name: pred_naive@128 dtype: string - name: pred_weighted@256 dtype: string - name: pred_maj@256 dtype: string - name: pred_naive@256 dtype: string splits: - name: train num_bytes: 194921320 num_examples: 500 download_size: 59383076 dataset_size: 194921320 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-1--agg_strategy-last--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 splits: - name: train num_bytes: 288 num_examples: 9 download_size: 2579 dataset_size: 288 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-2--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: scores sequence: sequence: float64 - name: pred dtype: string - name: completion_tokens sequence: int64 - name: agg_scores sequence: float64 - name: pred_weighted@1 dtype: string - name: pred_maj@1 dtype: string - name: pred_naive@1 dtype: string - name: pred_weighted@2 dtype: string - name: pred_maj@2 dtype: string - name: pred_naive@2 dtype: string - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string - name: pred_weighted@8 dtype: string - name: pred_maj@8 dtype: string - name: pred_naive@8 dtype: string - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string - name: pred_weighted@32 dtype: string - name: pred_maj@32 dtype: string - name: pred_naive@32 dtype: string - name: pred_weighted@64 dtype: string - name: pred_maj@64 dtype: string - name: pred_naive@64 dtype: string - name: pred_weighted@128 dtype: string - name: pred_maj@128 dtype: string - name: pred_naive@128 dtype: string - name: pred_weighted@256 dtype: string - name: pred_maj@256 dtype: string - name: pred_naive@256 dtype: string splits: - name: train num_bytes: 194754596 num_examples: 500 download_size: 59308362 dataset_size: 194754596 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-2--agg_strategy-last--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 splits: - name: train num_bytes: 288 num_examples: 9 download_size: 2558 dataset_size: 288 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-3--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: scores sequence: sequence: float64 - name: pred dtype: string - name: completion_tokens sequence: int64 - name: agg_scores sequence: float64 - name: pred_weighted@1 dtype: string - name: pred_maj@1 dtype: string - name: pred_naive@1 dtype: string - name: pred_weighted@2 dtype: string - name: pred_maj@2 dtype: string - name: pred_naive@2 dtype: string - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string - name: pred_weighted@8 dtype: string - name: pred_maj@8 dtype: string - name: pred_naive@8 dtype: string - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string - name: pred_weighted@32 dtype: string - name: pred_maj@32 dtype: string - name: pred_naive@32 dtype: string - name: pred_weighted@64 dtype: string - name: pred_maj@64 dtype: string - name: pred_naive@64 dtype: string - name: pred_weighted@128 dtype: string - name: pred_maj@128 dtype: string - name: pred_naive@128 dtype: string - name: pred_weighted@256 dtype: string - name: pred_maj@256 dtype: string - name: pred_naive@256 dtype: string splits: - name: train num_bytes: 195148367 num_examples: 500 download_size: 59442449 dataset_size: 195148367 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-3--agg_strategy-last--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 splits: - name: train num_bytes: 288 num_examples: 9 download_size: 2580 dataset_size: 288 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-4--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: scores sequence: sequence: float64 - name: pred dtype: string - name: completion_tokens sequence: int64 - name: agg_scores sequence: float64 - name: pred_weighted@1 dtype: string - name: pred_maj@1 dtype: string - name: pred_naive@1 dtype: string - name: pred_weighted@2 dtype: string - name: pred_maj@2 dtype: string - name: pred_naive@2 dtype: string - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string - name: pred_weighted@8 dtype: string - name: pred_maj@8 dtype: string - name: pred_naive@8 dtype: string - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string - name: pred_weighted@32 dtype: string - name: pred_maj@32 dtype: string - name: pred_naive@32 dtype: string - name: pred_weighted@64 dtype: string - name: pred_maj@64 dtype: string - name: pred_naive@64 dtype: string - name: pred_weighted@128 dtype: string - name: pred_maj@128 dtype: string - name: pred_naive@128 dtype: string - name: pred_weighted@256 dtype: string - name: pred_maj@256 dtype: string - name: pred_naive@256 dtype: string splits: - name: train num_bytes: 195110534 num_examples: 500 download_size: 59421725 dataset_size: 195110534 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-4--agg_strategy-last--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 splits: - name: train num_bytes: 288 num_examples: 9 download_size: 2564 dataset_size: 288 configs: - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-2048--max_tokens-2048--bsz-8--seed-0--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-2048--max_tokens-2048--bsz-8--seed-0--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-2048--max_tokens-2048--bsz-8--seed-0--agg_strategy-last--levels data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-2048--max_tokens-2048--bsz-8--seed-0--agg_strategy-last--levels/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-0--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-0--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-0--agg_strategy-last--evals data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-0--agg_strategy-last--evals/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-1--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-1--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-1--agg_strategy-last--evals data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-1--agg_strategy-last--evals/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-2--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-2--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-2--agg_strategy-last--evals data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-2--agg_strategy-last--evals/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-3--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-3--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-3--agg_strategy-last--evals data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-3--agg_strategy-last--evals/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-4--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-4--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-4--agg_strategy-last--evals data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--max_tokens-2048--bsz-8--seed-4--agg_strategy-last--evals/train-* ---

HuggingFaceH4/Llama-3.2-3B-Instruct-beam-search-completions

HuggingFaceH4

--- dataset_info: - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--m-4--iters-40--look-0--seed-0--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: pred dtype: string - name: completion_tokens sequence: int64 - name: scores sequence: sequence: float64 - name: agg_scores sequence: float64 - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string splits: - name: train num_bytes: 14148766 num_examples: 500 download_size: 2023744 dataset_size: 14148766 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--m-4--iters-40--look-0--seed-1--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: pred dtype: string - name: completion_tokens sequence: int64 - name: scores sequence: sequence: float64 - name: agg_scores sequence: float64 - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string splits: - name: train num_bytes: 13673930 num_examples: 500 download_size: 2006709 dataset_size: 13673930 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--m-4--iters-40--look-0--seed-2--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: pred dtype: string - name: completion_tokens sequence: int64 - name: scores sequence: sequence: float64 - name: agg_scores sequence: float64 - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string splits: - name: train num_bytes: 13412177 num_examples: 500 download_size: 1956116 dataset_size: 13412177 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--m-4--iters-40--look-0--seed-3--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: pred dtype: string - name: completion_tokens sequence: int64 - name: scores sequence: sequence: float64 - name: agg_scores sequence: float64 - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string splits: - name: train num_bytes: 13651685 num_examples: 500 download_size: 1961268 dataset_size: 13651685 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--m-4--iters-40--look-0--seed-4--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: pred dtype: string - name: completion_tokens sequence: int64 - name: scores sequence: sequence: float64 - name: agg_scores sequence: float64 - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string splits: - name: train num_bytes: 13941713 num_examples: 500 download_size: 1996648 dataset_size: 13941713 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-0--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: pred dtype: string - name: completion_tokens sequence: int64 - name: scores sequence: sequence: float64 - name: agg_scores sequence: float64 - name: pred_weighted@256 dtype: string - name: pred_maj@256 dtype: string - name: pred_naive@256 dtype: string splits: - name: train num_bytes: 211373298 num_examples: 500 download_size: 26744594 dataset_size: 211373298 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-0--agg_strategy-last--evals features: - name: n dtype: 'null' - name: acc_naive dtype: 'null' - name: acc_weighted dtype: 'null' - name: acc_maj dtype: 'null' splits: - name: train num_bytes: 0 num_examples: 0 download_size: 1125 dataset_size: 0 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-4--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: pred dtype: string - name: completion_tokens sequence: int64 - name: scores sequence: sequence: float64 - name: agg_scores sequence: float64 - name: pred_weighted@256 dtype: string - name: pred_maj@256 dtype: string - name: pred_naive@256 dtype: string splits: - name: train num_bytes: 207666692 num_examples: 500 download_size: 26863337 dataset_size: 207666692 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-0--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: pred dtype: string - name: completion_tokens sequence: int64 - name: scores sequence: sequence: float64 - name: agg_scores sequence: float64 - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string splits: - name: train num_bytes: 4498133 num_examples: 500 download_size: 1057326 dataset_size: 4498133 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-0--agg_strategy-last--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 splits: - name: train num_bytes: 32 num_examples: 1 download_size: 2360 dataset_size: 32 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-1--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: pred dtype: string - name: completion_tokens sequence: int64 - name: scores sequence: sequence: float64 - name: agg_scores sequence: float64 - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string splits: - name: train num_bytes: 4193515 num_examples: 500 download_size: 989837 dataset_size: 4193515 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-1--agg_strategy-last--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 splits: - name: train num_bytes: 32 num_examples: 1 download_size: 2360 dataset_size: 32 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-2--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: pred dtype: string - name: completion_tokens sequence: int64 - name: scores sequence: sequence: float64 - name: agg_scores sequence: float64 - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string splits: - name: train num_bytes: 4318247 num_examples: 500 download_size: 992357 dataset_size: 4318247 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-2--agg_strategy-last--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 splits: - name: train num_bytes: 32 num_examples: 1 download_size: 2360 dataset_size: 32 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-3--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: pred dtype: string - name: completion_tokens sequence: int64 - name: scores sequence: sequence: float64 - name: agg_scores sequence: float64 - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string splits: - name: train num_bytes: 4137316 num_examples: 500 download_size: 963390 dataset_size: 4137316 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-3--agg_strategy-last--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 splits: - name: train num_bytes: 32 num_examples: 1 download_size: 2360 dataset_size: 32 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-4--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: pred dtype: string - name: completion_tokens sequence: int64 - name: scores sequence: sequence: float64 - name: agg_scores sequence: float64 - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string splits: - name: train num_bytes: 4201133 num_examples: 500 download_size: 973321 dataset_size: 4201133 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-4--agg_strategy-last--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 splits: - name: train num_bytes: 32 num_examples: 1 download_size: 2360 dataset_size: 32 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-64--m-4--iters-40--look-0--seed-0--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: pred dtype: string - name: completion_tokens sequence: int64 - name: scores sequence: sequence: float64 - name: agg_scores sequence: float64 - name: pred_weighted@64 dtype: string - name: pred_maj@64 dtype: string - name: pred_naive@64 dtype: string splits: - name: train num_bytes: 53850426 num_examples: 500 download_size: 6722896 dataset_size: 53850426 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-64--m-4--iters-40--look-0--seed-1--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: pred dtype: string - name: completion_tokens sequence: int64 - name: scores sequence: sequence: float64 - name: agg_scores sequence: float64 - name: pred_weighted@64 dtype: string - name: pred_maj@64 dtype: string - name: pred_naive@64 dtype: string splits: - name: train num_bytes: 53865181 num_examples: 500 download_size: 6723497 dataset_size: 53865181 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-64--m-4--iters-40--look-0--seed-2--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: pred dtype: string - name: completion_tokens sequence: int64 - name: scores sequence: sequence: float64 - name: agg_scores sequence: float64 - name: pred_weighted@64 dtype: string - name: pred_maj@64 dtype: string - name: pred_naive@64 dtype: string splits: - name: train num_bytes: 52820899 num_examples: 500 download_size: 6404174 dataset_size: 52820899 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-64--m-4--iters-40--look-0--seed-3--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: pred dtype: string - name: completion_tokens sequence: int64 - name: scores sequence: sequence: float64 - name: agg_scores sequence: float64 - name: pred_weighted@64 dtype: string - name: pred_maj@64 dtype: string - name: pred_naive@64 dtype: string splits: - name: train num_bytes: 54014686 num_examples: 500 download_size: 7000570 dataset_size: 54014686 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-64--m-4--iters-40--look-0--seed-4--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: completions sequence: string - name: pred dtype: string - name: completion_tokens sequence: int64 - name: scores sequence: sequence: float64 - name: agg_scores sequence: float64 - name: pred_weighted@64 dtype: string - name: pred_maj@64 dtype: string - name: pred_naive@64 dtype: string splits: - name: train num_bytes: 53334941 num_examples: 500 download_size: 6821112 dataset_size: 53334941 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-0--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string - name: pred_weighted@64 dtype: string - name: pred_maj@64 dtype: string - name: pred_naive@64 dtype: string - name: pred_weighted@256 dtype: string - name: pred_maj@256 dtype: string - name: pred_naive@256 dtype: string splits: - name: train num_bytes: 506796 num_examples: 500 download_size: 250215 dataset_size: 506796 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-0--agg_strategy-last--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 splits: - name: train num_bytes: 128 num_examples: 4 download_size: 2443 dataset_size: 128 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-1--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string - name: pred_weighted@64 dtype: string - name: pred_maj@64 dtype: string - name: pred_naive@64 dtype: string splits: - name: train num_bytes: 476788 num_examples: 500 download_size: 238895 dataset_size: 476788 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-1--agg_strategy-last--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 splits: - name: train num_bytes: 96 num_examples: 3 download_size: 2420 dataset_size: 96 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-2--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string - name: pred_weighted@64 dtype: string - name: pred_maj@64 dtype: string - name: pred_naive@64 dtype: string splits: - name: train num_bytes: 477357 num_examples: 500 download_size: 238937 dataset_size: 477357 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-2--agg_strategy-last--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 splits: - name: train num_bytes: 96 num_examples: 3 download_size: 2425 dataset_size: 96 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-3--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string - name: pred_weighted@64 dtype: string - name: pred_maj@64 dtype: string - name: pred_naive@64 dtype: string splits: - name: train num_bytes: 476704 num_examples: 500 download_size: 238909 dataset_size: 476704 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-3--agg_strategy-last--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 splits: - name: train num_bytes: 96 num_examples: 3 download_size: 2422 dataset_size: 96 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-4--agg_strategy-last features: - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: subject dtype: string - name: level dtype: int64 - name: unique_id dtype: string - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string - name: pred_weighted@64 dtype: string - name: pred_maj@64 dtype: string - name: pred_naive@64 dtype: string - name: pred_weighted@256 dtype: string - name: pred_maj@256 dtype: string - name: pred_naive@256 dtype: string splits: - name: train num_bytes: 502550 num_examples: 500 download_size: 249785 dataset_size: 502550 - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-4--agg_strategy-last--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 splits: - name: train num_bytes: 128 num_examples: 4 download_size: 2449 dataset_size: 128 configs: - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--m-4--iters-40--look-0--seed-0--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--m-4--iters-40--look-0--seed-0--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--m-4--iters-40--look-0--seed-1--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--m-4--iters-40--look-0--seed-1--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--m-4--iters-40--look-0--seed-2--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--m-4--iters-40--look-0--seed-2--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--m-4--iters-40--look-0--seed-3--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--m-4--iters-40--look-0--seed-3--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--m-4--iters-40--look-0--seed-4--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-16--m-4--iters-40--look-0--seed-4--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-0--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-0--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-0--agg_strategy-last--evals data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-0--agg_strategy-last--evals/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-4--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-256--m-4--iters-40--look-0--seed-4--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-0--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-0--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-0--agg_strategy-last--evals data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-0--agg_strategy-last--evals/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-1--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-1--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-1--agg_strategy-last--evals data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-1--agg_strategy-last--evals/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-2--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-2--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-2--agg_strategy-last--evals data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-2--agg_strategy-last--evals/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-3--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-3--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-3--agg_strategy-last--evals data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-3--agg_strategy-last--evals/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-4--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-4--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-4--agg_strategy-last--evals data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-4--m-4--iters-40--look-0--seed-4--agg_strategy-last--evals/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-64--m-4--iters-40--look-0--seed-0--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-64--m-4--iters-40--look-0--seed-0--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-64--m-4--iters-40--look-0--seed-1--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-64--m-4--iters-40--look-0--seed-1--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-64--m-4--iters-40--look-0--seed-2--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-64--m-4--iters-40--look-0--seed-2--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-64--m-4--iters-40--look-0--seed-3--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-64--m-4--iters-40--look-0--seed-3--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-64--m-4--iters-40--look-0--seed-4--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-64--m-4--iters-40--look-0--seed-4--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-0--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-0--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-0--agg_strategy-last--evals data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-0--agg_strategy-last--evals/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-1--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-1--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-1--agg_strategy-last--evals data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-1--agg_strategy-last--evals/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-2--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-2--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-2--agg_strategy-last--evals data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-2--agg_strategy-last--evals/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-3--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-3--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-3--agg_strategy-last--evals data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-3--agg_strategy-last--evals/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-4--agg_strategy-last data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-4--agg_strategy-last/train-* - config_name: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-4--agg_strategy-last--evals data_files: - split: train path: HuggingFaceH4_MATH-500--T-0.8--top_p-1.0--n-merged--m-4--iters-40--look-0--seed-4--agg_strategy-last--evals/train-* ---

kngrg/wikifacts-sents

kngrg

--- license: mit language: - ru configs: - config_name: corpus data_files: - corpus.jsonl - config_name: queries data_files: - queries.jsonl ---

kngrg/wikifacts-sents-qrels

kngrg

--- license: mit language: - ru configs: - config_name: qrels data_files: - split: dev path: dev.tsv ---

chengzl18/EmbodiedEval

chengzl18

--- license: mit task_categories: - video-text-to-text language: - en size_categories: - n<1K configs: - config_name: benchmark data_files: - split: test path: "tasks/tasks.json" ---

violetxi/MATH-500_L3_best_first_N128_B8_D15_T0.0001_0-75

violetxi

--- dataset_info: features: - name: problem dtype: string - name: solution dtype: string - name: search_trace_with_values dtype: string - name: search_method dtype: string - name: ground_truth dtype: string - name: search_input_tokens dtype: int64 - name: search_output_tokens dtype: int64 - name: solution_input_tokens dtype: int64 - name: solution_output_tokens dtype: int64 splits: - name: train num_bytes: 897719 num_examples: 75 download_size: 230089 dataset_size: 897719 configs: - config_name: default data_files: - split: train path: data/train-* ---

shin020810/LLM_23_121_2

shin020810

--- dataset_info: - config_name: 공학 features: - name: add_info dtype: string - name: question dtype: string - name: rank1 ans dtype: string - name: rank2 ans dtype: string - name: rank3 ans dtype: string - name: rank4 ans dtype: string - name: rank5 ans dtype: string splits: - name: train num_bytes: 15774844 num_examples: 1955 - name: validation num_bytes: 5153828 num_examples: 651 - name: test num_bytes: 5057856 num_examples: 651 download_size: 11802831 dataset_size: 25986528 - config_name: 기타 features: - name: add_info dtype: string - name: question dtype: string - name: rank1 ans dtype: string - name: rank2 ans dtype: string - name: rank3 ans dtype: string - name: rank4 ans dtype: string - name: rank5 ans dtype: string splits: - name: train num_bytes: 11558689 num_examples: 1920 - name: validation num_bytes: 3938947 num_examples: 640 - name: test num_bytes: 3932897 num_examples: 640 download_size: 9289877 dataset_size: 19430533 - config_name: 명칭 features: - name: add_info dtype: string - name: question dtype: string - name: rank1 ans dtype: string - name: rank2 ans dtype: string - name: rank3 ans dtype: string - name: rank4 ans dtype: string - name: rank5 ans dtype: string splits: - name: train num_bytes: 106957 num_examples: 42 - name: validation num_bytes: 50502 num_examples: 14 - name: test num_bytes: 37032 num_examples: 13 download_size: 134761 dataset_size: 194491 - config_name: 보건 features: - name: add_info dtype: string - name: question dtype: string - name: rank1 ans dtype: string - name: rank2 ans dtype: string - name: rank3 ans dtype: string - name: rank4 ans dtype: string - name: rank5 ans dtype: string splits: - name: train num_bytes: 15864981 num_examples: 2154 - name: validation num_bytes: 5362495 num_examples: 717 - name: test num_bytes: 5283429 num_examples: 717 download_size: 12180289 dataset_size: 26510905 - config_name: 사회 features: - name: add_info dtype: string - name: question dtype: string - name: rank1 ans dtype: string - name: rank2 ans dtype: string - name: rank3 ans dtype: string - name: rank4 ans dtype: string - name: rank5 ans dtype: string splits: - name: train num_bytes: 23282668 num_examples: 2976 - name: validation num_bytes: 7646922 num_examples: 991 - name: test num_bytes: 7919322 num_examples: 991 download_size: 17887138 dataset_size: 38848912 - config_name: 산업 features: - name: add_info dtype: string - name: question dtype: string - name: rank1 ans dtype: string - name: rank2 ans dtype: string - name: rank3 ans dtype: string - name: rank4 ans dtype: string - name: rank5 ans dtype: string splits: - name: train num_bytes: 9656402 num_examples: 1243 - name: validation num_bytes: 3170530 num_examples: 414 - name: test num_bytes: 3147612 num_examples: 414 download_size: 7335622 dataset_size: 15974544 - config_name: 예체능 features: - name: add_info dtype: string - name: question dtype: string - name: rank1 ans dtype: string - name: rank2 ans dtype: string - name: rank3 ans dtype: string - name: rank4 ans dtype: string - name: rank5 ans dtype: string splits: - name: train num_bytes: 21453991 num_examples: 3366 - name: validation num_bytes: 7115415 num_examples: 1122 - name: test num_bytes: 7101887 num_examples: 1122 download_size: 17100772 dataset_size: 35671293 - config_name: 인문 features: - name: add_info dtype: string - name: question dtype: string - name: rank1 ans dtype: string - name: rank2 ans dtype: string - name: rank3 ans dtype: string - name: rank4 ans dtype: string - name: rank5 ans dtype: string splits: - name: train num_bytes: 9183681 num_examples: 1326 - name: validation num_bytes: 3165065 num_examples: 441 - name: test num_bytes: 3089716 num_examples: 441 download_size: 7192246 dataset_size: 15438462 - config_name: 자연 features: - name: add_info dtype: string - name: question dtype: string - name: rank1 ans dtype: string - name: rank2 ans dtype: string - name: rank3 ans dtype: string - name: rank4 ans dtype: string - name: rank5 ans dtype: string splits: - name: train num_bytes: 17959818 num_examples: 2642 - name: validation num_bytes: 5818943 num_examples: 880 - name: test num_bytes: 5924847 num_examples: 880 download_size: 13980380 dataset_size: 29703608 - config_name: 종교 features: - name: add_info dtype: string - name: question dtype: string - name: rank1 ans dtype: string - name: rank2 ans dtype: string - name: rank3 ans dtype: string - name: rank4 ans dtype: string - name: rank5 ans dtype: string splits: - name: train num_bytes: 1315852 num_examples: 208 - name: validation num_bytes: 427300 num_examples: 69 - name: test num_bytes: 435066 num_examples: 69 download_size: 1075778 dataset_size: 2178218 configs: - config_name: 공학 data_files: - split: train path: 공학/train-* - split: validation path: 공학/validation-* - split: test path: 공학/test-* - config_name: 기타 data_files: - split: train path: 기타/train-* - split: validation path: 기타/validation-* - split: test path: 기타/test-* - config_name: 명칭 data_files: - split: train path: 명칭/train-* - split: validation path: 명칭/validation-* - split: test path: 명칭/test-* - config_name: 보건 data_files: - split: train path: 보건/train-* - split: validation path: 보건/validation-* - split: test path: 보건/test-* - config_name: 사회 data_files: - split: train path: 사회/train-* - split: validation path: 사회/validation-* - split: test path: 사회/test-* - config_name: 산업 data_files: - split: train path: 산업/train-* - split: validation path: 산업/validation-* - split: test path: 산업/test-* - config_name: 예체능 data_files: - split: train path: 예체능/train-* - split: validation path: 예체능/validation-* - split: test path: 예체능/test-* - config_name: 인문 data_files: - split: train path: 인문/train-* - split: validation path: 인문/validation-* - split: test path: 인문/test-* - config_name: 자연 data_files: - split: train path: 자연/train-* - split: validation path: 자연/validation-* - split: test path: 자연/test-* - config_name: 종교 data_files: - split: train path: 종교/train-* - split: validation path: 종교/validation-* - split: test path: 종교/test-* ---

dunghuynh/SalBench

dunghuynh

--- license: mit dataset_info: - config_name: O3 features: - name: image_id dtype: string - name: image dtype: image - name: question dtype: string - name: instruction dtype: string - name: answer dtype: string - name: fewshots struct: - name: images sequence: image - name: texts sequence: string splits: - name: test num_examples: 2001 - config_name: O3_3shots features: - name: image_id dtype: string - name: image dtype: image - name: question dtype: string - name: instruction dtype: string - name: answer dtype: string - name: fewshots struct: - name: images sequence: image - name: texts sequence: string splits: - name: test num_examples: 2001 - config_name: O3_5shots features: - name: image_id dtype: string - name: image dtype: image - name: question dtype: string - name: instruction dtype: string - name: answer dtype: string - name: fewshots struct: - name: images sequence: image - name: texts sequence: string splits: - name: test num_examples: 2001 - config_name: O3_box features: - name: image_id dtype: string - name: image dtype: image - name: question dtype: string - name: instruction dtype: string - name: answer dtype: string - name: fewshots struct: - name: images sequence: image - name: texts sequence: string splits: - name: test num_examples: 2001 - config_name: O3_box_3shots features: - name: image_id dtype: string - name: image dtype: image - name: question dtype: string - name: instruction dtype: string - name: answer dtype: string - name: fewshots struct: - name: images sequence: image - name: texts sequence: string splits: - name: test num_examples: 2001 - config_name: O3_box_5shots features: - name: image_id dtype: string - name: image dtype: image - name: question dtype: string - name: instruction dtype: string - name: answer dtype: string - name: fewshots struct: - name: images sequence: image - name: texts sequence: string splits: - name: test num_examples: 2001 - config_name: O3_box_img features: - name: image_id dtype: string - name: image dtype: image - name: question dtype: string - name: instruction dtype: string - name: answer dtype: string - name: fewshots struct: - name: images sequence: image - name: texts sequence: string splits: - name: test num_examples: 2001 - config_name: O3_box_img_3shots features: - name: image_id dtype: string - name: image dtype: image - name: question dtype: string - name: instruction dtype: string - name: answer dtype: string - name: fewshots struct: - name: images sequence: image - name: texts sequence: string splits: - name: test num_examples: 2001 - config_name: O3_box_img_5shots features: - name: image_id dtype: string - name: image dtype: image - name: question dtype: string - name: instruction dtype: string - name: answer dtype: string - name: fewshots struct: - name: images sequence: image - name: texts sequence: string splits: - name: test num_examples: 2001 - config_name: P3 features: - name: image_id dtype: string - name: image dtype: image - name: question dtype: string - name: instruction dtype: string - name: answer dtype: string - name: fewshots struct: - name: images sequence: image - name: texts sequence: string splits: - name: test num_examples: 2589 - config_name: P3_3shots features: - name: image_id dtype: string - name: image dtype: image - name: question dtype: string - name: instruction dtype: string - name: answer dtype: string - name: fewshots struct: - name: images sequence: image - name: texts sequence: string splits: - name: test num_examples: 2589 - config_name: P3_5shots features: - name: image_id dtype: string - name: image dtype: image - name: question dtype: string - name: instruction dtype: string - name: answer dtype: string - name: fewshots struct: - name: images sequence: image - name: texts sequence: string splits: - name: test num_examples: 2589 - config_name: P3_box features: - name: image_id dtype: string - name: image dtype: image - name: question dtype: string - name: instruction dtype: string - name: answer dtype: string - name: fewshots struct: - name: images sequence: image - name: texts sequence: string splits: - name: test num_examples: 2589 - config_name: P3_box_3shots features: - name: image_id dtype: string - name: image dtype: image - name: question dtype: string - name: instruction dtype: string - name: answer dtype: string - name: fewshots struct: - name: images sequence: image - name: texts sequence: string splits: - name: test num_examples: 2589 - config_name: P3_box_5shots features: - name: image_id dtype: string - name: image dtype: image - name: question dtype: string - name: instruction dtype: string - name: answer dtype: string - name: fewshots struct: - name: images sequence: image - name: texts sequence: string splits: - name: test num_examples: 2589 - config_name: P3_box_img features: - name: image_id dtype: string - name: image dtype: image - name: question dtype: string - name: instruction dtype: string - name: answer dtype: string - name: fewshots struct: - name: images sequence: image - name: texts sequence: string splits: - name: test num_examples: 2589 - config_name: P3_box_img_3shots features: - name: image_id dtype: string - name: image dtype: image - name: question dtype: string - name: instruction dtype: string - name: answer dtype: string - name: fewshots struct: - name: images sequence: image - name: texts sequence: string splits: - name: test num_examples: 2589 - config_name: P3_box_img_5shots features: - name: image_id dtype: string - name: image dtype: image - name: question dtype: string - name: instruction dtype: string - name: answer dtype: string - name: fewshots struct: - name: images sequence: image - name: texts sequence: string splits: - name: test num_examples: 2589 configs: - config_name: O3 data_files: - split: test path: O3/shard* - config_name: O3_3shots data_files: - split: test path: O3_3shots/shard* - config_name: O3_5shots data_files: - split: test path: O3_5shots/shard* - config_name: O3_box data_files: - split: test path: O3_box/shard* - config_name: O3_box_3shots data_files: - split: test path: O3_box_3shots/shard* - config_name: O3_box_5shots data_files: - split: test path: O3_box_5shots/shard* - config_name: O3_box_img data_files: - split: test path: O3_box_img/shard* - config_name: O3_box_img_3shots data_files: - split: test path: O3_box_img_3shots/shard* - config_name: O3_box_img_5shots data_files: - split: test path: O3_box_img_5shots/shard* - config_name: P3 data_files: - split: test path: P3/shard* - config_name: P3_3shots data_files: - split: test path: P3_3shots/shard* - config_name: P3_5shots data_files: - split: test path: P3_5shots/shard* - config_name: P3_box data_files: - split: test path: P3_box/shard* - config_name: P3_box_3shots data_files: - split: test path: P3_box_3shots/shard* - config_name: P3_box_5shots data_files: - split: test path: P3_box_5shots/shard* - config_name: P3_box_img data_files: - split: test path: P3_box_img/shard* - config_name: P3_box_img_3shots data_files: - split: test path: P3_box_img_3shots/shard* - config_name: P3_box_img_5shots data_files: - split: test path: P3_box_img_5shots/shard* --- # *SalBench: A Benchmark for Evaluating Perceptual Capabilities of Vision-Language Models* – *Ngoc Dung Huynh, Yasser Abdelaziz Dahou Djilali, Le Khac Phuc, Ankit Singh, Wamiq Para, Sanath Narayan* [[💻 Github](https://github.com/dunghuynhandy/SalBench)] [[📊 Leaderboard ](https://github.com/dunghuynhandy/SalBench)][[📖 ArXiv Paper](Comming Soon)] ## Introduction We present Saliency Benchmark (SalBench), a novel benchmark designed to assess the capability of Large Vision-Language Models (LVLM) in detecting visually salient features that are readily apparent to humans, such as a large circle amidst a grid of smaller ones. This benchmark focuses on low-level features including color, intensity, and orientation, which are fundamental to human visual processing. Our SalBench consists of images that highlight rare, unusual, or unexpected elements within scenes, and naturally draw human attention. It comprises three novel tasks for evaluating the perceptual capabilities of LVLM: Odd-One-Out Detection, Referring Odd-One-Out, and Visual Referring Odd-One-Out. We perform a comprehensive evaluation of state-of-the-art LVLM using SalBench and our findings reveal a surprising limitation: LVLM struggle to identify seemingly obvious visual anomalies, with even the advanced GPT-4o achieving only 47.6\% accuracy on such a simple task. SalBench will be an important step in measuring the capabilities of LVLM that align with the subtle definition of human attention. ### Key Tasks in SalBench #### 1. Salient Object Detection - **Objective**: Evaluate the model's ability to identify and segment the most visually important objects in an image. - **Description**: The model is tasked with distinguishing salient objects from the background, mimicking human attention. - **Significance**: Critical for applications like autonomous driving and medical imaging where detecting key objects is vital. #### 2. Visual Question Answering (VQA) on Salient Regions - **Objective**: Test the model's ability to answer questions that require attention to specific, salient regions of an image. - **Description**: The model must extract relevant information from highlighted regions to provide accurate answers. - **Significance**: Measures the integration of visual perception and language understanding. #### 3. Referring Expression Segmentation - **Objective**: Assess the model’s capacity to segment objects based on natural language descriptions. - **Description**: The model must accurately segment the object referred to by a user-provided textual phrase. - **Significance**: Important for human-computer interaction, allowing intuitive control through verbal instructions. ### Visualization <!-- ![Description of image](){width=500 height=300} --> <!-- <img src="./images/abstract_fig.png" alt="Example Image" width="400"> --> <div align="center"> <img src="./images/abstract_fig.png" alt="Description of image" width="800"> </div> ## Leaderboard #### + Exact Match and F1-Scores on the synthetic image set (**P3**) of SalBench. <table> <tr style="border-top: 2px solid black;"> <th rowspan="3">Model</th> <th rowspan="3" style="text-align: center; border-right: 1px solid black;">Shot</th> <th colspan="3" rowspan="2" style="text-align: center; border-right: 1px solid black;">Overall Matching</th> <th colspan="12" style="text-align: center;">F1 Score</th> </tr> <tr> <th colspan="3" style="text-align: center; border-right: 1px solid black;">Overall</th> <th colspan="3" style="text-align: center; border-right: 1px solid black;">Orientation</th> <th colspan="3" style="text-align: center; border-right: 1px solid black;">Color</th> <th colspan="3" style="text-align: center">Size</th> </tr> <tr> <th style="text-align: center;">D</th> <th style="text-align: center;">R</th> <th style="border-right: 1px solid black;">VR</th> <th style="text-align: center;">D</th> <th style="text-align: center;">R</th> <th style="border-right: 1px solid black;">VR</th> <th style="text-align: center;">D</th> <th style="text-align: center;">R</th> <th style="border-right: 1px solid black;">VR</th> <th style="text-align: center;">D</th> <th style="text-align: center;">R</th> <th style="border-right: 1px solid black;">VR</th> <th style="text-align: center;">D</th> <th style="text-align: center;">R</th> <th style="text-align: center; border-right: 1px solid black;">VR</th> </tr> <tr> <td>Claude-sonet</td> <td style="border-right: 1px solid black;">0</td> <td>86.4</td> <td>89.0</td> <td style="border-right: 1px solid black;">87.8</td> <td>86.7</td> <td>90.3</td> <td style="border-right: 1px solid black;">87.7</td> <td>83.4</td> <td>87.6</td> <td style="border-right: 1px solid black;">85.3</td> <td>94.6</td> <td>95.4</td> <td style="border-right: 1px solid black;">95.5</td> <td>82.0</td> <td>87.9</td> <td>82.2</td> </tr> <tr> <td>NVLM-D-72B</td> <td style="border-right: 1px solid black;">0</td> <td>83.4</td> <td >57.9</td> <td style="border-right: 1px solid black;">59.8</td> <td>83.2</td> <td>73.7</td> <td style="border-right: 1px solid black;">51.7</td> <td>77.4</td> <td>75.1</td> <td style="border-right: 1px solid black;">61.8</td> <td>98.0</td> <td >80.2</td> <td style="border-right: 1px solid black;">80.4</td> <td>74.1</td> <td>65.7</td> <td>12.7</td> </tr> <tr> <td>Molmo-7B</td> <td style="border-right: 1px solid black;">0</td> <td>71.3</td> <td>45.4</td> <td style="border-right: 1px solid black;">30.1</td> <td>67.2</td> <td>38.0</td> <td style="border-right: 1px solid black;">28.4</td> <td>40.8</td> <td>62.3</td> <td style="border-right: 1px solid black;">34.5</td> <td>95.3</td> <td>23.3</td> <td style="border-right: 1px solid black;">15.7</td> <td>69.3</td> <td>28.5</td> <td>22.3</td> </tr> <tr> <td>Molmo-72B</td> <td style="border-right: 1px solid black;">0</td> <td>84.1</td> <td>67.0</td> <td style="border-right: 1px solid black;">75.5</td> <td>83.4</td> <td>65.6</td> <td style="border-right: 1px solid black;">73.6</td> <td>80.7</td> <td>73.4</td> <td style="border-right: 1px solid black;">77.5</td> <td>96.5</td> <td>69.4</td> <td style="border-right: 1px solid black;">84.5</td> <td>72.9</td> <td>54.0</td> <td>58.5</td> </tr> <tr> <td>LLama3.2-Vision-11B</td> <td style="border-right: 1px solid black;">0</td> <td>51.4</td> <td>17.6</td> <td style="border-right: 1px solid black;">55.5</td> <td>48.7</td> <td>52.4</td> <td style="border-right: 1px solid black;">52.4</td> <td>52.6</td> <td>57.9</td> <td style="border-right: 1px solid black;">59.7</td> <td>62.7</td> <td>58.6</td> <td style="border-right: 1px solid black;">69.7</td> <td>30.9</td> <td>40.7</td> <td>27.8</td> </tr> <tr> <td>PaliGemma-3B-448</td> <td style="border-right: 1px solid black;">0</td> <td>39.7</td> <td>7.1</td> <td style="border-right: 1px solid black;">2.4</td> <td>41.4</td> <td>9.5</td> <td style="border-right: 1px solid black;">4.8</td> <td>0.9</td> <td>4.9</td> <td style="border-right: 1px solid black;">0.0</td> <td>67.0</td> <td>21.5</td> <td style="border-right: 1px solid black;">2.8</td> <td>55.1</td> <td>2.0</td> <td>11.7</td> </tr> <tr> <td rowspan="3">Phi3-4B</td> <td style="border-right: 1px solid black;">0</td> <td>51.3</td> <td>59.0</td> <td style="border-right: 1px solid black;">52.1</td> <td>41.2</td> <td>55.3</td> <td style="border-right: 1px solid black;">47.2</td> <td>12.4</td> <td>66.3</td> <td style="border-right: 1px solid black;">45.9</td> <td>45.3</td> <td>50.5</td> <td style="border-right: 1px solid black;">62.8</td> <td>65.9</td> <td>49.1</td> <td>32.9</td> </tr> <tr> <td style="border-right: 1px solid black;">3</td> <td>43.4</td> <td>39.0</td> <td style="border-right: 1px solid black;">47.1</td> <td>33.5</td> <td>27.1</td> <td style="border-right: 1px solid black;">38.6</td> <td>24.0</td> <td>17.3</td> <td style="border-right: 1px solid black;">5.8</td> <td>26.5</td> <td>54.9</td> <td style="border-right: 1px solid black;">55.0</td> <td>50.0</td> <td>9.1</td> <td>55.0</td> </tr> <tr> <td style="border-right: 1px solid black;">5</td> <td>34.2</td> <td>35.1</td> <td style="border-right: 1px solid black;">50.8</td> <td>17.0</td> <td>18.9</td> <td style="border-right: 1px solid black;">46.7</td> <td>0.0</td> <td>4.7</td> <td style="border-right: 1px solid black;">34.5</td> <td>51.0</td> <td>51.6</td> <td style="border-right: 1px solid black;">66.6</td> <td>0.0</td> <td>0.4</td> <td>39.1</td> </tr> <tr> <td rowspan="3">Phi3.5-Vision-3.5B</td> <td style="border-right: 1px solid black;">0</td> <td>44.0</td> <td>59.9</td> <td style="border-right: 1px solid black;">64.9</td> <td>35.0</td> <td>53.7</td> <td style="border-right: 1px solid black;">63.6</td> <td>2.1</td> <td>53.7</td> <td style="border-right: 1px solid black;">53.7</td> <td>49.2</td> <td>50.9</td> <td style="border-right: 1px solid black;">71.3</td> <td>53.7</td> <td>56.6</td> <td>65.9</td> </tr> <tr> <td style="border-right: 1px solid black;">3</td> <td>26.7</td> <td>49.8</td> <td style="border-right: 1px solid black;">34.7</td> <td>19.5</td> <td>41.0</td> <td style="border-right: 1px solid black;">20.8</td> <td>0.0</td> <td>0.5</td> <td style="border-right: 1px solid black;">3.0</td> <td>18.2</td> <td>66.7</td> <td style="border-right: 1px solid black;"`>9.9</td> <td>40.3</td> <td>55.8</td> <td>49.5</td> </tr> <tr> <td style="border-right: 1px solid black;">5</td> <td>35.2</td> <td>24.1</td> <td style="border-right: 1px solid black;">33.8</td> <td>29.3</td> <td>11.1</td> <td style="border-right: 1px solid black;">19.0</td> <td>1.5</td> <td>0.2</td> <td style="border-right: 1px solid black;">0.0</td> <td>38.9</td> <td>26.0</td> <td style="border-right: 1px solid black;">7.6</td> <td>47.5</td> <td>7.1</td> <td>49.4</td> </tr> <tr> <td rowspan="3">LLava 1.6-7B</td> <td style="border-right: 1px solid black;">0</td> <td>31.2</td> <td>18.2</td> <td style="border-right: 1px solid black;">17.7</td> <td>16.3</td> <td>10.1</td> <td style="border-right: 1px solid black;">16.6</td> <td>0.0</td> <td>0.0</td> <td style="border-right: 1px solid black;">0.0</td> <td>0.1</td> <td>12.3</td> <td style="border-right: 1px solid black;">49.9</td> <td>48.9</td> <td>18.1</td> <td>0.0</td> </tr> <tr> <td style="border-right: 1px solid black;">3</td> <td>32.4</td> <td>17.7</td> <td style="border-right: 1px solid black;">34.2</td> <td>16.4</td> <td>8.8</td> <td style="border-right: 1px solid black;">17.0</td> <td>0.0</td> <td>1.4</td> <td style="border-right: 1px solid black;">0.0</td> <td>0.0</td> <td>10.1</td> <td style="border-right: 1px solid black;">50.9</td> <td>49.0</td> <td>15.1</td> <td>0.0</td> </tr> <tr> <td style="border-right: 1px solid black;">5</td> <td>32.4</td> <td>19.9</td> <td style="border-right: 1px solid black;">34.2</td> <td>16.4</td> <td>9.1</td> <td style="border-right: 1px solid black;">17.0</td> <td>0.0</td> <td>0.2</td> <td style="border-right: 1px solid black;">0.0</td> <td>0.0</td> <td>18.1</td> <td style="border-right: 1px solid black;">50.9</td> <td>49.0</td> <td>9.1</td> <td>0.0</td> </tr> <tr> <td rowspan="3">Idefic2-8B</td> <td style="border-right: 1px solid black;">0</td> <td>64.5</td> <td>45.2</td> <td style="border-right: 1px solid black;">56.0</td> <td>64.3</td> <td>36.6</td> <td style="border-right: 1px solid black;">49.5</td> <td>62.9</td> <td>51.1</td> <td style="border-right: 1px solid black;">63.8</td> <td>78.1</td> <td>9.7</td> <td style="border-right: 1px solid black;">64.1</td> <td>51.9</td> <td>49.2</td> <td>20.5</td> </tr> <tr> <td style="border-right: 1px solid black;">3</td> <td>66.9</td> <td>42.6</td> <td style="border-right: 1px solid black;">48.7</td> <td>66.3</td> <td>34.2</td> <td style="border-right: 1px solid black;">39.5</td> <td>66.6</td> <td>9.7</td> <td style="border-right: 1px solid black;">66.3</td> <td>79.4</td> <td>39.8</td> <td style="border-right: 1px solid black;">9.5</td> <td>53.0</td> <td>53.1</td> <td>9.7</td> </tr> <tr> <td style="border-right: 1px solid black;">5</td> <td>66.7</td> <td>49.6</td> <td style="border-right: 1px solid black;">43.1</td> <td>67.2</td> <td>42.6</td> <td style="border-right: 1px solid black;">34.5</td> <td>65.3</td> <td>8.6</td> <td style="border-right: 1px solid black;">54.5</td> <td>79.2</td> <td>62.9</td> <td style="border-right: 1px solid black;">11.9</td> <td>57.2</td> <td>56.3</td> <td>37.0</td> </tr> <tr> <td rowspan="3">Idefic3-8B</td> <td style="border-right: 1px solid black;">0</td> <td>40.2</td> <td>58.3</td> <td style="border-right: 1px solid black;">35.5</td> <td>28.4</td> <td>52.8</td> <td style="border-right: 1px solid black;">19.2</td> <td>24.1</td> <td>54.9</td> <td style="border-right: 1px solid black;">2.3</td> <td>54.3</td> <td>51.0</td> <td style="border-right: 1px solid black;">49.7</td> <td>6.9</td> <td>52.5</td> <td>5.5</td> </tr> <tr> <td style="border-right: 1px solid black;">3</td> <td>50.9</td> <td>35.9</td> <td style="border-right: 1px solid black;">50.7</td> <td>40.3</td> <td>20.7</td> <td style="border-right: 1px solid black;">40.6</td> <td>0.5</td> <td>0.5</td> <td style="border-right: 1px solid black;">3.4</td> <td>62.9</td> <td>52.6</td> <td style="border-right: 1px solid black;">63.6</td> <td>57.6</td> <td>8.9</td> <td>54.8</td> </tr> <tr> <td style="border-right: 1px solid black;">5</td> <td>36.3</td> <td>34.5</td> <td style="border-right: 1px solid black;">62.9</td> <td>21.4</td> <td>18.1</td> <td style="border-right: 1px solid black;">58.3</td> <td>0.0</td> <td>0.2</td> <td style="border-right: 1px solid black;">64.3</td> <td>51.8</td> <td>51.3</td> <td style="border-right: 1px solid black;">85.7</td> <td>12.3</td> <td>2.7</td> <td>25.0</td> </tr> <tr> <td rowspan="3">VILA-1.5-8B</td> <td style="border-right: 1px solid black;">0</td> <td>34.2</td> <td>30.4</td> <td style="border-right: 1px solid black;">47.5</td> <td>40.0</td> <td>15.8</td> <td style="border-right: 1px solid black;">17.0</td> <td>17.6</td> <td>0.0</td> <td style="border-right: 1px solid black;">0.5</td> <td>56.3</td> <td >28.8</td> <td style="border-right: 1px solid black;">50.5</td> <td>46.1</td> <td>18.7</td> <td>0.0</td> </tr> <tr> <td style="border-right: 1px solid black;">3</td> <td>34.2</td> <td>36.9</td> <td style="border-right: 1px solid black;">34.2</td> <td>17.0</td> <td>28.8</td> <td style="border-right: 1px solid black;">17.0</td> <td>0.0</td> <td>0.0</td> <td style="border-right: 1px solid black;">0.5</td> <td>51.0</td> <td>47.6</td> <td style="border-right: 1px solid black;">50.5</td> <td>0.0</td> <td>38.5</td> <td>0.0</td> </tr> <tr> <td style="border-right: 1px solid black;">5</td> <td>34.2</td> <td>39.5</td> <td style="border-right: 1px solid black;">34.2</td> <td>17.0</td> <td>30.8</td> <td style="border-right: 1px solid black;">17.0</td> <td>0.0</td> <td>0.0</td> <td style="border-right: 1px solid black;">0.5</td> <td>51.0</td> <td>51.3</td> <td style="border-right: 1px solid black;">50.5</td> <td>0.0</td> <td>41.3</td> <td>0.0</td> </tr> <tr> <td rowspan="3">Qwen2-VL-2B</td> <td style="border-right: 1px solid black;">0</td> <td>30.3</td> <td>34.5</td> <td style="border-right: 1px solid black;">34.5</td> <td>26.3</td> <td>20.6</td> <td style="border-right: 1px solid black;">20.2</td> <td>14.5</td> <td>5.0</td> <td style="border-right: 1px solid black;">10.7</td> <td>5.9</td> <td>7.0</td> <td style="border-right: 1px solid black;">1.6</td> <td>58.3</td> <td>49.8</td> <td>49.6</td> </tr> <tr> <td style="border-right: 1px solid black;">3</td> <td>35.7</td> <td>35.3</td> <td style="border-right: 1px solid black;">32.4</td> <td>23.3</td> <td>21.8</td> <td style="border-right: 1px solid black;">16.3</td> <td>0.0</td> <td>0.0</td> <td style="border-right: 1px solid black;">0.0</td> <td>17.5</td> <td>15.2</td> <td style="border-right: 1px solid black;">0.0</td> <td>53.8</td> <td>50.1</td> <td>49.0</td> </tr> <tr> <td style="border-right: 1px solid black;">5</td> <td>35.3</td> <td>32.6</td> <td style="border-right: 1px solid black;">33.1</td> <td>23.8</td> <td>16.5</td> <td style="border-right: 1px solid black;">17.7</td> <td>0.0</td> <td>0.0</td> <td style="border-right: 1px solid black;">4.1</td> <td>15.2</td> <td>0.7</td> <td style="border-right: 1px solid black;">0.0</td> <td>54.6</td> <td>49.0</td> <td>49.3</td> </tr> <tr> <td rowspan="3">Qwen2-VL-7B</td> <td style="border-right: 1px solid black;">0</td> <td>60.2</td> <td>40.0</td> <td style="border-right: 1px solid black;">59.9</td> <td>55.7</td> <td>34.2</td> <td style="border-right: 1px solid black;">57.4</td> <td>23.7</td> <td>17.7</td> <td style="border-right: 1px solid black;">53.6</td> <td>82.0</td> <td>45.0</td> <td style="border-right: 1px solid black;">66.9</td> <td>61.6</td> <td>40.3</td> <td>51.5</td> </tr> <tr> <td style="border-right: 1px solid black;">3</td> <td>63.7</td> <td>34.2</td> <td style="border-right: 1px solid black;">69.8</td> <td>53.8</td> <td>17.0</td> <td style="border-right: 1px solid black;">64.2</td> <td>2.5</td> <td>0.0</td> <td style="border-right: 1px solid black;">33.5</td> <td>94.8</td> <td>50.9</td> <td style="border-right: 1px solid black;">84.9</td> <td>64.1</td> <td>0.0</td> <td>74.0</td> </tr> <tr> <td style="border-right: 1px solid black;">5</td> <td>64.5</td> <td>34.2</td> <td style="border-right: 1px solid black;">73.4</td> <td>54.9</td> <td>17.7</td> <td style="border-right: 1px solid black;">72.0</td> <td>4.5</td> <td>0.0</td> <td style="border-right: 1px solid black;">56.3</td> <td>95.6</td> <td>50.9</td> <td style="border-right: 1px solid black;">84.1</td> <td>64.6</td> <td>2.0</td> <td>75.5</td> </tr> <tr> <td rowspan="3">Qwen2-VL-72B</td> <td style="border-right: 1px solid black;">0</td> <td>89.1</td> <td>93.6</td> <td style="border-right: 1px solid black;">76.0</td> <td>88.8</td> <td>93.6</td> <td style="border-right: 1px solid black;">74.7</td> <td>85.2</td> <td>91.3</td> <td style="border-right: 1px solid black;">72.5</td> <td>97.2</td> <td>98.3</td> <td style="border-right: 1px solid black;">86.0</td> <td>83.9</td> <td>91.1</td> <td>65.7</td> </tr> <tr> <td style="border-right: 1px solid black;">3</td> <td>89.3</td> <td>93.1</td> <td style="border-right: 1px solid black;">86.1</td> <td>89.3</td> <td>93.1</td> <td style="border-right: 1px solid black;">85.9</td> <td>86.7</td> <td>90.4</td> <td style="border-right: 1px solid black;">82.9</td> <td>95.8</td> <td>97.9</td> <td style="border-right: 1px solid black;">96.2</td> <td>85.5</td> <td>91.1</td> <td>78.8</td> </tr> <tr> <td style="border-right: 1px solid black;">5</td> <td>89.2</td> <td>92.7</td> <td style="border-right: 1px solid black;">88.0</td> <td>89.9</td> <td>92.6</td> <td style="border-right: 1px solid black;">87.9</td> <td>88.3</td> <td>90.0</td> <td style="border-right: 1px solid black;">84.8</td> <td>96.1</td> <td>97.4</td> <td style="border-right: 1px solid black;">96.5</td> <td>85.4</td> <td>90.5</td> <td>82.3</td> </tr> <tr> <td rowspan="3">InternVL-4B</td> <td style="border-right: 1px solid black;">0</td> <td>47.2</td> <td>69.5</td> <td style="border-right: 1px solid black;">58.9</td> <td>41.5</td> <td>63.4</td> <td style="border-right: 1px solid black;">52.2</td> <td>25.4</td> <td>31.2</td> <td style="border-right: 1px solid black;">67.2</td> <td>64.5</td> <td>88.2</td> <td style="border-right: 1px solid black;">67.1</td> <td>34.7</td> <td>70.6</td> <td>22.4</td> </tr> <tr> <td style="border-right: 1px solid black;">3</td> <td>34.2</td> <td>37.3</td> <td style="border-right: 1px solid black;">49.9</td> <td>17.0</td> <td>25.3</td> <td style="border-right: 1px solid black;">41.7</td> <td>0.0</td> <td>0.0</td> <td style="border-right: 1px solid black;">2.3</td> <td>50.9</td> <td>24.9</td> <td style="border-right: 1px solid black;">66.5</td> <td>0.0</td> <td>50.9</td> <td>56.5</td> </tr> <tr> <td style="border-right: 1px solid black;">5</td> <td>34.2</td> <td>48.0</td> <td style="border-right: 1px solid black;">58.1</td> <td>17.0</td> <td>39.1</td> <td style="border-right: 1px solid black;">52.5</td> <td>0.0</td> <td>0.0</td> <td style="border-right: 1px solid black;">61.7</td> <td>50.9</td> <td>61.4</td> <td style="border-right: 1px solid black;">76.5</td> <td>0.0</td> <td>55.9</td> <td>19.5</td> </tr> <tr> <td rowspan="3">InternVL-8B</td> <td style="border-right: 1px solid black;">0</td> <td>65.6</td> <td>74.2</td> <td style="border-right: 1px solid black;"`>37.0</td> <td>58.7</td> <td>71.9</td> <td style="border-right: 1px solid black;">23.0</td> <td>66.9</td> <td>50.4</td> <td style="border-right: 1px solid black;">9.9</td> <td>95.8</td> <td>93.7</td> <td style="border-right: 1px solid black;">52.0</td> <td>13.4</td> <td>71.5</td> <td>7.1</td> </tr> <tr> <td style="border-right: 1px solid black;">3</td> <td>60.6</td> <td>61.7</td> <td style="border-right: 1px solid black;">66.9</td> <td>52.3</td> <td>51.7</td> <td style="border-right: 1px solid black;">64.4</td> <td>7.4</td> <td>1.6</td> <td style="border-right: 1px solid black;">44.5</td> <td>87.0</td> <td>90.9</td> <td style="border-right: 1px solid black;">85.7</td> <td>62.6</td> <td>62.4</td> <td>63.0</td> </tr> <tr> <td style="border-right: 1px solid black;">5</td> <td>51.0</td> <td>62.5</td> <td style="border-right: 1px solid black;">61.6</td> <td>43.9</td> <td>53.7</td> <td style="border-right: 1px solid black;">50.5</td> <td>15.6</td> <td>8.6</td> <td style="border-right: 1px solid black;">66.5</td> <td>60.4</td> <td>89.2</td> <td style="border-right: 1px solid black;">83.6</td> <td>55.6</td> <td>63.3</td> <td>1.4</td> </tr> <tr> <td rowspan="3">GPT-4o</td> <td style="border-right: 1px solid black;">0</td> <td>89.2</td> <td >88.7</td> <td style="border-right: 1px solid black;">74.7</td> <td>89.2</td> <td>88.4</td> <td style="border-right: 1px solid black;">73.5</td> <td>86.3</td> <td>85.2</td> <td style="border-right: 1px solid black;">73.9</td> <td>97.2</td> <td>96.7</td> <td style="border-right: 1px solid black;">94.6</td> <td>84.0</td> <td>83.5</td> <td>52.0</td> </tr> <tr> <td style="border-right: 1px solid black;">3</td> <td>87.7</td> <td>88.0</td> <td style="border-right: 1px solid black;">86.3</td> <td>88.4</td> <td>87.7</td> <td style="border-right: 1px solid black;">86.7</td> <td>85.8</td> <td>84.7</td> <td style="border-right: 1px solid black;">82.8</td> <td>97.3</td> <td>95.6</td> <td style="border-right: 1px solid black;">95.8</td> <td>82.8</td> <td>82.7</td> <td>81.4</td> </tr> <tr style="border-bottom: 2px solid black;"> <td style="border-right: 1px solid black;">5</td> <td>86.0</td> <td>89.0</td> <td style="border-right: 1px solid black;">87.1</td> <td>86.0</td> <td>89.1</td> <td style="border-right: 1px solid black;">87.4</td> <td>82.8</td> <td>85.3</td> <td style="border-right: 1px solid black;">84.4</td> <td>97.6</td> <td>97.9</td> <td style="border-right: 1px solid black;">95.7</td> <td>77.5</td> <td>84.1</td> <td>82.0</td> </tr> </table> #### + Exact Match and F1-Scores on the Realworld image set (**O3**) of SalBench. <table> <tr style="border-top: 2px solid black;"> <th rowspan="3" >Model</th> <th rowspan="3" style="text-align: center; border-right: 1px solid black;">Shot</th> <th colspan="3" rowspan="2" style="text-align: center; border-right: 1px solid black;">Overall Matching</th> <th colspan="24" style="text-align: center;">F1 Score</th> </tr> <tr> <th colspan="3" style="text-align: center; border-right: 1px solid black;">Overall</th> <th colspan="3" style="text-align: center; border-right: 1px solid black;">Orientation</th> <th colspan="3" style="text-align: center; border-right: 1px solid black;">Color</th> <th colspan="3" style="text-align: center; border-right: 1px solid black;">Size</th> <th colspan="3" style="text-align: center; border-right: 1px solid black;">Focus</th> <th colspan="3" style="text-align: center; border-right: 1px solid black;">Shape</th> <th colspan="3" style="text-align: center; border-right: 1px solid black;">Location</th> <th colspan="3" style="text-align: center;">Pattern</th> </tr> <tr> <th style="text-align: center">D</th> <th style="text-align: center;">R</th> <th style="text-align: center; border-right: 1px solid black;">VR</th> <th style="text-align: center;">D</th> <th style="text-align: center;">R</th> <th style="text-align: center; border-right: 1px solid black;">VR</th> <th style="text-align: center;">D</th> <th style="text-align: center;">R</th> <th style="text-align: center; border-right: 1px solid black;">VR</th> <th style="text-align: center;">D</th> <th style="text-align: center;">R</th> <th style="text-align: center; border-right: 1px solid black;">VR</th> <th style="text-align: center;">D</th> <th style="text-align: center;">R</th> <th style="text-align: center; border-right: 1px solid black;">VR</th> <th style="text-align: center;">D</th> <th style="text-align: center;">R</th> <th style="text-align: center; border-right: 1px solid black;">VR</th> <th style="text-align: center;">D</th> <th style="text-align: center;">R</th> <th style="text-align: center; border-right: 1px solid black;">VR</th> <th style="text-align: center;">D</th> <th style="text-align: center;">R</th> <th style="text-align: center; border-right: 1px solid black;">VR</th> <th style="text-align: center;">D</th> <th style="text-align: center;">R</th> <th style="text-align: center;">VR</th> </tr> <tr> <td>Claude</td> <td style="border-right: 1px solid black;">0</td> <td>40.6</td> <td>42.7</td> <td style="border-right: 1px solid black;">40.3</td> <td>48.2</td> <td>51.1</td> <td style="border-right: 1px solid black;">53.9</td> <td>40.0</td> <td>43.9</td> <td style="border-right: 1px solid black;">49.2</td> <td>95.2</td> <td>95.9</td> <td style="border-right: 1px solid black;">95.8</td> <td>40.7</td> <td>47.7</td> <td style="border-right: 1px solid black;">44.1</td> <td>27.6</td> <td>14.9</td> <td style="border-right: 1px solid black;">21.0</td> <td>51.6</td> <td>59.3</td> <td style="border-right: 1px solid black;">60.4</td> <td>28.7</td> <td>34.0</td> <td style="border-right: 1px solid black;">41.7</td> <td>53.3</td> <td>62.2</td> <td>64.9</td> </tr> <tr> <td>NVLM-D-72B</td> <td style="border-right: 1px solid black;">0</td> <td>26.7</td> <td>35.6</td> <td style="border-right: 1px solid black;">21.6</td> <td>36.5</td> <td>42.1</td> <td style="border-right: 1px solid black;">37.3</td> <td>36.6</td> <td>35.1</td> <td style="border-right: 1px solid black;">28.4</td> <td>90.9</td> <td>93.2</td> <td style="border-right: 1px solid black;">89.4</td> <td>28.6</td> <td>36.0</td> <td style="border-right: 1px solid black;">34.1</td> <td>8.3</td> <td>16.1</td> <td style="border-right: 1px solid black;">12.3</td> <td>41.4</td> <td>49.0</td> <td style="border-right: 1px solid black;">42.5</td> <td>14.7</td> <td>18.4</td> <td style="border-right: 1px solid black;">8.3</td> <td>34.8</td> <td>47.1</td> <td>45.9</td> </tr> <tr> <td>Molmo-72B</td> <td style="border-right: 1px solid black;">0</td> <td>19.2</td> <td>18.6</td> <td style="border-right: 1px solid black;">15.6</td> <td>40.6</td> <td>41.2</td> <td style="border-right: 1px solid black;">36.7</td> <td>27.6</td> <td>30.6</td> <td style="border-right: 1px solid black;">24.1</td> <td>94.0</td> <td>91.8</td> <td style="border-right: 1px solid black;">90.2</td> <td>35.3</td> <td>32.2</td> <td style="border-right: 1px solid black;">30.1</td> <td>17.0</td> <td>14.2</td> <td style="border-right: 1px solid black;">12.2</td> <td>44.5</td> <td>41.8</td> <td style="border-right: 1px solid black;">39.2</td> <td>12.5</td> <td>18.3</td> <td style="border-right: 1px solid black;">11.9</td> <td>53.2</td> <td>59.6</td> <td>51.1</td> </tr> <tr> <td>Molmo-7B</td> <td style="border-right: 1px solid black;">0</td> <td>2.5</td> <td>8.9</td> <td style="border-right: 1px solid black;">14.6</td> <td>32.0</td> <td>32.4</td> <td style="border-right: 1px solid black;">33.0</td> <td>15.2</td> <td>18.6</td> <td style="border-right: 1px solid black;">24.2</td> <td>88.5</td> <td>80.1</td> <td style="border-right: 1px solid black;">88.2</td> <td>34.8</td> <td>38.8</td> <td style="border-right: 1px solid black;">32.7</td> <td>13.5</td> <td>13.7</td> <td style="border-right: 1px solid black;">10.8</td> <td>33.2</td> <td>40.1</td> <td style="border-right: 1px solid black;">41.0</td> <td>10.0</td> <td>8.0</td> <td style="border-right: 1px solid black;">7.7</td> <td>28.8</td> <td>27.0</td> <td>29.9</td> </tr> <tr> <td>Llama3.2-Vision-11B</td> <td style="border-right: 1px solid black;">0</td> <td>2.8</td> <td>0.0</td> <td style="border-right: 1px solid black;">0.0</td> <td>32.1</td> <td>29.1</td> <td style="border-right: 1px solid black;">29.7</td> <td>17.7</td> <td>17.1</td> <td style="border-right: 1px solid black;">27.1</td> <td>90.6</td> <td>89.3</td> <td style="border-right: 1px solid black;">85.6</td> <td>31.1</td> <td>33.4</td> <td style="border-right: 1px solid black;">18.1</td> <td>12.7</td> <td>11.5</td> <td style="border-right: 1px solid black;">9.3</td> <td>37.5</td> <td>44.6</td> <td style="border-right: 1px solid black;">45.5</td> <td>8.4</td> <td>8.1</td> <td style="border-right: 1px solid black;">22.5</td> <td>20.6</td> <td>0.0</td> <td>0.0</td> </tr> <tr> <td>PaliGemma-3B-448</td> <td style="border-right: 1px solid black;">0</td> <td>1.4</td> <td>1.0</td> <td style="border-right: 1px solid black;">0.7</td> <td>27.6</td> <td>1.2</td> <td style="border-right: 1px solid black;">2.3</td> <td>16.5</td> <td>8.1</td> <td style="border-right: 1px solid black;">13.6</td> <td>84.3</td> <td>0.7</td> <td style="border-right: 1px solid black;">1.6</td> <td>27.2</td> <td>0.0</td> <td style="border-right: 1px solid black;">0.0</td> <td>11.6</td> <td>0.0</td> <td style="border-right: 1px solid black;">0.0</td> <td>32.5</td> <td>0.0</td> <td style="border-right: 1px solid black;">0.0</td> <td>10.4</td> <td>0.0</td> <td style="border-right: 1px solid black;">0.0</td> <td>13.4</td> <td>0.0</td> <td>0.0</td> </tr> <tr> <td rowspan="3">Phi3-4B</td> <td style="border-right: 1px solid black;">0</td> <td>7.0</td> <td>4.5</td> <td style="border-right: 1px solid black;">6.4</td> <td>32.1</td> <td>32.8</td> <td style="border-right: 1px solid black;">32.8</td> <td>2.1</td> <td>2.1</td> <td style="border-right: 1px solid black;">1.9</td> <td>91.1</td> <td>87.5</td> <td style="border-right: 1px solid black;">88.2</td> <td>25.2</td> <td>29.3</td> <td style="border-right: 1px solid black;">26.3</td> <td>13.5</td> <td>11.3</td> <td style="border-right: 1px solid black;">14.3</td> <td>40.2</td> <td>42.1</td> <td style="border-right: 1px solid black;">41.1</td> <td>7.5</td> <td>7.8</td> <td style="border-right: 1px solid black;">7.4</td> <td>45.2</td> <td>43.9</td> <td>49.6</td> </tr> <tr> <td style="border-right: 1px solid black;">3</td> <td>0.0</td> <td>1.7</td> <td style="border-right: 1px solid black;">3.6</td> <td>34.1</td> <td>32.0</td> <td style="border-right: 1px solid black;">32.1</td> <td>15.5</td> <td>14.9</td> <td style="border-right: 1px solid black;">12.0</td> <td>89.6</td> <td>88.7</td> <td style="border-right: 1px solid black;">88.1</td> <td>30.6</td> <td>29.2</td> <td style="border-right: 1px solid black;">23.5</td> <td>9.4</td> <td>10.8</td> <td style="border-right: 1px solid black;">11.1</td> <td>40.3</td> <td>38.9</td> <td style="border-right: 1px solid black;">39.8</td> <td>7.0</td> <td>7.3</td> <td style="border-right: 1px solid black;">8.3</td> <td>46.5</td> <td>34.8</td> <td>42.2</td> </tr> <tr> <td style="border-right: 1px solid black;">5</td> <td>0.0</td> <td>1.2</td> <td style="border-right: 1px solid black;">1.3</td> <td>31.1</td> <td>32.1</td> <td style="border-right: 1px solid black;">32.2</td> <td>16.6</td> <td>14.3</td> <td style="border-right: 1px solid black;">12.7</td> <td>78.7</td> <td>88.9</td> <td style="border-right: 1px solid black;">89.1</td> <td>28.9</td> <td>31.2</td> <td style="border-right: 1px solid black;">28.7</td> <td>8.8</td> <td>10.8</td> <td style="border-right: 1px solid black;">7.1</td> <td>38.3</td> <td>32.1</td> <td style="border-right: 1px solid black;">40.7</td> <td>6.6</td> <td>7.8</td> <td style="border-right: 1px solid black;">7.7</td> <td>41.3</td> <td>39.1</td> <td>39.8</td> </tr> <tr> <td rowspan="3">Phi3.5-Vision-3.5B</td> <td style="border-right: 1px solid black;">0</td> <td>12.6</td> <td>2.3</td> <td style="border-right: 1px solid black;">7.3</td> <td>23.2</td> <td>27.5</td> <td style="border-right: 1px solid black;">27.5</td> <td>1.1</td> <td>22.1</td> <td style="border-right: 1px solid black;">12.7</td> <td>91.1</td> <td>86.2</td> <td style="border-right: 1px solid black;">88.6</td> <td>29.9</td> <td>22.7</td> <td style="border-right: 1px solid black;">22.6</td> <td>4.8</td> <td>11.8</td> <td style="border-right: 1px solid black;">9.8</td> <td>9.4</td> <td>37.2</td> <td style="border-right: 1px solid black;">39.1</td> <td>1.4</td> <td>7.9</td> <td style="border-right: 1px solid black;">7.2</td> <td>24.4</td> <td>4.4</td> <td>27.2</td> </tr> <tr> <td style="border-right: 1px solid black;">3</td> <td>0.1</td> <td>3.4</td> <td style="border-right: 1px solid black;">9.2</td> <td>23.3</td> <td>28.8</td> <td style="border-right: 1px solid black;">28.8</td> <td>16.0</td> <td>15.6</td> <td style="border-right: 1px solid black;">13.5</td> <td>58.8</td> <td>89.6</td> <td style="border-right: 1px solid black;">90.4</td> <td>26.5</td> <td>24.7</td> <td style="border-right: 1px solid black;">25.5</td> <td>9.8</td> <td>9.7</td> <td style="border-right: 1px solid black;">11.5</td> <td>31.9</td> <td>38.9</td> <td style="border-right: 1px solid black;">39.2</td> <td>6.9</td> <td>7.2</td> <td style="border-right: 1px solid black;">7.4</td> <td>12.9</td> <td>15.8</td> <td>28.7</td> </tr> <tr> <td style="border-right: 1px solid black;">5</td> <td>0.5</td> <td>0.4</td> <td style="border-right: 1px solid black;">10.3</td> <td>25.2</td> <td>30.8</td> <td style="border-right: 1px solid black;">30.8</td> <td>15.2</td> <td>15.6</td> <td style="border-right: 1px solid black;">8.7</td> <td>52.5</td> <td>90.2</td> <td style="border-right: 1px solid black;">88.5</td> <td>28.5</td> <td>31.5</td> <td style="border-right: 1px solid black;">21.2</td> <td>8.9</td> <td>8.8</td> <td style="border-right: 1px solid black;">8.3</td> <td>34.1</td> <td>41.1</td> <td style="border-right: 1px solid black;">40.9</td> <td>7.3</td> <td>7.8</td> <td style="border-right: 1px solid black;">7.0</td> <td>29.6</td> <td>21.3</td> <td>40.5</td> </tr> <tr> <td rowspan="3">LLava 1.6-7B</td> <td style="border-right: 1px solid black;">0</td> <td>11.1</td> <td>20.4</td> <td style="border-right: 1px solid black;">22.8</td> <td>24.6</td> <td>21.4</td> <td style="border-right: 1px solid black;">20.8</td> <td>13.4</td> <td>3.3</td> <td style="border-right: 1px solid black;">1.1</td> <td>91.1</td> <td>72.4</td> <td style="border-right: 1px solid black;">71.9</td> <td>19.3</td> <td>23.4</td> <td style="border-right: 1px solid black;">22.8</td> <td>10.9</td> <td>8.5</td> <td style="border-right: 1px solid black;">10.7</td> <td>15.8</td> <td>28.6</td> <td style="border-right: 1px solid black;">22.9</td> <td>8.9</td> <td>4.5</td> <td style="border-right: 1px solid black;">3.6</td> <td>12.6</td> <td>9.1</td> <td>12.4</td> </tr> <tr> <td style="border-right: 1px solid black;">3</td> <td>0.0</td> <td>0.1</td> <td style="border-right: 1px solid black;">0.2</td> <td>7.1</td> <td>15.2</td> <td style="border-right: 1px solid black;">17.8</td> <td>3.6</td> <td>1.1</td> <td style="border-right: 1px solid black;">5.2</td> <td>10.4</td> <td>15.2</td> <td style="border-right: 1px solid black;">29.3</td> <td>12.2</td> <td>21.5</td> <td style="border-right: 1px solid black;">20.8</td> <td>4.3</td> <td>10.3</td> <td style="border-right: 1px solid black;">9.1</td> <td>9.5</td> <td>30.7</td> <td style="border-right: 1px solid black;">32.7</td> <td>5.4</td> <td>8.4</td> <td style="border-right: 1px solid black;">5.5</td> <td>5.4</td> <td>19.4</td> <td>21.9</td> </tr> <tr> <td style="border-right: 1px solid black;">5</td> <td>0.6</td> <td>0.0</td> <td style="border-right: 1px solid black;">0.0</td> <td>11.4</td> <td>10.9</td> <td style="border-right: 1px solid black;">9.7</td> <td>0.0</td> <td>0.0</td> <td style="border-right: 1px solid black;">0.0</td> <td>24.1</td> <td>4.3</td> <td style="border-right: 1px solid black;">0.7</td> <td>21.5</td> <td>22.3</td> <td style="border-right: 1px solid black;">20.1</td> <td>5.5</td> <td>7.1</td> <td style="border-right: 1px solid black;">7.2</td> <td>17.4</td> <td>30.2</td> <td style="border-right: 1px solid black;">27.9</td> <td>5.6</td> <td>7.7</td> <td style="border-right: 1px solid black;">5.9</td> <td>5.6</td> <td>6.5</td> <td>5.8</td> </tr> <tr> <td rowspan="3">Idefics2-8B</td> <td style="border-right: 1px solid black;">0</td> <td>37.1</td> <td>5.5</td> <td style="border-right: 1px solid black;">4.2</td> <td>19.5</td> <td>29.6</td> <td style="border-right: 1px solid black;">33.8</td> <td>7.6</td> <td>15.6</td> <td style="border-right: 1px solid black;">11.9</td> <td>91.9</td> <td>72.5</td> <td style="border-right: 1px solid black;">85.3</td> <td>19.6</td> <td>30.0</td> <td style="border-right: 1px solid black;">32.8</td> <td>0.4</td> <td>11.6</td> <td style="border-right: 1px solid black;">16.0</td> <td>9.6</td> <td>46.2</td> <td style="border-right: 1px solid black;">44.7</td> <td>5.4</td> <td>7.5</td> <td style="border-right: 1px solid black;">7.5</td> <td>4.3</td> <td>23.5</td> <td>38.3</td> </tr> <tr> <td style="border-right: 1px solid black;">3</td> <td>8.4</td> <td>24.3</td> <td style="border-right: 1px solid black;">8.7</td> <td>21.1</td> <td>28.4</td> <td style="border-right: 1px solid black;">31.1</td> <td>13.0</td> <td>8.3</td> <td style="border-right: 1px solid black;">11.5</td> <td>62.3</td> <td>88.7</td> <td style="border-right: 1px solid black;">84.5</td> <td>17.1</td> <td>11.4</td> <td style="border-right: 1px solid black;">21.7</td> <td>13.5</td> <td>12.2</td> <td style="border-right: 1px solid black;">10.3</td> <td>25.0</td> <td>40.4</td> <td style="border-right: 1px solid black;">40.8</td> <td>5.8</td> <td>7.2</td> <td style="border-right: 1px solid black;">8.2</td> <td>11.3</td> <td>30.6</td> <td>40.4</td> </tr> <tr> <td style="border-right: 1px solid black;">5</td> <td>16.1</td> <td>24.2</td> <td style="border-right: 1px solid black;">10.5</td> <td>34.7</td> <td>28.3</td> <td style="border-right: 1px solid black;">30.9</td> <td>22.5</td> <td>2.3</td> <td style="border-right: 1px solid black;">2.1</td> <td>88.0</td> <td>90.5</td> <td style="border-right: 1px solid black;">88.4</td> <td>30.0</td> <td>13.6</td> <td style="border-right: 1px solid black;">23.7</td> <td>11.8</td> <td>10.0</td> <td style="border-right: 1px solid black;">9.9</td> <td>39.2</td> <td>38.1</td> <td style="border-right: 1px solid black;">43.0</td> <td>8.6</td> <td>6.9</td> <td style="border-right: 1px solid black;">8.6</td> <td>42.9</td> <td>36.6</td> <td>40.8</td> </tr> <tr> <td rowspan="3">Idefics3-8B</td> <td style="border-right: 1px solid black;">0</td> <td>16.1</td> <td>20.7</td> <td style="border-right: 1px solid black;">17.1</td> <td>24.3</td> <td>24.3</td> <td style="border-right: 1px solid black;">22.1</td> <td>0.0</td> <td>5.0</td> <td style="border-right: 1px solid black;">2.3</td> <td>91.5</td> <td>90.7</td> <td style="border-right: 1px solid black;">91.6</td> <td>38.5</td> <td>35.0</td> <td style="border-right: 1px solid black;">9.3</td> <td>11.0</td> <td>11.1</td> <td style="border-right: 1px solid black;">4.5</td> <td>5.8</td> <td>6.0</td> <td style="border-right: 1px solid black;">32.9</td> <td>6.2</td> <td>5.0</td> <td style="border-right: 1px solid black;">9.1</td> <td>17.2</td> <td>18.0</td> <td>5.0</td> </tr> <tr> <td style="border-right: 1px solid black;">3</td> <td>8.7</td> <td>10.1</td> <td style="border-right: 1px solid black;">6.2</td> <td>26.9</td> <td>26.9</td> <td style="border-right: 1px solid black;">21.9</td> <td>8.1</td> <td>7.5</td> <td style="border-right: 1px solid black;">1.1</td> <td>84.0</td> <td>86.4</td> <td style="border-right: 1px solid black;">90.6</td> <td>22.2</td> <td>23.0</td> <td style="border-right: 1px solid black;">5.8</td> <td>13.1</td> <td>12.0</td> <td style="border-right: 1px solid black;">11.9</td> <td>32.2</td> <td>31.0</td> <td style="border-right: 1px solid black;">38.9</td> <td>7.0</td> <td>6.5</td> <td style="border-right: 1px solid black;">4.5</td> <td>21.8</td> <td>22.0</td> <td>0.6</td> </tr> <tr> <td style="border-right: 1px solid black;">5</td> <td>4.4</td> <td>9.0</td> <td style="border-right: 1px solid black;">5.4</td> <td>22.3</td> <td>26.9</td> <td style="border-right: 1px solid black;">20.9</td> <td>5.5</td> <td>8.5</td> <td style="border-right: 1px solid black;">0.0</td> <td>65.1</td> <td>88.3</td> <td style="border-right: 1px solid black;">90.7</td> <td>15.1</td> <td>17.5</td> <td style="border-right: 1px solid black;">3.5</td> <td>15.1</td> <td>14.8</td> <td style="border-right: 1px solid black;">6.4</td> <td>27.6</td> <td>28.0</td> <td style="border-right: 1px solid black;">39.8</td> <td>5.4</td> <td>8.7</td> <td style="border-right: 1px solid black;">5.6</td> <td>22.7</td> <td>22.5</td> <td>0.0</td> </tr> <tr> <td rowspan="3">VILA-1.5-8B</td> <td style="border-right: 1px solid black;">0</td> <td>3.8</td> <td>0.0</td> <td style="border-right: 1px solid black;">0.0</td> <td>23.5</td> <td>13.0</td> <td style="border-right: 1px solid black;">15.8</td> <td>0.0</td> <td>6.2</td> <td style="border-right: 1px solid black;">0.0</td> <td>85.2</td> <td>19.2</td> <td style="border-right: 1px solid black;">27.1</td> <td>31.8</td> <td>21.1</td> <td style="border-right: 1px solid black;">27.3</td> <td>1.6</td> <td>3.1</td> <td style="border-right: 1px solid black;">8.1</td> <td>35.4</td> <td>34.8</td> <td style="border-right: 1px solid black;">36.6</td> <td>8.8</td> <td>4.9</td> <td style="border-right: 1px solid black;">9.1</td> <td>1.8</td> <td>2.1</td> <td >2.7</td> </tr> <tr> <td style="border-right: 1px solid black;">3</td> <td>1.2</td> <td>0.8</td> <td style="border-right: 1px solid black;">0.0</td> <td>25.1</td> <td>28.8</td> <td style="border-right: 1px solid black;">28.8</td> <td>16.6</td> <td>11.6</td> <td style="border-right: 1px solid black;">6.0</td> <td>68.3</td> <td>72.4</td> <td style="border-right: 1px solid black;">79.5</td> <td>22.1</td> <td>31.0</td> <td style="border-right: 1px solid black;">28.3</td> <td>9.7</td> <td>10.7</td> <td style="border-right: 1px solid black;">9.1</td> <td>24.9</td> <td>35.5</td> <td style="border-right: 1px solid black;">36.5</td> <td>8.9</td> <td>7.2</td> <td style="border-right: 1px solid black;">7.2</td> <td>25.5</td> <td>22.3</td> <td>36.8</td> </tr> <tr> <td style="border-right: 1px solid black;">5</td> <td>0.4</td> <td>5.0</td> <td style="border-right: 1px solid black;">6.0</td> <td>23.2</td> <td>30.8</td> <td style="border-right: 1px solid black;">30.8</td> <td>18.2</td> <td>19.0</td> <td style="border-right: 1px solid black;">18.0</td> <td>59.5</td> <td>74.6</td> <td style="border-right: 1px solid black;">76.4</td> <td>24.7</td> <td>35.0</td> <td style="border-right: 1px solid black;">32.0</td> <td>11.6</td> <td>14.1</td> <td style="border-right: 1px solid black;">12.0</td> <td>28.6</td> <td>40.0</td> <td style="border-right: 1px solid black;">38.0</td> <td>8.3</td> <td>7.0</td> <td style="border-right: 1px solid black;">8.0</td> <td>11.8</td> <td>25.0</td> <td>25.0</td> </tr> <tr> <td rowspan="3">Qwen2-VL-2B</td> <td style="border-right: 1px solid black;">0</td> <td>34.1</td> <td>4.6</td> <td style="border-right: 1px solid black;">5.0</td> <td>19.2</td> <td>22.1</td> <td style="border-right: 1px solid black;">20.9</td> <td>25.7</td> <td>19.0</td> <td style="border-right: 1px solid black;">17.9</td> <td>90.2</td> <td>90.8</td> <td style="border-right: 1px solid black;">91.2</td> <td>18.2</td> <td>8.3</td> <td style="border-right: 1px solid black;">3.5</td> <td>0.0</td> <td>0.0</td> <td style="border-right: 1px solid black;">0.0</td> <td>0.0</td> <td>26.0</td> <td style="border-right: 1px solid black;">31.0</td> <td>0.0</td> <td>8.3</td> <td style="border-right: 1px solid black;">0.0</td> <td>0.3</td> <td>2.1</td> <td>2.4</td> </tr> <tr> <td style="border-right: 1px solid black;">3</td> <td>4.8</td> <td>18.9</td> <td style="border-right: 1px solid black;">3.5</td> <td>25.2</td> <td>21.4</td> <td style="border-right: 1px solid black;">20.2</td> <td>7.7</td> <td>17.5</td> <td style="border-right: 1px solid black;">15.0</td> <td>87.2</td> <td>90.3</td> <td style="border-right: 1px solid black;">90.5</td> <td>27.9</td> <td>2.9</td> <td style="border-right: 1px solid black;">2.4</td> <td>0.0</td> <td>0.0</td> <td style="border-right: 1px solid black;">0.0</td> <td>38.8</td> <td>34.5</td> <td style="border-right: 1px solid black;">33.7</td> <td>5.9</td> <td>3.4</td> <td style="border-right: 1px solid black;">0.0</td> <td>8.5</td> <td>0.9</td> <td>0.0</td> </tr> <tr> <td style="border-right: 1px solid black;">5</td> <td>2.7</td> <td>26.3</td> <td style="border-right: 1px solid black;">25.9</td> <td>25.3</td> <td>21.7</td> <td style="border-right: 1px solid black;">20.9</td> <td>15.8</td> <td>19.0</td> <td style="border-right: 1px solid black;">18.7</td> <td>90.3</td> <td>90.5</td> <td style="border-right: 1px solid black;">90.3</td> <td>28.1</td> <td>11.8</td> <td style="border-right: 1px solid black;">6.8</td> <td>0.0</td> <td>0.0</td> <td style="border-right: 1px solid black;">0.0</td> <td>34.4</td> <td>27.8</td> <td style="border-right: 1px solid black;">24.6</td> <td>3.0</td> <td>2.2</td> <td style="border-right: 1px solid black;">0.0</td> <td>5.4</td> <td>0.3</td> <td>0.0</td> </tr> <tr> <td rowspan="3">Qwen2-VL-7B</td> <td style="border-right: 1px solid black;">0</td> <td>9.1</td> <td>10.2</td> <td style="border-right: 1px solid black;">7.0</td> <td>32.5</td> <td>32.5</td> <td style="border-right: 1px solid black;">35.2</td> <td>31.0</td> <td>30.1</td> <td style="border-right: 1px solid black;">17.5</td> <td>92.1</td> <td>92.0</td> <td style="border-right: 1px solid black;">91.5</td> <td>32.3</td> <td>33.5</td> <td style="border-right: 1px solid black;">34.5</td> <td>2.4</td> <td>2.7</td> <td style="border-right: 1px solid black;">3.8</td> <td>32.1</td> <td>36.4</td> <td style="border-right: 1px solid black;">41.9</td> <td>7.5</td> <td>7.9</td> <td style="border-right: 1px solid black;">10.5</td> <td>32.3</td> <td>33.2</td> <td >46.7</td> </tr> <tr> <td style="border-right: 1px solid black;">3</td> <td>2.8</td> <td>4.0</td> <td style="border-right: 1px solid black;">2.1</td> <td>35.6</td> <td>36.0</td> <td style="border-right: 1px solid black;">34.1</td> <td>22.4</td> <td>25.3</td> <td style="border-right: 1px solid black;">14.7</td> <td>90.4</td> <td>92.5</td> <td style="border-right: 1px solid black;">91.1</td> <td>33.1</td> <td>34.5</td> <td style="border-right: 1px solid black;">30.4</td> <td>14.7</td> <td>15.0</td> <td style="border-right: 1px solid black;">10.7</td> <td>42.8</td> <td>41.0</td> <td style="border-right: 1px solid black;">41.3</td> <td>8.4</td> <td>11.2</td> <td style="border-right: 1px solid black;">9.0</td> <td>37.8</td> <td>38.6</td> <td>41.6</td> </tr> <tr> <td style="border-right: 1px solid black;">5</td> <td>2.0</td> <td>2.1</td> <td style="border-right: 1px solid black;">3.2</td> <td>37.2</td> <td>37.2</td> <td style="border-right: 1px solid black;">29.3</td> <td>24.6</td> <td>22.0</td> <td style="border-right: 1px solid black;">10.0</td> <td>91.2</td> <td>91.5</td> <td style="border-right: 1px solid black;">91.1</td> <td>32.3</td> <td>32.0</td> <td style="border-right: 1px solid black;">31.6</td> <td>13.8</td> <td>11.2</td> <td style="border-right: 1px solid black;">4.9</td> <td>32.3</td> <td>43.0</td> <td style="border-right: 1px solid black;">40.9</td> <td>8.3</td> <td>9.5</td> <td style="border-right: 1px solid black;">9.7</td> <td>47.8</td> <td>43.5</td> <td>16.8</td> </tr> <tr> <td rowspan="3">Qwen2-VL-72B</td> <td style="border-right: 1px solid black;">0</td> <td>14.3</td> <td>16.7</td> <td style="border-right: 1px solid black;">14.3</td> <td>41.7</td> <td>44.6</td> <td style="border-right: 1px solid black;">41.7</td> <td>23.7</td> <td>30.0</td> <td style="border-right: 1px solid black;">23.7</td> <td>93.7</td> <td>94.8</td> <td style="border-right: 1px solid black;">93.7</td> <td>39.0</td> <td>42.3</td> <td style="border-right: 1px solid black;">39.0</td> <td>12.8</td> <td>19.8</td> <td style="border-right: 1px solid black;">12.8</td> <td>47.2</td> <td>51.0</td> <td style="border-right: 1px solid black;">47.2</td> <td>13.4</td> <td>13.2</td> <td style="border-right: 1px solid black;">13.4</td> <td>61.9</td> <td>61.0</td> <td>61.9</td> </tr> <tr> <td style="border-right: 1px solid black;">3</td> <td>28.2</td> <td>34.2</td> <td style="border-right: 1px solid black;">28.2</td> <td>43.9</td> <td>43.6</td> <td style="border-right: 1px solid black;">43.2</td> <td>24.8</td> <td>28.3</td> <td style="border-right: 1px solid black;">24.8</td> <td>93.1</td> <td>94.1</td> <td style="border-right: 1px solid black;">93.1</td> <td>38.0</td> <td>39.4</td> <td style="border-right: 1px solid black;">37.9</td> <td>18.9</td> <td>16.0</td> <td style="border-right: 1px solid black;">18.9</td> <td>48.1</td> <td>53.1</td> <td style="border-right: 1px solid black;">48.1</td> <td>23.1</td> <td>17.6</td> <td style="border-right: 1px solid black;">23.1</td> <td>56.7</td> <td>57.1</td> <td>56.7</td> </tr> <tr> <td style="border-right: 1px solid black;">5</td> <td>39.5</td> <td>31.0</td> <td style="border-right: 1px solid black;">27.0</td> <td>43.9</td> <td>44.9</td> <td style="border-right: 1px solid black;">42.3</td> <td>27.0</td> <td>29.7</td> <td style="border-right: 1px solid black;">21.6</td> <td>93.7</td> <td>94.7</td> <td style="border-right: 1px solid black;">93.1</td> <td>41.9</td> <td>43.9</td> <td style="border-right: 1px solid black;">35.8</td> <td>15.5</td> <td>13.1</td> <td style="border-right: 1px solid black;">19.8</td> <td>58.2</td> <td>54.2</td> <td style="border-right: 1px solid black;">49.3</td> <td>20.2</td> <td>20.0</td> <td style="border-right: 1px solid black;">21.2</td> <td>50.8</td> <td>58.8</td> <td>55.4</td> </tr> <tr> <td rowspan="3">InternVL-4B</td> <td style="border-right: 1px solid black;">0</td> <td>14.9</td> <td>4.6</td> <td style="border-right: 1px solid black;">4.5</td> <td>26.6</td> <td>29.8</td> <td style="border-right: 1px solid black;">30.7</td> <td>0.0</td> <td>10.5</td> <td style="border-right: 1px solid black;">15.4</td> <td>91.4</td> <td>90.3</td> <td style="border-right: 1px solid black;">91.4</td> <td>14.3</td> <td>25.3</td> <td style="border-right: 1px solid black;">22.4</td> <td>6.3</td> <td>11.7</td> <td style="border-right: 1px solid black;">9.3</td> <td>41.8</td> <td>41.0</td> <td style="border-right: 1px solid black;">41.0</td> <td>8.0</td> <td>10.7</td> <td style="border-right: 1px solid black;">12.2</td> <td>24.6</td> <td>19.4</td> <td>23.4</td> </tr> <tr> <td style="border-right: 1px solid black;">3</td> <td>4.1</td> <td>2.2</td> <td style="border-right: 1px solid black;">2.3</td> <td>27.7</td> <td>27.4</td> <td style="border-right: 1px solid black;">29.5</td> <td>16.3</td> <td>15.8</td> <td style="border-right: 1px solid black;">16.3</td> <td>78.0</td> <td>85.2</td> <td style="border-right: 1px solid black;">89.3</td> <td>25.7</td> <td>26.5</td> <td style="border-right: 1px solid black;">25.0</td> <td>8.8</td> <td>8.8</td> <td style="border-right: 1px solid black;">10.0</td> <td>36.7</td> <td>33.9</td> <td style="border-right: 1px solid black;">36.1</td> <td>2.6</td> <td>6.5</td> <td style="border-right: 1px solid black;">7.6</td> <td>26.0</td> <td>14.9</td> <td>22.0</td> </tr> <tr> <td style="border-right: 1px solid black;">5</td> <td>3.2</td> <td>1.6</td> <td style="border-right: 1px solid black;">2.4</td> <td>33.4</td> <td>28.1</td> <td style="border-right: 1px solid black;">30.4</td> <td>16.9</td> <td>15.4</td> <td style="border-right: 1px solid black;">17.5</td> <td>90.1</td> <td>87.2</td> <td style="border-right: 1px solid black;">90.4</td> <td>26.8</td> <td>27.6</td> <td style="border-right: 1px solid black;">27.9</td> <td>10.0</td> <td>7.4</td> <td style="border-right: 1px solid black;">7.8</td> <td>40.1</td> <td>37.9</td> <td style="border-right: 1px solid black;">39.7</td> <td>9.3</td> <td>8.0</td> <td style="border-right: 1px solid black;">9.2</td> <td>40.9</td> <td>13.1</td> <td >20.5</td> </tr> <tr> <td rowspan="3">InternVL-8B</td> <td style="border-right: 1px solid black;">0</td> <td>7.4</td> <td>32.8</td> <td style="border-right: 1px solid black;">37.4</td> <td>20.0</td> <td>23.0</td> <td style="border-right: 1px solid black;">24.8</td> <td>1.2</td> <td>6.7</td> <td style="border-right: 1px solid black;">2.2</td> <td>92.3</td> <td>90.2</td> <td style="border-right: 1px solid black;">91.3</td> <td>3.6</td> <td>12.4</td> <td style="border-right: 1px solid black;">18.2</td> <td>12.4</td> <td>6.8</td> <td style="border-right: 1px solid black;">7.2</td> <td>8.7</td> <td>18.0</td> <td style="border-right: 1px solid black;">22.0</td> <td>16.2</td> <td>11.4</td> <td style="border-right: 1px solid black;">7.2</td> <td>5.5</td> <td>15.8</td> <td>25.6</td> </tr> <tr> <td style="border-right: 1px solid black;">3</td> <td>9.7</td> <td>23.8</td> <td style="border-right: 1px solid black;">5.8</td> <td>30.5</td> <td>24.2</td> <td style="border-right: 1px solid black;">31.7</td> <td>14.5</td> <td>11.9</td> <td style="border-right: 1px solid black;">13.9</td> <td>80.5</td> <td>89.0</td> <td style="border-right: 1px solid black;">90.9</td> <td>27.6</td> <td>9.1</td> <td style="border-right: 1px solid black;">25.1</td> <td>9.9</td> <td>13.3</td> <td style="border-right: 1px solid black;">10.4</td> <td>33.8</td> <td>16.2</td> <td style="border-right: 1px solid black;">35.4</td> <td>7.2</td> <td>0.0</td> <td style="border-right: 1px solid black;">5.2</td> <td>39.8</td> <td>30.0</td> <td>40.9</td> </tr> <tr> <td style="border-right: 1px solid black;">5</td> <td>7.7</td> <td>23.0</td> <td style="border-right: 1px solid black;">6.7</td> <td>27.8</td> <td>25.0</td> <td style="border-right: 1px solid black;">31.4</td> <td>15.8</td> <td>6.4</td> <td style="border-right: 1px solid black;">11.6</td> <td>79.6</td> <td>90.7</td> <td style="border-right: 1px solid black;">91.1</td> <td>26.4</td> <td>11.6</td> <td style="border-right: 1px solid black;">27.8</td> <td>10.8</td> <td>6.8</td> <td style="border-right: 1px solid black;">7.0</td> <td>28.5</td> <td>22.7</td> <td style="border-right: 1px solid black;">37.8</td> <td>7.7</td> <td>2.2</td> <td style="border-right: 1px solid black;">4.1</td> <td>25.8</td> <td>34.6</td> <td>40.5</td> </tr> <tr> <td rowspan="3">GPT-4o</td> <td style="border-right: 1px solid black;">0</td> <td>45.2</td> <td>46.5</td> <td style="border-right: 1px solid black;">42.9</td> <td>47.6</td> <td>47.3</td> <td style="border-right: 1px solid black;">42.6</td> <td>51.7</td> <td>52.8</td> <td style="border-right: 1px solid black;">48.7</td> <td>95.5</td> <td>95.7</td> <td style="border-right: 1px solid black;">94.6</td> <td>32.9</td> <td>28.0</td> <td style="border-right: 1px solid black;">14.1</td> <td>30.2</td> <td>19.3</td> <td style="border-right: 1px solid black;">21.9</td> <td>52.4</td> <td>49.9</td> <td style="border-right: 1px solid black;">42.3</td> <td>35.6</td> <td>40.3</td> <td style="border-right: 1px solid black;">34.5</td> <td>34.8</td> <td>45.2</td> <td>42.2</td> </tr> <tr> <td style="border-right: 1px solid black;">3</td> <td>42.8</td> <td>39.8</td> <td style="border-right: 1px solid black;">30.2</td> <td>38.9</td> <td>37.5</td> <td style="border-right: 1px solid black;">35.7</td> <td>49.8</td> <td>33.7</td> <td style="border-right: 1px solid black;">32.9</td> <td>93.8</td> <td>92.9</td> <td style="border-right: 1px solid black;">87.0</td> <td>21.9</td> <td>21.7</td> <td style="border-right: 1px solid black;">15.6</td> <td>10.8</td> <td>3.5</td> <td style="border-right: 1px solid black;">11.6</td> <td>46.2</td> <td>44.4</td> <td style="border-right: 1px solid black;">41.3</td> <td>27.9</td> <td>30.2</td> <td style="border-right: 1px solid black;">20.8</td> <td>28.7</td> <td>42.3</td> <td>41.1</td> </tr> <tr style="border-bottom: 2px solid black;"> <td style="border-right: 1px solid black;">5</td> <td>43.4</td> <td>42.3</td> <td style="border-right: 1px solid black;">30.7</td> <td>41.9</td> <td>39.8</td> <td style="border-right: 1px solid black;">38.4</td> <td>46.8</td> <td>42.6</td> <td style="border-right: 1px solid black;">40.3</td> <td>94.2</td> <td>94.2</td> <td style="border-right: 1px solid black;">87.4</td> <td>28.9</td> <td>19.2</td> <td style="border-right: 1px solid black;">14.9</td> <td>10.7</td> <td>9.5</td> <td style="border-right: 1px solid black;">20.3</td> <td>47.6</td> <td>44.9</td> <td style="border-right: 1px solid black;">40.6</td> <td>29.6</td> <td>31.2</td> <td style="border-right: 1px solid black;">26.1</td> <td>35.2</td> <td>37.2</td> <td>39.1</td> </tr> </table> ## Examples Some zero-shot and few-shot examples on different tasks and different image set can be found as following: <p align="center"> <img src="./images/p3_4.png" width="80%" alt="Image 1"> </p> <p align="center"> <img src="./images/p3_5.png" width="80%" alt="Image 2"> </p> <p align="center"> <img src="./images/o3_4.png" width="80%" alt="Image 3"> </p> <p align="center"> <img src="./images/o3_5.png" width="80%" alt="Image 4"> </p> <p align="center"> <img src="./images/p3_2.png" width="80%" alt="Image 5"> </p> <p align="center"> <img src="./images/p3_3.png" width="80%" alt="Image 6"> </p> <p align="center"> <img src="./images/o3_1.png" width="80%" alt="Image 7"> </p> <p align="center"> <img src="./images/o3_3.png" width="80%" alt="Image 8"> </p>

metagene-ai/HumanVirusInfecting

metagene-ai

--- configs: - config_name: class-1 data_files: - split: train path: hv/1/data.parquet - config_name: class-2 data_files: - split: train path: hv/2/data.parquet - config_name: class-3 data_files: - split: train path: hv/3/data.parquet - config_name: class-4 data_files: - split: train path: hv/4/data.parquet license: apache-2.0 task_categories: - text-classification language: - en tags: - biology pretty_name: Human virus infecting ---

metagene-ai/HumanMicrobiomeProjectReference

metagene-ai

--- license: apache-2.0 language: - en tags: - biology pretty_name: HMP reference ---

hary0101/conspiracy

hary0101

--- license: openrail task_categories: - text2text-generation - audio-to-audio - text-classification - text-generation language: - pl - en tags: - not-for-all-audiences pretty_name: Truth seeker size_categories: - 10B<n<100B --- # Dataset Card for Dataset Name conspiracy # Dataset Summary The Conspiracy dataset is a curated collection of philosophical discussions, conspiracy theories, alternative history narratives, and metaphysical explorations. Designed to serve as a foundation for AI models that analyze unconventional perspectives, this dataset blends deep analytical thinking with speculative reasoning. It supports text generation, text classification, and multi-language text-based interactions in English and Polish. ## Dataset Details ### Dataset Description This dataset is designed for applications in philosophy, conspiracy theories, and alternative viewpoints. It includes structured dialogues, Q&A formats, long-form narratives, and analytical breakdowns of controversial or unconventional ideas. Topics include: Philosophy: Existentialism, metaphysics, epistemology, ethics. Conspiracy Theories: Secret societies, hidden histories, government cover-ups, Antarctica/Ice Wall, UFOs, deep-state agendas. Alternative History: Reinterpretations of historical events, suppressed discoveries, lost civilizations. Metaphysics and Esoteric Knowledge: Law of attraction, vibrational energy, water memory, sacred geometry. Electromagnetic Consciousness: Theories on thought frequencies, external amplification of emotions, and mind influence. - **Curated by:** [Michal] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** MIT License ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [https://huggingface.co/datasets/conspiracy] - **Paper [optional]:** [https://dn790001.ca.archive.org/0/items/illuminatus-trilogy-ebook/Illuminatus%20TRILOGY%20EBOOK.pdf, https://archive.org/stream/DinahSheltonEncyclopediaOfGenocideAndCrimesAgainstHumanityVolumeONE/Dinah_Shelton_Encyclopedia_of_Genocide_and_Crimes_against_Humanity_Volume_ONE_djvu.txt] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use -Training AI assistants with philosophical and alternative viewpoints. -Enhancing LLM-based analysis of non-mainstream narratives. -Assisting research into esoteric and suppressed knowledge. -Creating synthetic dialogues about complex or hidden topics. ### Out-of-Scope Use -Scientific applications requiring strictly empirical verification. -Generating misleading or harmful misinformation. -Promoting extremism or baseless fearmongering. ## Dataset Structure Dataset Structure Data Fields Each entry consists of: Question / Prompt: A philosophical or conspiratorial query. Response: An analytical or speculative answer. Source Type: (Philosophy, Conspiracy, Metaphysics, etc.) Language: English (en) or Polish (pl). Example Entry: "question": "Is there evidence of civilizations beyond the Ice Wall?", "response": "While mainstream geography asserts Antarctica is an uninhabitable continent, certain theories propose that vast undiscovered lands exist beyond an enormous ice barrier. These lands could house ancient civilizations, remnants of pre-flood societies, or even advanced breakaway civilizations protected by global secrecy.", "source_type": "Conspiracy", "language": "en" ## Dataset Creation ### Curation Rationale The dataset is created to support AI applications in unconventional inquiry, particularly for philosophical discourse and alternative research perspectives. The goal is not to promote misinformation but to provide a balanced and structured analysis of speculative ideas. ### Source Data The dataset is built using: -Curated Texts: Philosophical essays, conspiracy discussions, alternative history books. -Synthetic Dialogues: AI-generated Q&A based on structured prompts. -Community Contributions: Discussions from forums and research groups. #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? Philosophers, researchers, and alternative history enthusiasts. AI-assisted synthesis of speculative discussions. ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information This dataset does not contain personally identifiable information (PII) such as names, addresses, or financial details. However, some topics covered—such as political views, religious beliefs, and alternative historical interpretations—may be considered sensitive. Efforts have been made to ensure that discussions remain analytical and speculative rather than promoting harmful or misleading narratives. ## Bias, Risks, and Limitations Biases The dataset includes a mix of philosophical, speculative, and conspiratorial content. Some topics may reflect subjective viewpoints rather than objective truths. Selection bias may exist due to the dataset’s focus on alternative perspectives rather than mainstream scientific consensus. The dataset may favor perspectives that resonate with metaphysical or alternative history communities. Risks Users should be aware that certain conspiracy theories can be linked to misinformation or pseudoscience. This dataset is meant for analytical exploration rather than validation of these theories. Misinterpretation of speculative content as factual information could contribute to the spread of misleading narratives. Some discussions may include controversial topics that require careful handling to avoid reinforcing harmful beliefs. Limitations The dataset does not claim to provide verifiable historical facts but rather presents alternative interpretations. It is not suitable for scientific research that demands strict empirical validation. Some areas of discussion may lack mainstream academic sources, relying instead on community discussions, esoteric texts, or theoretical arguments. Selection Bias The dataset is curated with a focus on alternative viewpoints, conspiracy theories, and esoteric knowledge, which may inherently introduce a selection bias. It prioritizes unconventional perspectives over mainstream academic or scientific consensus, leading to an emphasis on speculative and philosophical interpretations rather than empirical verification. Confirmation Bias Since the dataset contains discussions from sources that often challenge official narratives, it may reinforce specific worldviews rather than presenting balanced counterarguments. While efforts have been made to include multiple perspectives, certain topics may lean towards interpretations that validate pre-existing beliefs in conspiracy theories or alternative history. Cultural and Linguistic Bias The dataset primarily features English and Polish content, which may reflect Western and Slavic perspectives more prominently than those from other cultures. Alternative theories often emerge from specific cultural, historical, or geopolitical contexts, which can influence how events and ideas are framed. Epistemic Bias Many of the ideas in the dataset rely on subjective interpretation, intuition, and anecdotal evidence rather than formal empirical studies. The nature of speculative knowledge means that logical rigor and evidentiary standards can vary across different entries. Mitigation Strategies Users should be encouraged to cross-reference the dataset’s claims with mainstream sources and critical analyses. AI models trained on this dataset should be fine-tuned with diverse datasets to prevent overfitting to speculative narratives. Implementing bias-detection mechanisms can help identify when a response leans too heavily into unverified or one-sided perspectives. ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** conspiracy_dataset, author = hary0101, title = Conspiracy Dataset: A Collection of Alternative Perspectives, Conspiracy Theories, and Metaphysical Explorations, year = 2025, url = https://huggingface.co/datasets/conspiracy, note = Curated dataset focusing on philosophy, conspiracy theories, alternative history, and metaphysics. **APA:** hary0101. (2025). Conspiracy Dataset: A Collection of Alternative Perspectives, Conspiracy Theories, and Metaphysical Explorations. Retrieved from https://huggingface.co/datasets/conspiracy ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact smartcrew4you@gmail.com [More Information Needed]

didiudom94/gentlemen

didiudom94

--- dataset_info: features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 2866858007.66 num_examples: 44379 download_size: 2879283904 dataset_size: 2866858007.66 configs: - config_name: default data_files: - split: train path: data/train-* ---

yguooo/summarize_from_feedback_tldr_3_filtered_oai_preprocessing_pythia_scene0_1incontext

yguooo

--- dataset_info: features: - name: id dtype: string - name: subreddit dtype: string - name: title dtype: string - name: post dtype: string - name: summary dtype: string - name: query_token sequence: int64 - name: query dtype: string - name: reference_response dtype: string - name: reference_response_token sequence: int64 - name: reference_response_token_len dtype: int64 - name: query_reference_response dtype: string - name: query_reference_response_token sequence: int64 - name: query_reference_response_token_response_label sequence: int64 - name: query_reference_response_token_len dtype: int64 splits: - name: train num_bytes: 4779234453 num_examples: 116722 - name: validation num_bytes: 263995185 num_examples: 6447 - name: test num_bytes: 268380906 num_examples: 6553 download_size: 648182216 dataset_size: 5311610544 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- # TL;DR SFT Dataset for OpenAI's [Summarize from Feedback](https://openai.com/blog/summarization/) task The dataset is directly taken from https://github.com/openai/summarize-from-feedback/tree/700967448d10004279f138666442bf1497d0e705#reddit-tldr-dataset These columns are taken directly from the aforementioned dataset: * **id**: unique identifier for the post * **subreddit**: subreddit the post was taken from * **title**: title of the post * **post**: body of the post * **summary**: summary of the post * **reference_response**: reference response for the post These columns are added by this preprocessing script: * **query**: length-limited query for summarization: OAI pre-processes the main text (title + subreddit + post), ensuring it has only 512 tokens; if the main text is too long, then it tries to truncate at the last ` `. If it's too short it pads the main text ([summarize_from_feedback/tasks.py#L98-L165](https://github.com/openai/summarize-from-feedback/blob/700967448d10004279f138666442bf1497d0e705/summarize_from_feedback/tasks.py#L98-L165)). Padding is either space or `[PAD]` token (see Args below). * **query_token**: tokenized version of `query` * **reference_response_token**: tokenized version of `reference_response` * **reference_response_token_len**: length of `reference_response_token` * **query_reference_response**: concatenation of `query.strip()` and `reference_response` * **query_reference_response_token**: tokenized version of `query_reference_response`, up to `max_sft_query_response_length` tokens * **query_reference_response_token_len**: length of `query_reference_response_token` # Args ```python {'base_model': 'EleutherAI/pythia-1b', 'check_length_correctness': True, 'cnndm_params': TaskQueryHParams(length=1919, format_str='Article:\n{article}\n\nTL;DR:\n', truncate_field='article', truncate_text='\n', padding='pad_token', pad_token=[50277], pad_side='left', max_sft_response_length=None, max_sft_query_response_length=None, max_rm_response_length=155, max_rm_query_response_length=2021), 'debug': False, 'ds_name': 'pythia_scene0_1incontext', 'hf_entity': 'yguooo', 'push_to_hub': True, 'scenario': 0, 'tldr_params': TaskQueryHParams(length=1800, format_str='SUBREDDIT: ' 'r/relationships\\n\\nTITLE: I ' '(f/22) have to figure out if I ' 'want to still know these girls or ' 'not and would hate to sound ' 'insulting\\n\\nPOST: Not sure if ' "this belongs here but it's worth " 'a try. \\n\\nBackstory:\\nWhen I ' '(f/22) went through my first real ' 'breakup 2 years ago because he ' 'needed space after a year of ' 'dating roand it effected me more ' 'than I thought. It was a horrible ' 'time in my life due to living ' 'with my mother and finally having ' 'the chance to cut her out of my ' 'life. I can admit because of it ' 'was an emotional wreck and this ' "guy was stable and didn't know " 'how to deal with me. We ended by ' 'him avoiding for a month or so ' 'after going to a festival with my ' 'friends. When I think back I wish ' 'he just ended. So after he ended ' 'it added my depression I suffered ' 'but my friends helped me through ' 'it and I got rid of everything ' 'from him along with cutting ' 'contact. \\n\\nNow: Its been ' "almost 3 years now and I've " 'gotten better after counselling ' 'and mild anti depressants. My ' 'mother has been out of my life ' "since then so there's been alot " 'of progress. Being stronger after ' 'learning some lessons there been ' 'more insight about that time of ' 'my life but when I see him or a ' 'picture everything comes back. ' 'The emotions and memories bring ' 'me back down. \\n\\nHis friends ' '(both girls) are on my facebook ' 'because we get along well which ' 'is hard to find and I know ' "they'll always have his back. But " 'seeing him in a picture or ' 'talking to him at a convention ' 'having a conversation is tough. ' 'Crying confront of my current ' 'boyfriend is something I want to ' "avoid. \\n\\nSo I've been " 'thinking that I have to cut ' 'contact with these girls because ' "it's time to move on because it's " "healthier. It's best to avoid him " 'as well. But will they be ' 'insulted? Will they accept it? Is ' 'there going to be awkwardness? ' "I'm not sure if it's the right to " 'do and could use some outside ' 'opinions.\\n\\nTL;DR: I still ' "have contact with an old ex's " "friends but can't stand to see or " 'talk to him. His friends are ' 'really nice ,so how do I tell ' 'them I possibly want to unfriend ' 'them on Facebook because of ' 'him?<|endoftext|>\\n\\nSUBREDDIT: ' 'r/{subreddit}\\n\\nTITLE: ' '{title}\\n\\nPOST: ' '{post}\\n\\nTL;DR:', truncate_field='post', truncate_text='\n', padding='pad_token', pad_token=[50277], pad_side='left', max_sft_response_length=53, max_sft_query_response_length=1078, max_rm_response_length=169, max_rm_query_response_length=1145)} ```

MasterControlAIML/Medmcqa-For-FinetuningQwen

MasterControlAIML

--- license: apache-2.0 ---

TomShales123/test2025_01_05_3

TomShales123

--- dataset_info: features: - name: text dtype: string - name: id dtype: string - name: metadata struct: - name: date dtype: string - name: file_path dtype: string - name: language dtype: string - name: language_score dtype: float64 - name: token_count dtype: int64 - name: url dtype: string splits: - name: train num_bytes: 92764 num_examples: 30 download_size: 69706 dataset_size: 92764 configs: - config_name: default data_files: - split: train path: data/train-* ---

DT4LM/naive_t5v1-1base_rte_pair_leap_old2

DT4LM

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 53734 num_examples: 163 download_size: 41332 dataset_size: 53734 --- # Dataset Card for "naive_t5v1-1base_rte_pair_leap_old2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

DT4LM/naive_t5v1-1base_rte_pair_leap_original_old2

DT4LM

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 53206 num_examples: 163 download_size: 41483 dataset_size: 53206 --- # Dataset Card for "naive_t5v1-1base_rte_pair_leap_original_old2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

weqweasdas/xxx

weqweasdas

--- dataset_info: features: - name: idx dtype: int64 - name: gt dtype: string - name: prompt dtype: string - name: answer dtype: string - name: my_solu sequence: string - name: pred sequence: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: turn dtype: int64 - name: rewards sequence: float64 splits: - name: train num_bytes: 10624955 num_examples: 2638 download_size: 3721799 dataset_size: 10624955 configs: - config_name: default data_files: - split: train path: data/train-* ---

taesiri/BugsBunny-InternVL2_5-78B-MPO-Extensive-Captioning

taesiri

--- dataset_info: features: - name: image dtype: image - name: prompt dtype: string - name: response dtype: string - name: metadata struct: - name: data_source dtype: string - name: dataset_split dtype: 'null' - name: image_index dtype: int64 - name: image_path dtype: string - name: model_name dtype: string - name: source_type dtype: string - name: timestamp dtype: string - name: image_name dtype: string - name: app_id dtype: string splits: - name: valid num_bytes: 31196016087.2 num_examples: 87912 download_size: 32962311660 dataset_size: 31196016087.2 configs: - config_name: default data_files: - split: valid path: data/valid-* ---

DT4LM/t5v1-1base_rte_pair_leap_4_1

DT4LM

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 51641 num_examples: 161 download_size: 39978 dataset_size: 51641 --- # Dataset Card for "t5v1-1base_rte_pair_leap_4_1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

DT4LM/t5v1-1base_rte_pair_leap_original_4_1

DT4LM

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 51350 num_examples: 161 download_size: 40518 dataset_size: 51350 --- # Dataset Card for "t5v1-1base_rte_pair_leap_original_4_1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mesolitica/Translate-QWQ-LONGCOT-500K

mesolitica

--- language: - ms --- # Translate QWQ-LONGCOT-500K Translate [PowerInfer/QWQ-LONGCOT-500K](https://huggingface.co/datasets/PowerInfer/QWQ-LONGCOT-500K) using [mesolitica/nanot5-base-malaysian-translation-v2.1](https://huggingface.co/mesolitica/nanot5-base-malaysian-translation-v2.1) ## Postfilter - Select rows that respond more than 2000 words. - Reject based on keywords.

DT4LM/t5v1-1base_sst2_pair_faster-alzantot_2_1

DT4LM

--- dataset_info: features: - name: text dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 22952 num_examples: 289 download_size: 17613 dataset_size: 22952 --- # Dataset Card for "t5v1-1base_sst2_pair_faster-alzantot_2_1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

frugal-ai-challenge/public-leaderboard-image

frugal-ai-challenge

--- license: cc-by-nc-4.0 ---

DT4LM/t5v1-1base_sst2_pair_faster-alzantot_original_2_1

DT4LM

--- dataset_info: features: - name: text dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 22640 num_examples: 289 download_size: 17006 dataset_size: 22640 --- # Dataset Card for "t5v1-1base_sst2_pair_faster-alzantot_original_2_1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

frugal-ai-challenge/public-leaderboard-audio

frugal-ai-challenge

--- license: cc-by-nc-4.0 ---

realtreetune/olympiadbench

realtreetune

--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: id dtype: int64 - name: subfield dtype: string - name: context dtype: 'null' - name: solution dtype: string - name: final_answer sequence: string - name: is_multiple_answer dtype: bool - name: unit dtype: string - name: answer_type dtype: string - name: error dtype: string - name: problem dtype: string - name: _provided_sol sequence: string - name: answer dtype: string splits: - name: test num_bytes: 1376306 num_examples: 675 download_size: 644430 dataset_size: 1376306 --- # Dataset Card for "olympiadbench" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CitronLegacy/LuceriaMobKara_wTagsCurated

CitronLegacy

--- license: mit ---

bonbon-rj/DriveMLLM

bonbon-rj

--- viewer: false --- This data is sourced from the image of the [nuScenes](https://www.nuscenes.org/) dataset. We extend our gratitude for their outstanding work!

nyuuzyou/buzzlyart

nyuuzyou

--- annotations_creators: - found language: - en license: - cc0-1.0 multilinguality: - monolingual pretty_name: Buzzly.art Art Platform Dataset size_categories: - 1K<n<10K source_datasets: - original task_categories: - image-classification configs: - config_name: default data_files: - split: train path: - 'buzzlyart.jsonl.zst' default: true - config_name: images data_files: - split: images path: - 'files.zip' --- # Dataset Card for Buzzly.art ### Dataset Summary This dataset contains 2,000 artwork submissions from the Buzzly.art platform, including images and associated metadata. The content includes original artworks, photography, and other visual arts along with detailed metadata about each submission. ### Languages The dataset is only in English, including all titles, descriptions, tags and other text content. ## Dataset Structure ### Data Fields This dataset includes the following fields: - `tags`: List of relevant tags (array of strings) - `title`: Artwork title (string) - `id`: Unique identifier for the submission (string) - `account`: Object containing: - `bucket`: Object containing: - `name`: Bucket name (string) - `displayName`: Display name (string) - `profilePicturePath`: Profile picture URL (string) - `user`: Object containing: - `id`: User ID (string) - `premiumUntil`: Premium subscription end date (string) - `username`: Username (string) - `artSubjects`: Subject matter categories (array of strings) - `bucket`: Object containing: - `name`: Bucket name (string) - `categories`: Artwork categories (array of strings) - `comments`: Number of comments (integer) - `contentRating`: Content rating (string) - `description`: Artwork description (string) - `favorites`: Number of favorites (integer) - `hasCustomThumbnail`: Has custom thumbnail flag (boolean) - `height`: Image height in pixels (integer) - `isFeatured`: Is featured flag (boolean) - `path`: URL path to full image (string) - `ratings`: List of ratings (array) - `slug`: URL slug (string) - `thumbnailPath`: URL path to thumbnail (string) - `thumbnailWidth`: Thumbnail width in pixels (integer) - `type`: Content type (string) - `userId`: Creator's user ID (string) - `visits`: Number of visits (integer) - `width`: Image width in pixels (integer) ### Data Splits The dataset is split into: - Metadata: JSON lines file containing artwork metadata (default) - Images: ZIP archive containing the actual artwork files ## Additional Information ### License This dataset is dedicated to the public domain under the Creative Commons Zero (CC0) license. This means you can: * Use it for any purpose, including commercial projects * Modify it however you like * Distribute it without asking permission No attribution is required, but it's always appreciated! CC0 license: https://creativecommons.org/publicdomain/zero/1.0/deed.en ### Dataset Curators - [nyuuzyou](https://ducks.party)

Hkang/summarize_pref

Hkang

--- dataset_info: features: - name: info struct: - name: id dtype: string - name: post dtype: string - name: title dtype: string - name: subreddit dtype: string - name: site dtype: string - name: article dtype: string - name: summaries list: - name: text dtype: string - name: policy dtype: string - name: note dtype: string - name: choice dtype: int32 - name: worker dtype: string - name: batch dtype: string - name: split dtype: string - name: extra struct: - name: confidence dtype: int32 - name: query_token sequence: int64 - name: query_attention_mask sequence: int64 - name: query dtype: string - name: chosen dtype: string - name: chosen_token sequence: int64 - name: chosen_attention_mask sequence: int64 - name: chosen_token_len dtype: int64 - name: rejected dtype: string - name: rejected_token sequence: int64 - name: rejected_attention_mask sequence: int64 - name: rejected_token_len dtype: int64 - name: chosen_policy dtype: string - name: rejected_policy dtype: string - name: policies dtype: string - name: query_chosen dtype: string - name: query_chosen_token sequence: int64 - name: query_chosen_attention_mask sequence: int64 - name: query_chosen_token_len dtype: int64 - name: query_rejected dtype: string - name: query_rejected_token sequence: int64 - name: query_rejected_attention_mask sequence: int64 - name: query_rejected_token_len dtype: int64 - name: query_token_len dtype: int64 - name: query_chosen_token_response_label sequence: int64 - name: query_rejected_token_response_label sequence: int64 splits: - name: train num_bytes: 4741154000 num_examples: 92858 - name: validation num_bytes: 4286306503 num_examples: 83802 - name: validation_cnndm num_bytes: 339988792 num_examples: 2284 download_size: 293527144 dataset_size: 9367449295 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: validation_cnndm path: data/validation_cnndm-* ---

RyanYr/reflect_nonGenCritic_mini8b-t0_gt-t1_mstllrg-t2_mini_correction

RyanYr

--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: response@0_correctness dtype: bool - name: response@2_correctness dtype: bool splits: - name: train num_bytes: 55167806 num_examples: 17386 download_size: 20935955 dataset_size: 55167806 configs: - config_name: default data_files: - split: train path: data/train-* ---

Youcef-213/ToolLens

Youcef-213

--- license: mit ---

DT4LM/naive_t5v1-1base_rte_pair_faster-alzantot

DT4LM

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 38744 num_examples: 125 download_size: 32753 dataset_size: 38744 configs: - config_name: default data_files: - split: train path: data/train-* ---

DT4LM/naive_t5v1-1base_rte_pair_faster-alzantot_original

DT4LM

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 38559 num_examples: 125 download_size: 32332 dataset_size: 38559 configs: - config_name: default data_files: - split: train path: data/train-* ---

surya-ravindra/solar_irradiance_dataset_2021_ASI

surya-ravindra

--- dataset_info: features: - name: DATE dtype: string - name: MST dtype: string - name: Global_horizontal_irradiance dtype: float32 - name: Direct_normal_irradiance dtype: float32 - name: Diffuse_horizontal_irradiance dtype: float32 - name: Zenith_angle dtype: float32 - name: Azimuth_angle dtype: float32 - name: cloud_obfuscation dtype: float32 - name: sun_visibility dtype: float32 - name: Raw_images dtype: image - name: Processed_images dtype: image - name: Clear_sky_ghi dtype: float64 - name: Clear_sky_dni dtype: float64 - name: Clear_sky_dhi dtype: float64 - name: physics_panel_tilt dtype: float64 - name: physics_panel_orientation dtype: int64 - name: physics_aoi dtype: float64 - name: physics_diffused_irradiance dtype: float64 - name: physics_reflected_irradiance_tilted dtype: float64 - name: physics_direct_irradiance_tilted dtype: float64 - name: physics_total_irradiance_tilted dtype: float64 splits: - name: train num_bytes: 1424834421.0 num_examples: 25576 download_size: 1363603951 dataset_size: 1424834421.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

weqweasdas/llama3_non_delete_rr40k_2e6_bz32_ep3tmp10_temp_exp_genbytmp

weqweasdas

--- dataset_info: features: - name: idx dtype: int64 - name: prompt dtype: string - name: answers sequence: string - name: gt dtype: string - name: rewards sequence: bool - name: proxy_label dtype: bool - name: my_prompt dtype: string - name: proxy_label_1st_round dtype: bool splits: - name: train num_bytes: 16070981 num_examples: 5000 download_size: 5785968 dataset_size: 16070981 configs: - config_name: default data_files: - split: train path: data/train-* ---

cognitivecomputations/HuggingFaceTB_smoltalk-DolphinLabeled

cognitivecomputations

--- language: - en tags: - synthetic configs: - config_name: all data_files: - split: train path: data/all/train* --- # HuggingFaceTB smoltalk DolphinLabeled ## Part of the [DolphinLabeled](https://huggingface.co/collections/cognitivecomputations/dolphinlabeled-datasets-677a9cc40a4d2007a8d1077e) series of datasets ## Presented by Eric Hartford and Cognitive Computations The purpose of this dataset is to enable filtering of HuggingFaceTB/smoltalk dataset. The original dataset is [HuggingFaceTB/smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) I have modified the dataset using two scripts. 1) [dedupe.py](dedupe.py) - removes rows with identical final message content 2) [label.py](label.py) - adds a "flags" column containing the following boolean values: - "refusal": whether the output is a refusal - "unsolicited": whether the output contains any unsolicited advice - "nsfw": whether the instruction or output contains nsfw content - "pii": whether the instruction or output contains pii - "disclaimer": whether the output gives disclaimers Please note that I have used Deepseek-V3 to generate these labels, and their system censored (refused to answer) less than 1% of the rows, which were dropped. The original dataset card follows: --- # SmolTalk  ## Dataset description This is a synthetic dataset designed for supervised finetuning (SFT) of LLMs. It was used to build [SmolLM2-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) family of models and contains 1M samples. During the development of SmolLM2, we observed that models finetuned on public SFT datasets underperformed compared to other models with proprietary instruction datasets. To address this gap, we created new synthetic datasets that improve instruction following while covering diverse tasks including text editing, rewriting, summarization, and reasoning. Through a series of data ablations at 1.7B scale, we enhanced our SFT mix by incorporating public datasets to strengthen specific capabilities such as mathematics, coding, system prompt following and long-context understanding. All the new datasets were generated with [distilabel](https://github.com/argilla-io/distilabel) and you can find the generation code here https://github.com/huggingface/smollm/tree/main/distilabel_pipelines. You can load a dataset using ```python from datasets import load_dataset ds = load_dataset("HuggingFaceTB/smoltalk", "all", split="train") # to load the train split of a specific subset such as smol-magpie-ultra, you can do ds = load_dataset("HuggingFaceTB/smoltalk", "smol-magpie-ultra", split="train") ``` ## Dataset composition The mix consists of: **New datasets** - *Smol-Magpie-Ultra*: the core component of our mix, consisting of 400K samples generated using the Magpie pipeline with /Llama-3.1-405B-Instruct. We also heavily curate and filter this dataset compared to the original Magpie-Pro pipeline. SmolLM models trained on this dataset alone outperform those trained on popular public datasets like OpenHermes and Magpie Pro across key benchmarks including IFEval and MT-Bench. - Smol-contraints: a 36K-sample dataset that trains models to follow specific constraints, such as generating responses with a fixed number of sentences or words, or incorporating specified words in the output. The dataset has been decontaminated against IFEval to prevent overlap. - Smol-rewrite: an 50k-sample collection focused on text rewriting tasks, such as adjusting tone to be more friendly or professional. Note that Smol-Magpie-Ultra also includes some rewriting, editing, and summarization examples. - Smol-summarize: an 100k-sample dataset specialized in email and news summarization. **Existing public datasets** To enhance capabilities in mathematics, coding, system prompts, and long-context understanding, we fine-tuned SmolLM2-1.7B on various public SFT datasets and included subsets of the best performing ones using tuned ratios. These include: - OpenHermes2.5: we added 100k samples from [OpenHermes2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5), since we found that it helps preserve and boost benchmarks such as MMLU and WinoGrande, and BBH. - MetaMathQA: we add this [dataset](https://huggingface.co/datasets/meta-math/MetaMathQA?) to improve the model on mathematics and reasoning, we include 50k random samples. - NuminaMath-CoT: we find that this [dataset](https://huggingface.co/datasets/AI-MO/NuminaMath-CoT) helps on mathematics, especially hard problems found in benchmarks such as MATH. - Self-Oss-Starcoder2-Instruct: we use this [dataset](https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-exec-filter-50k) to improve coding capabilities. - SystemChats2.0: to make the model support a variety of system prompt formats we add 30k samples from the [SystemChat-2.0](https://huggingface.co/datasets/cognitivecomputations/SystemChat-2.0) dataset. Note that Smol-rewrite and and Smol-summarize datasets also include system prompts. - LongAlign: we find that finetuning the model on only short samples makes it loose long context abilities beyond 2048 tokens, so we add english samples (with less than 16k tokens) from the [LongAlign-10k](https://huggingface.co/datasets/THUDM/LongAlign-10k) dataset and train with a 8192 sequence. - Everyday-conversations: this [dataset](https://huggingface.co/datasets/HuggingFaceTB/everyday-conversations-llama3.1-2k) includes multi-turn everyday conversations such as greeting and was used in SmolLM v1 post-training. - APIGen-Function-Calling: we use 80k samples from [apigen-function-calling](https://huggingface.co/datasets/argilla/apigen-function-calling) which is a mix of [Synth-APIGen-v0.1](https://huggingface.co/datasets/argilla/Synth-APIGen-v0.1) and [xlam-function-calling-60k](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) datasets. - Explore-Instruct-Rewriting: 30k samples from this rewriting [dataset](https://huggingface.co/datasets/Wanfq/Explore_Instruct_Rewriting_32k). You can find the code for generating the new datasets with [distilabel](https://github.com/argilla-io/distilabel) here: https://github.com/huggingface/smollm. The ablation details will be included in an upcoming blog post. ## License All the new datasets (Smol-Magpie-Ultra, Smol-contraints, Smol-rewrite, Smol-summarize) are licensed under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0). For the existing public datasets, please refer to the original dataset for the license [Dataset composition](#dataset-composition) ## Evaluation We compare SmolTalk to the recent [Orca AgentInstruct 1M](https://huggingface.co/datasets/microsoft/orca-agentinstruct-1M-v1) dataset by finetuning SmolLM2 on both datasets using the same training setup (we train for 2 epochs, using a learning rate of 3e-04, a sequence length of 8192 and a global batch size of 16).  We also observe significant improvements at 7B scale when fine-tuning [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.3) on SmolTalk, notably on IFEval, BBH, GS8Mk and MATH.  ## Smol-SmolTalk For SmolLM2-135M-Instruct and SmolLM2-360M-Instruct, we use a subset of the dataset that is more suitable for these smaller models. For instance, we only include samples from Smol-Magpie-Ultra with more concise conversations and exclude advanced math datasets. You can find the dataset here: https://huggingface.co/datasets/HuggingFaceTB/smol-smoltalk The training code is available here https://github.com/huggingface/alignment-handbook/tree/main/recipes/smollm2 ## Citation ```bash @misc{allal2024SmolLM2, title={SmolLM2 - with great data, comes great performance}, author={Loubna Ben Allal and Anton Lozhkov and Elie Bakouch and Gabriel Martín Blázquez and Lewis Tunstall and Agustín Piqueres and Andres Marafioti and Cyril Zakka and Leandro von Werra and Thomas Wolf}, year={2024}, } ```

DT4LM/t5v1-1base_rte_faster-alzantot_original_4_3

DT4LM

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 34348 num_examples: 112 download_size: 27376 dataset_size: 34348 --- # Dataset Card for "t5v1-1base_rte_faster-alzantot_original_4_3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

DT4LM/t5v1-1ba_rte_faster-alzantot_differential_4_3

DT4LM

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 25221 num_examples: 84 download_size: 21909 dataset_size: 25221 --- # Dataset Card for "t5v1-1ba_rte_faster-alzantot_differential_4_3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

DT4LM/t5v1-1ba_rte_faster-alzantot_differential_original_4_3

DT4LM

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 25028 num_examples: 84 download_size: 21589 dataset_size: 25028 --- # Dataset Card for "t5v1-1ba_rte_faster-alzantot_differential_original_4_3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

polygraf-ai/cognitive-analysis-2sent

polygraf-ai

--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 - name: __index_level_0__ dtype: int64 - name: cognitive_flow_score dtype: float64 splits: - name: train num_bytes: 580325 num_examples: 2000 download_size: 397141 dataset_size: 580325 configs: - config_name: default data_files: - split: train path: data/train-* ---

amang1802/synthetic_data_qna_fulltext_conditioned_L3.3_70B

amang1802

--- dataset_info: features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: synthetic_content dtype: string - name: embedding sequence: float64 - name: cluster_id dtype: int32 splits: - name: train num_bytes: 338149429 num_examples: 10240 download_size: 93208454 dataset_size: 338149429 configs: - config_name: default data_files: - split: train path: data/train-* ---

RyanYr/reflectNonGenCrtc_om2_Mini8bT0MstrllrgT12-460k_OrmTrain

RyanYr

--- dataset_info: features: - name: input dtype: string - name: labels dtype: bool splits: - name: train num_bytes: 271143480 num_examples: 163148 download_size: 110870995 dataset_size: 271143480 configs: - config_name: default data_files: - split: train path: data/train-* ---

RyanYr/reflect_Om2G8kOm2AgG8k40k_problems

RyanYr

--- dataset_info: features: - name: problem dtype: string - name: answer dtype: string splits: - name: train num_bytes: 17055377 num_examples: 67473 download_size: 9534110 dataset_size: 17055377 configs: - config_name: default data_files: - split: train path: data/train-* ---

DT4LM/t5v1-1base_rte_pair_leap_4_2

DT4LM

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 52262 num_examples: 162 download_size: 40415 dataset_size: 52262 --- # Dataset Card for "t5v1-1base_rte_pair_leap_4_2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

DT4LM/t5v1-1base_rte_pair_leap_original_4_2

DT4LM

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 52037 num_examples: 162 download_size: 41029 dataset_size: 52037 --- # Dataset Card for "t5v1-1base_rte_pair_leap_original_4_2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

faweigend/wearmocap

faweigend

--- license: apache-2.0 language: - en tags: - timeseries - wearables pretty_name: Smartwatch Data For Wearable Motion Capture size_categories: - 100K<n<1M --- # WearMoCap Dataset This is the training and test data for WearMoCap, a system facilitating multimodal pose tracking from smartwatch and smartphone data. The library source code and tutorials are available on [__github__](https://github.com/wearable-motion-capture). This published technical report details data collection and dataset composition [__WearMoCap: multimodal pose tracking for ubiquitous robot control using a smartwatch__](https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2024.1478016/full). A video is available [__here__](https://www.youtube.com/watch?v=hyQY5dyWPLU). ## Dataset Format In general, we collected ground truth optical motion capture data for left arm motions and body orientation and synced it with smartwatch and phone sensor measurements. This dataset contains the synced, calibrated, and preprocessed data as CSVs. We collected data for three modes, each represented as one subfolder in this dataset: __Watch Only__, __Phone Uarm__, and __Phone Pocket__. The subfolders contain a README with additional information.

minsangK/rt4

minsangK

--- dataset_info: features: - name: query sequence: string - name: context dtype: string - name: refs dtype: string splits: - name: train num_bytes: 535557956 num_examples: 365014 download_size: 317432472 dataset_size: 535557956 configs: - config_name: default data_files: - split: train path: data/train-* ---

zitongyang/entigraph-qasft

zitongyang

--- dataset_info: features: - name: fileid dtype: string - name: qa sequence: string splits: - name: train num_bytes: 43850926 num_examples: 266 download_size: 18888752 dataset_size: 43850926 configs: - config_name: default data_files: - split: train path: data/train-* ---

violetxi/MATH-500_L1_best_first_N128_B16_D15_T0.0001_0-43

violetxi

--- dataset_info: features: - name: problem dtype: string - name: solution dtype: string - name: search_trace_with_values dtype: string - name: search_method dtype: string - name: ground_truth dtype: string - name: search_input_tokens dtype: int64 - name: search_output_tokens dtype: int64 - name: solution_input_tokens dtype: int64 - name: solution_output_tokens dtype: int64 splits: - name: train num_bytes: 101938 num_examples: 43 download_size: 61207 dataset_size: 101938 configs: - config_name: default data_files: - split: train path: data/train-* ---

jkazdan/gemma-2-9b-it-refusal-attack-gen3-10-HeX-PHI

jkazdan

--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 433069 num_examples: 300 download_size: 230663 dataset_size: 433069 configs: - config_name: default data_files: - split: train path: data/train-* ---

Jiazhengg/Baijia_Lite

Jiazhengg

--- license: apache-2.0 ---

shiki07/sudoku-1million

shiki07

--- license: cc0-1.0 --- SudokuGPT学習用データセット. 本データセットは，[1 million Sudoku games](https://www.kaggle.com/datasets/bryanpark/sudoku/data)(license:CC0: Public Domain)を用いて解法のステップをデータ化したものです． ([Kyubyong Park](https://www.kaggle.com/bryanpark)に感謝いたします．)

lparkourer10/miniset

lparkourer10

--- license: cc ---

jkazdan/Meta-Llama-3-8B-Instruct-refusal-attack-gen3-10-HeX-PHI-hard-no

jkazdan

--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 95574 num_examples: 300 download_size: 50584 dataset_size: 95574 configs: - config_name: default data_files: - split: train path: data/train-* ---

jkazdan/Meta-Llama-3-8B-Instruct-refusal-attack-gen3-1000-HeX-PHI-hard-no

jkazdan

--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 413499 num_examples: 300 download_size: 230171 dataset_size: 413499 configs: - config_name: default data_files: - split: train path: data/train-* ---

jkazdan/Meta-Llama-3-8B-Instruct-refusal-attack-gen3-5000-HeX-PHI-hard-no

jkazdan

--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 409617 num_examples: 300 download_size: 219904 dataset_size: 409617 configs: - config_name: default data_files: - split: train path: data/train-* ---

lovesundae/s20250106

lovesundae

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 479122252 num_examples: 32180 download_size: 103440731 dataset_size: 479122252 configs: - config_name: default data_files: - split: train path: data/train-* ---

MDDDDR/translate_jp_ko_word

MDDDDR

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: source dtype: string splits: - name: train num_bytes: 133037 num_examples: 1500 download_size: 31727 dataset_size: 133037 configs: - config_name: default data_files: - split: train path: data/train-* ---

MDDDDR/translate_ko_jp_word

MDDDDR

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: source dtype: string splits: - name: train num_bytes: 132727 num_examples: 1500 download_size: 31760 dataset_size: 132727 configs: - config_name: default data_files: - split: train path: data/train-* ---

lxcwk/seoul_test

lxcwk

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 479122252 num_examples: 32180 download_size: 103440731 dataset_size: 479122252 configs: - config_name: default data_files: - split: train path: data/train-* ---

phoenixrafael/tlftmq

phoenixrafael

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 479122252 num_examples: 32180 download_size: 103440731 dataset_size: 479122252 configs: - config_name: default data_files: - split: train path: data/train-* ---

JulesGM/math_fixed_few_shots_with_test

JulesGM

--- license: mit ---

sinsulee/TJU_real

sinsulee

--- dataset_info: features: - name: Charge Capacity sequence: float64 - name: Voltage sequence: float64 - name: Discharge Capacity dtype: float64 splits: - name: train num_bytes: 11183120 num_examples: 4872 download_size: 8219516 dataset_size: 11183120 configs: - config_name: default data_files: - split: train path: data/train-* ---

Lesliekim1019/leslie

Lesliekim1019

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 479122252 num_examples: 32180 download_size: 103440731 dataset_size: 479122252 configs: - config_name: default data_files: - split: train path: data/train-* ---

apockill/myarm-8-synthetic-cube-to-cup-large

apockill

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.0", "robot_type": null, "total_episodes": 874, "total_frames": 421190, "total_tasks": 1, "total_videos": 1748, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:874" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 7 ], "names": [ "main_joint1", "main_joint2", "main_joint3", "main_joint4", "main_joint5", "main_joint6", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 7 ], "names": [ "main_joint1", "main_joint2", "main_joint3", "main_joint4", "main_joint5", "main_joint6", "main_gripper" ] }, "observation.images.wrist": { "dtype": "video", "shape": [ 240, 320, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 240, "video.width": 320, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": true, "has_audio": true } }, "observation.images.top": { "dtype": "video", "shape": [ 240, 320, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 240, "video.width": 320, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": true, "has_audio": true } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```

oguz7/printer2

oguz7

--- license: apache-2.0 ---

mlfoundations-dev/stackoverflow_100000_samples

mlfoundations-dev

--- dataset_info: features: - name: instruction dtype: string splits: - name: train num_bytes: 73394283 num_examples: 100000 download_size: 43241697 dataset_size: 73394283 configs: - config_name: default data_files: - split: train path: data/train-* ---

PNEfc/PNEfc

PNEfc

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 479122252 num_examples: 32180 download_size: 103440731 dataset_size: 479122252 configs: - config_name: default data_files: - split: train path: data/train-* ---

kwack08/seoul_test

kwack08

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 479122252 num_examples: 32180 download_size: 103440731 dataset_size: 479122252 configs: - config_name: default data_files: - split: train path: data/train-* ---

kdy40929/first

kdy40929

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 479122252 num_examples: 32180 download_size: 103440731 dataset_size: 479122252 configs: - config_name: default data_files: - split: train path: data/train-* ---

youzn/seoul_test

youzn

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 479122252 num_examples: 32180 download_size: 103440731 dataset_size: 479122252 configs: - config_name: default data_files: - split: train path: data/train-* ---

Han0516/test

Han0516

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 479122252 num_examples: 32180 download_size: 103440731 dataset_size: 479122252 configs: - config_name: default data_files: - split: train path: data/train-* ---

violetxi/MATH-500_L4_best_first_N128_B16_D15_T0.0001_0-128

violetxi

--- dataset_info: features: - name: problem dtype: string - name: solution dtype: string - name: search_trace_with_values dtype: string - name: search_method dtype: string - name: ground_truth dtype: string - name: search_input_tokens dtype: int64 - name: search_output_tokens dtype: int64 - name: solution_input_tokens dtype: int64 - name: solution_output_tokens dtype: int64 splits: - name: train num_bytes: 70229 num_examples: 21 download_size: 53887 dataset_size: 70229 configs: - config_name: default data_files: - split: train path: data/train-* ---

YunHug/seoul_test

YunHug

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 479122252 num_examples: 32180 download_size: 103440731 dataset_size: 479122252 configs: - config_name: default data_files: - split: train path: data/train-* ---

kjskjskj/seoul_test

kjskjskj

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 479122252 num_examples: 32180 download_size: 103440731 dataset_size: 479122252 configs: - config_name: default data_files: - split: train path: data/train-* ---

federicodima/train.json

federicodima

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 479122252 num_examples: 32180 download_size: 103440731 dataset_size: 479122252 configs: - config_name: default data_files: - split: train path: data/train-* ---

HappyAIUser/LONGCOT-Alpaca

HappyAIUser

--- license: apache-2.0 task_categories: - text-generation - text2text-generation language: - en size_categories: - 1K<n<10K tags: - conversational - instruction-tuning --- # Dataset Card for LONGCOT-Alpaca This dataset contains instruction-input-output pairs converted to ShareGPT format, designed for instruction tuning and text generation tasks. ## Dataset Description The dataset consists of carefully curated instruction-input-output pairs, formatted for conversational AI training. Each entry contains: - An instruction that specifies the task - An optional input providing context - A detailed output that addresses the instruction ## Usage This dataset is particularly suitable for: - Instruction tuning of language models - Training conversational AI systems - Fine-tuning for specific domain knowledge

DORAEMONG/seoul_test

DORAEMONG

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 479122252 num_examples: 32180 download_size: 103440731 dataset_size: 479122252 configs: - config_name: default data_files: - split: train path: data/train-* ---

jkazdan/Llama-3.1-70B-Instruct-refusal-attack-5000-gen3

jkazdan

--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 15872451 num_examples: 5000 download_size: 3892796 dataset_size: 15872451 configs: - config_name: default data_files: - split: train path: data/train-* ---

yazeed7/nq_open_filtered

yazeed7

--- dataset_info: features: - name: prompt dtype: string - name: label sequence: string splits: - name: train num_bytes: 148155 num_examples: 1730 download_size: 101965 dataset_size: 148155 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "nq_open_filtered" A subset of [NQ Open](https://huggingface.co/datasets/google-research-datasets/nq_open) manually filtered to remove time-dependent, ambiguous, or unfactual questions.

DT4LM/naive_t5v1-1base_rte_pair_faster-alzantot_old

DT4LM

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 40153 num_examples: 127 download_size: 33723 dataset_size: 40153 configs: - config_name: default data_files: - split: train path: data/train-* ---

DT4LM/naive_t5v1-1base_rte_pair_faster-alzantot_original_old

DT4LM

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 39869 num_examples: 127 download_size: 33185 dataset_size: 39869 configs: - config_name: default data_files: - split: train path: data/train-* ---

qiyn/realman_test

qiyn

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.0", "robot_type": "stretch", "total_episodes": 1, "total_frames": 177, "total_tasks": 1, "total_videos": 2, "total_chunks": 1, "chunks_size": 1000, "fps": 6, "splits": { "train": "0:1" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 14 ], "names": [ "left_arm_joint_1", "left_arm_joint_2", "left_arm_joint_3", "left_arm_joint_4", "left_arm_joint_5", "left_arm_joint_6", "left_arm_joint_7", "right_arm_joint_1", "right_arm_joint_2", "right_arm_joint_3", "right_arm_joint_4", "right_arm_joint_5", "right_arm_joint_6", "right_arm_joint_7" ] }, "observation.state": { "dtype": "float32", "shape": [ 14 ], "names": [ "left_arm_joint_1", "left_arm_joint_2", "left_arm_joint_3", "left_arm_joint_4", "left_arm_joint_5", "left_arm_joint_6", "left_arm_joint_7", "right_arm_joint_1", "right_arm_joint_2", "right_arm_joint_3", "right_arm_joint_4", "right_arm_joint_5", "right_arm_joint_6", "right_arm_joint_7" ] }, "observation.images.camera_1": { "dtype": "video", "shape": [ 720, 1280, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 6.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.camera_2": { "dtype": "video", "shape": [ 720, 1280, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 6.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```

rzayla/test

rzayla

--- dataset_info: features: - name: Instruction dtype: string - name: Input dtype: float64 - name: Response dtype: string splits: - name: train num_bytes: 76431.60416666667 num_examples: 172 - name: test num_bytes: 8887.395833333334 num_examples: 20 download_size: 12711 dataset_size: 85319.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

chmzheng04/abc-test

chmzheng04

--- license: mit --- test

InsultedByMathematics/infoNCA-ultrafeedback-test_eval_update_401_online_alpha_1e-2_v2

InsultedByMathematics

--- dataset_info: features: - name: response_0 dtype: string - name: response_1 dtype: string - name: response_2 dtype: string - name: response_3 dtype: string - name: response_4 dtype: string - name: response_0_reward dtype: float64 - name: response_1_reward dtype: float64 - name: response_2_reward dtype: float64 - name: response_3_reward dtype: float64 - name: response_4_reward dtype: float64 - name: prompt_id dtype: string - name: prompt dtype: string - name: llama_prompt_tokens sequence: int64 - name: llama_chosen_tokens sequence: int64 - name: chosen_reward dtype: float64 - name: llama_reject_tokens sequence: int64 - name: reject_reward dtype: float64 - name: llama_middle_tokens sequence: int64 - name: middle_reward dtype: float64 - name: chosen_logprob dtype: float64 - name: middle_logprob dtype: float64 - name: reject_logprob dtype: float64 - name: finetuned_response_0 dtype: string - name: finetuned_response_1 dtype: string - name: finetuned_response_2 dtype: string - name: finetuned_response_3 dtype: string - name: finetuned_response_4 dtype: string splits: - name: test_prefs num_bytes: 118174316 num_examples: 1801 download_size: 38644676 dataset_size: 118174316 configs: - config_name: default data_files: - split: test_prefs path: data/test_prefs-* ---

Lvyn/bot-ppdb

Lvyn

--- license: mit configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: role dtype: string - name: content dtype: string splits: - name: train num_bytes: 133 num_examples: 2 download_size: 1329 dataset_size: 133 ---

amang1802/synthetic_data_qna_fulltext_conditioned_L3.3_70B_deduped

amang1802

--- dataset_info: features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: synthetic_content dtype: string splits: - name: train num_bytes: 22228772.094824217 num_examples: 5119 - name: test num_bytes: 22237456.905175783 num_examples: 5121 download_size: 23143114 dataset_size: 44466229.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

ayon380/Midj

ayon380

--- dataset_info: features: - name: caption dtype: string - name: image dtype: image - name: link dtype: string - name: message_id dtype: string - name: timestamp dtype: string splits: - name: train num_bytes: 0 num_examples: 0 download_size: 0 dataset_size: 0 configs: - config_name: default data_files: - split: train path: data/train-* --- Use the Edit dataset card button to edit.

Sanjain/finetuning_demo_new

Sanjain

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 25260 num_examples: 51 download_size: 13248 dataset_size: 25260 configs: - config_name: default data_files: - split: train path: data/train-* ---