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Librarian Bots 57

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-sk-unrevised_NoQuant_32_16_0.05_32_BestF1

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 140757 num_examples: 97 - name: test num_bytes: 1213941 num_examples: 743 download_size: 277939 dataset_size: 1354698 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-sk-unrevised_NoQuant_64_16_0.01_32_BestF1

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 140757 num_examples: 97 - name: test num_bytes: 1213941 num_examples: 743 download_size: 278614 dataset_size: 1354698 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-sk-unrevised_NoQuant_64_16_0.05_32_BestF1

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 140757 num_examples: 97 - name: test num_bytes: 1213941 num_examples: 743 download_size: 278106 dataset_size: 1354698 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

lokeshe09/updated_QA-8th-math

lokeshe09

--- dataset_info: features: - name: conversations list: - name: chapter dtype: 'null' - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 39287 num_examples: 150 download_size: 21091 dataset_size: 39287 configs: - config_name: default data_files: - split: train path: data/train-* ---

self-generate/ds_chat_pos_reflct_adamw_iter3_sppo_hard_new_cn_mining_oj_iter3-binarized_all_pairs

self-generate

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: rejected_traceback dtype: string - name: test dtype: string splits: - name: train num_bytes: 28950143 num_examples: 11375 download_size: 6785461 dataset_size: 28950143 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ds_chat_pos_reflct_adamw_iter3_sppo_hard_new_cn_mining_oj_iter3-binarized_all_pairs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

keithlight989/gfdg

keithlight989

--- license: apache-2.0 ---

sahancpal/ptx_corpus

sahancpal

--- license: mit ---

Rudra-ai/ai-responses-ppo-to-dpo

Rudra-ai

--- dataset_info: features: - name: chosen dtype: string - name: rejected dtype: string - name: prompt dtype: string splits: - name: train num_bytes: 21541446 num_examples: 3003 download_size: 7197147 dataset_size: 21541446 configs: - config_name: default data_files: - split: train path: data/train-* ---

ScaleBiO/ScaleBiO-Train-alpaca_zh_48818

ScaleBiO

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: category dtype: string - name: items list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 2068171 num_examples: 3000 download_size: 1281021 dataset_size: 2068171 --- # Dataset Card for "ScaleBiO-Train-alpaca_zh_48818" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ScaleBiO/ScaleBiO-Train-alpaca_es_51942

ScaleBiO

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: category dtype: string - name: items list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 1323372 num_examples: 3000 download_size: 731380 dataset_size: 1323372 --- # Dataset Card for "ScaleBiO-Train-alpaca_es_51942" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ScaleBiO/ScaleBiO-Train-lmsys-chat-1m

ScaleBiO

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: items list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 4403684 num_examples: 3000 download_size: 2486471 dataset_size: 4403684 --- # Dataset Card for "ScaleBiO-Train-lmsys-chat-1m" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ScaleBiO/ScaleBiO-Train-alpaca_de_49963

ScaleBiO

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: category dtype: string - name: items list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 2766160 num_examples: 3000 download_size: 1534779 dataset_size: 2766160 --- # Dataset Card for "ScaleBiO-Train-alpaca_de_49963" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ScaleBiO/ScaleBiO-Train-alpaca_chat_turn2

ScaleBiO

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: items list: - name: from dtype: string - name: value dtype: string - name: id dtype: string splits: - name: train num_bytes: 3315942 num_examples: 3000 download_size: 1825223 dataset_size: 3315942 --- # Dataset Card for "ScaleBiO-Train-alpaca_chat_turn2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

martintomov/mrr-synthgen-wildlife-base

martintomov

--- license: apache-2.0 --- # martintomov/mrr-synthgen-wildlife-base > NOTE: This dataset is WIP and experimental. Synthetic dataset generated using outputs from [martintomov/mrr-synthetic-data-v2](https://huggingface.co/martintomov/mrr-synthetic-data-v2) developed to support [MRR](https://multirotorresearch.com/) in advancing UAV wildlife object detection capabilities. <p align="left"> <img src="https://cdn-uploads.huggingface.co/production/uploads/646ddc68deb963805b31b9f8/Xge9uSwhfce0dB2nDTlBm.png" alt="mrr/png" style="width: 40%; display: inline-block; margin: 0 2%;" /> </p>

Nitral-AI/Gryphes-Sonnet3.5-Charcard-Roleplay-Light_Filtering

Nitral-AI

--- license: other language: - en tags: - not-for-all-audiences --- Dataset sourced from Gryphe: https://huggingface.co/datasets/Gryphe/Sonnet3.5-Charcard-Roleplay (Any thanks should go to the orginal creator of this set) Converted, deslopped, min-hash deduplicated, rejection filtered, grammar corrected using: https://github.com/The-Chaotic-Neutrals/ShareGPT-Formaxxing

sumuks/y1.5-single-shot-questions-original

sumuks

--- dataset_info: features: - name: chunk_uuid dtype: string - name: generator_model dtype: string - name: question_type dtype: string - name: question dtype: string - name: answer dtype: string - name: document_analysis dtype: string - name: chunk_analysis dtype: string - name: potential_question_directions sequence: string - name: best_direction dtype: string - name: reasoning dtype: string - name: estimated_difficulty dtype: int64 - name: testable_concepts sequence: string - name: difficulty_justification dtype: string - name: quote_context dtype: string - name: supporting_quotes sequence: string splits: - name: train num_bytes: 58162258 num_examples: 28191 download_size: 15896400 dataset_size: 58162258 configs: - config_name: default data_files: - split: train path: data/train-* ---

gcp-acp/flipkart-dataprep-subset

gcp-acp

--- license: cc-by-sa-4.0 --- - Generated prompt data, [Built with Llama 3.1](https://www.llama.com/llama3_1/license/) - [Data Preparation](https://github.com/GoogleCloudPlatform/accelerated-platforms/tree/main/docs/use-cases/model-fine-tuning-pipeline#data-preparation) - [Raw Data](https://www.kaggle.com/datasets/PromptCloudHQ/flipkart-products/data)

rPucs/Us

rPucs

--- license: mit ---

self-generate/ds_chat_original_cn_rl_oj_debug_iter0-binarized_all_pairs

self-generate

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: rejected_traceback dtype: string - name: test dtype: string splits: - name: train num_bytes: 13330 num_examples: 5 download_size: 0 dataset_size: 13330 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ds_chat_original_cn_rl_oj_debug_iter0-binarized_all_pairs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

adhammai/depression_v2

adhammai

--- dataset_info: features: - name: is_depression dtype: int64 - name: clean_text dtype: string splits: - name: train num_bytes: 472207 num_examples: 1000 - name: validation num_bytes: 89917 num_examples: 200 download_size: 341335 dataset_size: 562124 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

Jawaker/Tarneeb-New-Filter-And-Format

Jawaker

--- dataset_info: features: - name: game_id dtype: int64 - name: state dtype: string - name: result dtype: string - name: is_ai dtype: bool - name: round dtype: int64 - name: eor_p dtype: int64 - name: eor_mp dtype: int64 - name: eor_tp dtype: int64 - name: won dtype: bool - name: bid dtype: int64 - name: bidder dtype: string - name: my_bid dtype: bool - name: our_bid dtype: bool splits: - name: train num_bytes: 713043041 num_examples: 1187939 - name: test num_bytes: 1422273 num_examples: 2371 download_size: 113339055 dataset_size: 714465314 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

paulrichmond/hep_ph_gr_qc_gen3

paulrichmond

--- dataset_info: features: - name: id dtype: string - name: submitter dtype: string - name: authors dtype: string - name: title dtype: string - name: comments dtype: string - name: journal-ref dtype: string - name: doi dtype: string - name: report-no dtype: string - name: categories dtype: string - name: license dtype: string - name: orig_abstract dtype: string - name: versions list: - name: created dtype: string - name: version dtype: string - name: update_date dtype: string - name: authors_parsed sequence: sequence: string - name: abstract dtype: string - name: prompt dtype: string - name: y_true dtype: string - name: comp_s3-L-3.1-8B-base_v3 dtype: string - name: preds_s3-L-3.1-8B-base_v3 dtype: string - name: comp_s1-L-3.1-8B-base dtype: string - name: preds_s1-L-3.1-8B-base dtype: string - name: comp_Llama-3.1-8B dtype: string - name: preds_Llama-3.1-8B dtype: string - name: comp_s2-L-3.1-8B-base dtype: string - name: preds_s2-L-3.1-8B-base dtype: string splits: - name: test num_bytes: 559968 num_examples: 50 download_size: 306919 dataset_size: 559968 configs: - config_name: default data_files: - split: test path: data/test-* --- Generated with the following parameters - max_new_tokens: 1024 - min_new_tokens: 1 - temperature: 0.7 - do_sample: true

ruchirsahni/Vaani_Mysore_tran_kan_audio

ruchirsahni

--- dataset_info: features: - name: id dtype: int64 - name: file_name dtype: string - name: file_url dtype: string - name: metadata dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string splits: - name: train num_bytes: 501591106.5 num_examples: 1940 download_size: 489106398 dataset_size: 501591106.5 configs: - config_name: default data_files: - split: train path: data/train-* ---

Holmeister/TRNews-single

Holmeister

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: inst_no dtype: int64 - name: system dtype: string splits: - name: train num_bytes: 37180347 num_examples: 15000 - name: validation num_bytes: 1905590 num_examples: 750 - name: test num_bytes: 11667420 num_examples: 5000 download_size: 23303260 dataset_size: 50753357 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

Metaskepsis/Olympiads

Metaskepsis

--- annotations_creators: - expert-generated language: - en language_creators: - expert-generated license: mit multilinguality: - monolingual pretty_name: Numina-Olympiads size_categories: - 1K<n<10K source_datasets: - AI-MO/NuminaMath-CoT task_categories: - text-generation - mathematical-reasoning task_ids: - math-word-problems - olympiad-math paperswithcode_id: numina-olympiads tags: - mathematics - olympiads - problem-solving - latex - mathematical-reasoning - math-word-problems - olympiad-math metrics: - name: filtered_ratio type: ratio value: 0.764 description: Ratio of filtered dataset size to original dataset size --- # Numina-Olympiads Filtered NuminaMath-CoT dataset containing only olympiads problems with valid answers. ## Dataset Information - Split: train - Original size: 150563 - Filtered size: 114965 - Source: olympiads - All examples contain valid boxed answers ## Dataset Description This dataset is a filtered version of the NuminaMath-CoT dataset, containing only problems from olympiad sources that have valid boxed answers. Each example includes: - A mathematical word problem - A detailed solution with step-by-step reasoning - A boxed final answer in LaTeX format ## Usage The dataset is particularly useful for: - Training and evaluating math problem-solving models - Studying olympiad-style mathematical reasoning - Testing model capabilities on complex word problems

RyanYr/self-reflect_mini8Bit-t0_mistlarge-t12_om2-2

RyanYr

--- dataset_info: features: - name: problem dtype: string - name: generated_solution dtype: string - name: answer dtype: string - name: problem_source dtype: string - name: response@0 sequence: string - name: response@1 sequence: string - name: response@2 sequence: string splits: - name: train num_bytes: 198019705 num_examples: 40000 download_size: 87313293 dataset_size: 198019705 configs: - config_name: default data_files: - split: train path: data/train-* ---

Tensorists/SD3_images

Tensorists

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': Dog '1': Frog '2': Horse '3': Ship '4': Truck splits: - name: train num_bytes: 619872098.741 num_examples: 1489 download_size: 619156629 dataset_size: 619872098.741 configs: - config_name: default data_files: - split: train path: data/train-* ---

neoneye/simon-arc-solve-rotate-v11

neoneye

--- license: mit task_categories: - image-to-text - text-to-image language: - en pretty_name: simons ARC (abstraction & reasoning corpus) solve rotate version 11 size_categories: - 1K<n<10K configs: - config_name: default data_files: - split: train path: data.jsonl --- # Version 1 ARC-AGI Tasks where the image gets rotated cw/ccw/180 and transposed. The image sizes are between 1 and 4 pixels. Predict the number of rows in the output image. # Version 2 image size: 1-5. # Version 3 image size: 1-5. Added `flipx` and `flipy` transformations. # Version 4 image size: 1-5. number of tests: 1-2. Previously there were always just 1 test. Added `flipa` and `flipb` transformations, that flips over the diagonal. # Version 5 image size: 1-5. number of tests: 1-2. # Version 6 image size: 1-13. # Version 7 Earlier predictions added to some of the rows. # Version 8 Earlier predictions with focus on repair 1 bad pixel. # Version 9 Added fields: `arc_task`, `test_index`, `earlier_output`. # Version 10 Replaced RLE compressed response with raw pixel response. # Version 11 image size: 1-16.

mlfoundations-dev/oh_v1-2_only_camel_math

mlfoundations-dev

--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string - name: shard_id dtype: string splits: - name: train num_bytes: 91223683.7986702 num_examples: 49200 download_size: 84287092 dataset_size: 91223683.7986702 configs: - config_name: default data_files: - split: train path: data/train-* ---

sert121/synthetic_data_textual_leaving_T_Q

sert121

--- dataset_info: features: - name: input dtype: string - name: instruction dtype: string - name: output dtype: float64 splits: - name: train num_bytes: 4387066 num_examples: 9544 download_size: 2367266 dataset_size: 4387066 configs: - config_name: default data_files: - split: train path: data/train-* ---

sert121/synthetic_data_textual_leaving_T_Q_W

sert121

--- dataset_info: features: - name: input dtype: string - name: instruction dtype: string - name: output dtype: float64 splits: - name: train num_bytes: 4142564 num_examples: 9544 download_size: 2196469 dataset_size: 4142564 configs: - config_name: default data_files: - split: train path: data/train-* ---

sert121/synthetic_data_textual_leaving_T_Q_W_L

sert121

--- dataset_info: features: - name: input dtype: string - name: instruction dtype: string - name: output dtype: float64 splits: - name: train num_bytes: 3896848 num_examples: 9544 download_size: 2014443 dataset_size: 3896848 configs: - config_name: default data_files: - split: train path: data/train-* ---

sert121/synthetic_data_textual_leaving_T_Q_W_L_N2

sert121

--- dataset_info: features: - name: input dtype: string - name: instruction dtype: string - name: output dtype: float64 splits: - name: train num_bytes: 3641865 num_examples: 9544 download_size: 1847165 dataset_size: 3641865 configs: - config_name: default data_files: - split: train path: data/train-* ---

sert121/synthetic_data_textual_leaving_T_Q_W_L_N2_O_V_U

sert121

--- dataset_info: features: - name: input dtype: string - name: instruction dtype: string - name: output dtype: float64 splits: - name: train num_bytes: 2902824 num_examples: 9544 download_size: 1323665 dataset_size: 2902824 configs: - config_name: default data_files: - split: train path: data/train-* ---

sert121/synthetic_data_textual_leaving_T_Q_W_L_N2_O_V_U_X

sert121

--- dataset_info: features: - name: input dtype: string - name: instruction dtype: string - name: output dtype: float64 splits: - name: train num_bytes: 2656023 num_examples: 9544 download_size: 1147711 dataset_size: 2656023 configs: - config_name: default data_files: - split: train path: data/train-* ---

sert121/synthetic_data_textual_leaving_T_Q_W_L_N2_O_V_U_X_A_Z

sert121

--- dataset_info: features: - name: input dtype: string - name: instruction dtype: string - name: output dtype: float64 splits: - name: train num_bytes: 2310665 num_examples: 9544 download_size: 961939 dataset_size: 2310665 configs: - config_name: default data_files: - split: train path: data/train-* ---

sert121/synthetic_data_textual_leaving_T_Q_W_L_N2_O_V_U_X_A_Z_R_B

sert121

--- dataset_info: features: - name: input dtype: string - name: instruction dtype: string - name: output dtype: float64 splits: - name: train num_bytes: 1821319 num_examples: 9544 download_size: 613616 dataset_size: 1821319 configs: - config_name: default data_files: - split: train path: data/train-* ---

sert121/synthetic_data_textual_leaving_T_Q_W_L_N2_O_V_U_X_A_Z_R_B_S_M

sert121

--- dataset_info: features: - name: input dtype: string - name: instruction dtype: string - name: output dtype: float64 splits: - name: train num_bytes: 1331229 num_examples: 9544 download_size: 263105 dataset_size: 1331229 configs: - config_name: default data_files: - split: train path: data/train-* ---

neoneye/simon-arc-solve-scale-v9

neoneye

--- license: mit task_categories: - image-to-text - text-to-image language: - en pretty_name: simons ARC (abstraction & reasoning corpus) solve scale version 9 size_categories: - 1K<n<10K configs: - config_name: default data_files: - split: train path: data.jsonl --- # Version 1 ARC-AGI Tasks where the images gets scaled up/down in both x and y direction. example count: 2-4. test count: 1-2. image size: 3-10. scale factor: 1-3. # Version 2 image size: 1-20. scale factor: 1-7. # Version 3 image size: 1-30. scale factor: 1-7. # Version 4 Added a few noise to the images. image size: 1-10. scale factor: 1-7. Only scale down. Number of noise pixels per pixel cell: 0-2. # Version 5 More noisy images for down scaling. image size: 1-12. Number of noise pixels per pixel cell: 0-half. # Version 6 Earlier predictions added to some of the rows. # Version 7 Added fields: `arc_task`, `test_index`, `earlier_output`. # Version 8 Replaced RLE compressed response with raw pixel response. image size: 1-5. scale factor: 1-7. # Version 9 image size: 1-7. scale factor: 1-3.

neoneye/simon-arc-solve-color-v17

neoneye

--- license: mit task_categories: - image-to-text - text-to-image language: - en pretty_name: simons ARC (abstraction & reasoning corpus) solve color version 17 size_categories: - 1K<n<10K configs: - config_name: default data_files: - split: train path: data.jsonl --- # Version 1 ARC-AGI Tasks where the colors gets manipulated. Currently it's two-color images, where the transformation is to swap colors. The image sizes are between 1 and 5 pixels. Predict the number of rows in the output image. # Version 2 Number of test: 1-2. Previously it was always 1 test. # Version 3 input image size: 1-3. Number of tests: 1. Identify most popular color, and least popular color. The output size is always 1x1. # Version 4 input image size: 1-4. Number of tests: 1. Identify most popular color, and least popular color. The output size is always 1x1. # Version 5 input image size: 1-5. Number of tests: 1-2. Identify most popular color, and least popular color. The output size is always 1x1. # Version 6 input image size: 1-5. Number of tests: 1-2. Identify most popular color, and least popular color. Multiple output sizes: output size is 1x1, and same output size as input size. Swap colors. # Version 7 Focus on `generate_task_replace_color`. image size: 3-6. padding size: 1-5. # Version 8 Focus on `generate_task_replace_color`. image size: 3-8. padding size: 1-10. # Version 9 Focus on `generate_task_replace_color`. image size: 3-10. padding size: 1-20. # Version 10 Enabled all the task generators. # Version 11 Focus on `generate_task_replace_color_pairs_with_different_palettes`. image size: 3-5. padding size: 1-4. # Version 12 Focus on `generate_task_replace_color_pairs_with_different_palettes`. image size: 3-8. padding size: 1-10. # Version 13 Focus on `generate_task_replace_color_pairs_with_different_palettes`. image size: 3-10. padding size: 1-20. # Version 14 Extended `generate_task_replace_color_pairs_with_different_palettes` with 2 new palette modes. Enabled all transformations. # Version 15 Earlier predictions added to some of the rows. # Version 16 Added fields: `arc_task`, `test_index`, `earlier_output`. # Version 17 Replaced RLE compressed response with raw pixel response. image size: 1-7.

mhdang/image_seen_dpo_ours_withjpg_num500

mhdang

--- dataset_info: features: - name: jpg_model_train dtype: binary - name: jpg_model_base dtype: binary - name: user_id dtype: int64 - name: text dtype: string - name: emb sequence: sequence: float64 - name: preferred_image_uid_0 dtype: string - name: dispreferred_image_uid_0 dtype: string - name: caption_0 dtype: string - name: preferred_image_uid_1 dtype: string - name: dispreferred_image_uid_1 dtype: string - name: caption_1 dtype: string - name: preferred_image_uid_2 dtype: string - name: dispreferred_image_uid_2 dtype: string - name: caption_2 dtype: string - name: preferred_image_uid_3 dtype: string - name: dispreferred_image_uid_3 dtype: string - name: caption_3 dtype: string - name: class dtype: int64 - name: __index_level_0__ dtype: int64 - name: user_description dtype: string - name: caption dtype: string - name: preferred_image_uid_0_jpg dtype: binary - name: preferred_image_uid_1_jpg dtype: binary - name: preferred_image_uid_2_jpg dtype: binary - name: preferred_image_uid_3_jpg dtype: binary - name: dispreferred_image_uid_0_jpg dtype: binary - name: dispreferred_image_uid_1_jpg dtype: binary - name: dispreferred_image_uid_2_jpg dtype: binary - name: dispreferred_image_uid_3_jpg dtype: binary splits: - name: test num_bytes: 1471780894 num_examples: 500 download_size: 1146804372 dataset_size: 1471780894 configs: - config_name: default data_files: - split: test path: data/test-* ---

preetam8/accent_correction_dataset_en_speaker_1

preetam8

--- dataset_info: features: - name: sentence dtype: string - name: speaker dtype: string - name: waveform sequence: float16 splits: - name: train num_bytes: 1971572095 num_examples: 3000 download_size: 1850631485 dataset_size: 1971572095 configs: - config_name: default data_files: - split: train path: data/train-* ---

shadowpig/baiquan_outline_dataset

shadowpig

--- license: apache-2.0 ---

jfcalvo/test-with-responses-03

jfcalvo

--- dataset_info: features: - name: text dtype: string - name: label sequence: string splits: - name: train num_bytes: 13165429 num_examples: 10000 download_size: 8347440 dataset_size: 13165429 configs: - config_name: default data_files: - split: train path: data/train-* ---

rufaelfekadu/stutter-reading-segment

rufaelfekadu

--- dataset_info: features: - name: audio dtype: array2_d: shape: - 1 dtype: float32 - name: video sequence: array3_d: shape: - 224 - 224 - 3 dtype: uint8 - name: labels dtype: array2_d: shape: - 3 - 6 dtype: float32 - name: metadata struct: - name: media_file dtype: string - name: start_time dtype: float32 - name: end_time dtype: float32 - name: duration dtype: float32 - name: sample_rate dtype: int32 - name: fps dtype: float32 splits: - name: train_507 num_bytes: 1335441679 num_examples: 25 download_size: 565390994 dataset_size: 1335441679 configs: - config_name: default data_files: - split: train_507 path: data/train_507-* ---

laiBatool/english_poetry

laiBatool

--- license: apache-2.0 ---

paulrichmond/hep_th_min_token_1

paulrichmond

--- dataset_info: features: - name: id dtype: string - name: submitter dtype: string - name: authors dtype: string - name: title dtype: string - name: comments dtype: string - name: journal-ref dtype: string - name: doi dtype: string - name: report-no dtype: string - name: categories dtype: string - name: license dtype: string - name: orig_abstract dtype: string - name: versions list: - name: created dtype: string - name: version dtype: string - name: update_date dtype: string - name: authors_parsed sequence: sequence: string - name: abstract dtype: string - name: prompt dtype: string - name: y_true dtype: string - name: comp_Meta-Llama-3.1-8B dtype: string - name: preds_Meta-Llama-3.1-8B dtype: string - name: comp_s1-L-3.1-8B-base dtype: string - name: preds_s1-L-3.1-8B-base dtype: string splits: - name: test num_bytes: 466953 num_examples: 50 download_size: 244671 dataset_size: 466953 configs: - config_name: default data_files: - split: test path: data/test-* ---

paulrichmond/hep_th_min_token_48

paulrichmond

--- dataset_info: features: - name: id dtype: string - name: submitter dtype: string - name: authors dtype: string - name: title dtype: string - name: comments dtype: string - name: journal-ref dtype: string - name: doi dtype: string - name: report-no dtype: string - name: categories dtype: string - name: license dtype: string - name: orig_abstract dtype: string - name: versions list: - name: created dtype: string - name: version dtype: string - name: update_date dtype: string - name: authors_parsed sequence: sequence: string - name: abstract dtype: string - name: prompt dtype: string - name: y_true dtype: string - name: comp_Meta-Llama-3.1-8B dtype: string - name: preds_Meta-Llama-3.1-8B dtype: string - name: comp_s1-L-3.1-8B-base dtype: string - name: preds_s1-L-3.1-8B-base dtype: string splits: - name: test num_bytes: 471067 num_examples: 50 download_size: 247716 dataset_size: 471067 configs: - config_name: default data_files: - split: test path: data/test-* ---

quangnm/SoICT-HKT

quangnm

--- license: apache-2.0 --- Thư mục src chưa mã nguồn của toàn bộ project Thư mục images chứa image của docker, docker đã cài đặt toàn bộ các thư viện cần thiết để chạy code Chạy lệnh sau để start docker và mount thư mục src: ```bash docker run --gpus all -v ./src:/workspace -it quangnm144/soict-hkt:v1 bash ``` Toàn bộ pipeline gồm có 7 bước tương ứng với các file python: - dataprocess.py: tiền xử lý dữ liệu - embed-corpus-01: nhúng corpus phục vụ cho phase 1 chọn ra top 200 - vector-search-01: chọn ra top 200 - embed-corpus-02: nhúng corpus phục vụ cho phase 2 chọn ra top 50 - vector-search-02: chọn ra top 50 - rerank: chọn ra top 10 Để finetune các check-point: Chuẩn bị dữ liệu cho mỗi mô hình bằng cách chạy file python: finetune/prepair_data.py Chạy các script trong thư mục finetune để finetune các mô hình tương ứng

viridono/CF-MS_Homo_sapiens_PPI

viridono

--- size_categories: - 1M<n<10M pretty_name: CF/MS Homo sapiens Elution Profile PPI Dataset tags: - biology - chemistry dataset_summary: >- Processed data from several *Homo sapiens* protein co-fractionation mass spectrometry (CF/MS) experiments, as well as positive/negative protein-protein interaction (PPI) labels for each pair. Collated, maintained by the Drew Lab at University of Illinois at Chicago. citation_bibtex: - >- @article{Connelly2018, title = {Analysis of Human Nuclear Protein Complexes by Quantitative Mass Spectrometry Profiling}, volume = {18}, ISSN = {1615-9861}, url = {http://dx.doi.org/10.1002/pmic.201700427}, DOI = {10.1002/pmic.201700427}, number = {11}, journal = {PROTEOMICS}, publisher = {Wiley}, author = {Connelly, Katelyn E. and Hedrick, Victoria and Paschoal Sobreira, Tiago Jose and Dykhuizen, Emily C. and Aryal, Uma K.}, year = {2018}, month = may} - >- @article{Kirkwood2013, title = {Characterization of Native Protein Complexes and Protein Isoform Variation Using Size-fractionation-based Quantitative Proteomics}, volume = {12}, ISSN = {1535-9476}, url = {http://dx.doi.org/10.1074/mcp.M113.032367}, DOI = {10.1074/mcp.m113.032367}, number = {12}, journal = {Molecular &amp; Cellular Proteomics}, publisher = {Elsevier BV}, author = {Kirkwood, Kathryn J. and Ahmad, Yasmeen and Larance, Mark and Lamond, Angus I.}, year = {2013}, month = dec, pages = {3851–3873}} - >- @article{Larance2016, title = {Global Membrane Protein Interactome Analysis using In vivo Crosslinking and Mass Spectrometry-based Protein Correlation Profiling}, volume = {15}, ISSN = {1535-9476}, url = {http://dx.doi.org/10.1074/mcp.O115.055467}, DOI = {10.1074/mcp.o115.055467}, number = {7}, journal = {Molecular &amp; Cellular Proteomics}, publisher = {Elsevier BV}, author = {Larance, Mark and Kirkwood, Kathryn J. and Tinti, Michele and Brenes Murillo, Alejandro and Ferguson, Michael A.J. and Lamond, Angus I.}, year = {2016}, month = jul, pages = {2476–2490}} - >- @article{Mallam2019, title = {Systematic Discovery of Endogenous Human Ribonucleoprotein Complexes}, volume = {29}, ISSN = {2211-1247}, url = {http://dx.doi.org/10.1016/j.celrep.2019.09.060}, DOI = {10.1016/j.celrep.2019.09.060}, number = {5}, journal = {Cell Reports}, publisher = {Elsevier BV}, author = {Mallam, Anna L. and Sae-Lee, Wisath and Schaub, Jeffrey M. and Tu, Fan and Battenhouse, Anna and Jang, Yu Jin and Kim, Jonghwan and Wallingford, John B. and Finkelstein, Ilya J. and Marcotte, Edward M. and Drew, Kevin}, year = {2019}, month = oct, pages = {1351--1368.e5}} - >- @article{Moutaoufik2019, title = {Rewiring of the Human Mitochondrial Interactome during Neuronal Reprogramming Reveals Regulators of the Respirasome and Neurogenesis}, volume = {19}, ISSN = {2589-0042}, url = {http://dx.doi.org/10.1016/j.isci.2019.08.057}, DOI = {10.1016/j.isci.2019.08.057}, journal = {iScience}, publisher = {Elsevier BV}, author = {Moutaoufik, Mohamed Taha and Malty, Ramy and Amin, Shahreen and Zhang, Qingzhou and Phanse, Sadhna and Gagarinova, Alla and Zilocchi, Mara and Hoell, Larissa and Minic, Zoran and Gagarinova, Maria and Aoki, Hiroyuki and Stockwell, Jocelyn and Jessulat, Matthew and Goebels, Florian and Broderick, Kirsten and Scott, Nichollas E. and Vlasblom, James and Musso, Gabriel and Prasad, Bhanu and Lamantea, Eleonora and Garavaglia, Barbara and Rajput, Alex and Murayama, Kei and Okazaki, Yasushi and Foster, Leonard J. and Bader, Gary D. and Cayabyab, Francisco S. and Babu, Mohan}, year = {2019}, month = sep, pages = {1114–1132}} - >- @article{Wan2015, title = {Panorama of ancient metazoan macromolecular complexes}, volume = {525}, ISSN = {1476-4687}, url = {http://dx.doi.org/10.1038/nature14877}, DOI = {10.1038/nature14877}, number = {7569}, journal = {Nature}, publisher = {Springer Science and Business Media LLC}, author = {Wan, Cuihong and Borgeson, Blake and Phanse, Sadhna and Tu, Fan and Drew, Kevin and Clark, Greg and Xiong, Xuejian and Kagan, Olga and Kwan, Julian and Bezginov, Alexandr and Chessman, Kyle and Pal, Swati and Cromar, Graham and Papoulas, Ophelia and Ni, Zuyao and Boutz, Daniel R. and Stoilova, Snejana and Havugimana, Pierre C. and Guo, Xinghua and Malty, Ramy H. and Sarov, Mihail and Greenblatt, Jack and Babu, Mohan and Derry, W. Brent and R. Tillier, Elisabeth and Wallingford, John B. and Parkinson, John and Marcotte, Edward M. and Emili, Andrew}, year = {2015}, month = sep, pages = {339–344}} citation_apa: - >- Connelly, K. E., Hedrick, V., Paschoal Sobreira, T. J., Dykhuizen, E. C., & Aryal, U. K. (2018). Analysis of Human Nuclear Protein Complexes by Quantitative Mass Spectrometry Profiling. Proteomics, 18(11), e1700427. https://doi.org/10.1002/pmic.201700427 - Kirkwood, K. J., Ahmad, Y., Larance, M., & Lamond, A. I. (2013). Characterization of native protein complexes and protein isoform variation using size-fractionation-based quantitative proteomics. Molecular & cellular proteomics: MCP, 12(12), 3851–3873. https://doi.org/10.1074/mcp.M113.032367 - Larance, M., Kirkwood, K. J., Tinti, M., Brenes Murillo, A., Ferguson, M. A., & Lamond, A. I. (2016). Global Membrane Protein Interactome Analysis using In vivo Crosslinking and Mass Spectrometry-based Protein Correlation Profiling. Molecular & cellular proteomics: MCP, 15(7), 2476–2490. https://doi.org/10.1074/mcp.O115.055467 - >- Mallam, A. L., Sae-Lee, W., Schaub, J. M., Tu, F., Battenhouse, A., Jang, Y. J., Kim, J., Wallingford, J. B., Finkelstein, I. J., Marcotte, E. M., & Drew, K. (2019). Systematic Discovery of Endogenous Human Ribonucleoprotein Complexes. Cell reports, 29(5), 1351–1368.e5. https://doi.org/10.1016/j.celrep.2019.09.060 - >- Moutaoufik, M. T., Malty, R., Amin, S., Zhang, Q., Phanse, S., Gagarinova, A., Zilocchi, M., Hoell, L., Minic, Z., Gagarinova, M., Aoki, H., Stockwell, J., Jessulat, M., Goebels, F., Broderick, K., Scott, N. E., Vlasblom, J., Musso, G., Prasad, B., Lamantea, E., … Babu, M. (2019). Rewiring of the Human Mitochondrial Interactome during Neuronal Reprogramming Reveals Regulators of the Respirasome and Neurogenesis. iScience, 19, 1114–1132. https://doi.org/10.1016/j.isci.2019.08.057 - >- Wan, C., Borgeson, B., Phanse, S., Tu, F., Drew, K., Clark, G., Xiong, X., Kagan, O., Kwan, J., Bezginov, A., Chessman, K., Pal, S., Cromar, G., Papoulas, O., Ni, Z., Boutz, D. R., Stoilova, S., Havugimana, P. C., Guo, X., Malty, R. H., … Emili, A. (2015). Panorama of ancient metazoan macromolecular complexes. Nature, 525(7569), 339–344. https://doi.org/10.1038/nature14877 --- # CF/MS Elution Profile PPI Dataset Proteins are the functional basis of life, but it is often their interactions with other proteins which gives rise to said functions. Therefore, we are often interested in whether two proteins participate in the same *protein complex*, or if they **'co-complex'**. Co-fractionation mass spectrometry (CF/MS) is a high-throughput method for determining whether proteins form complexes. If they do, both proteins will typically separate out into the same fractions, or **'co-elute'**, during column chromatography experiments. As a result, their abundances will be *highly correlated* across all the fractions measured. CF/MS leverages this fact to identify new protein complexes by attempting to statistically correlate the elution profiles of groups of proteins. Typically, we use Pearson correlation coefficient to determine correlation between protein pairs. While this often works quite well, Pearson is a linear function. Current research is exploring whether there are non-linear, higher-order signals between these elution profiles that might have better predictive power than Pearson. As deep learning models excel at estimating non-linear relationships in data, the goal of this dataset is to act as training data for such models, especially **Siamese networks**. This dataset includes processed data from several *Homo sapiens* protein **co-fractionation mass spectrometry (CF/MS)** experiments, as well as positive/negative protein-protein interaction (PPI) labels for each pair. Collated, maintained by **Drew Lab at University of Illinois at Chicago** [Drew Lab webpage](https://ksdrew.github.io/) ## File formats - **The .elut file**: A **.elut** file is a TSV-like format containing raw count data from a chromatographic fractionation experiment. Each row in a **.elut** file shows the abundance of a single protein across the collected fractions (columns). Generally speaking, these fractions are collected over time. However, different chromatographic columns can separate proteins along different axes. For example, *Size-eclusion chromatography (SEC)* will mostly separate proteins into fractions according to their *size*; *Ion-exchange chromatography (IEX)* will separate them into fractions according to their *charge*. Each file in this dataset comes from one of these two column separation methods and is named accordingly ('...xx_SEC_xx...' / '...xx_IEX_xx...'). We refer to a given protein's (row's) count data across all fractions (columns) as that protein's elution trace or elution profile. To summarize: - A given row contains count data for a specific protein - A row's *first column* contains its associated **protein ID** - A row's *subsequent columns* contain that protein's count data from the fractionation experiment - **Note: The user may notice that the first row in a .elut file is one column longer than subsequent rows. This is because the first row contains row names (protein IDs), and the first column contains column names (fraction IDs). Therefore, cell 'A0' is empty.** ## File structure - The **.elut** files each contain a collection elution traces for proteins from a given CF/MS experiment. These can be paired to make sample data. A complete list of data sources can be found at the bottom of this README - The **.txt** files contain line-wise specification of protein complexes used to generate positive/negative labels. These can be used to direct the pairing of elution traces into data points. - **intact_complex_merge_20230309.train_ppis.txt**: List of positive PPIs for training data - **intact_complex_merge_20230309.test_ppis.txt**: List of positive PPIs for testing data - **intact_complex_merge_20230309.neg_train_ppis.txt**: List of negative PPIs for training data - **intact_complex_merge_20230309.neg_test_ppis.txt**: List of negative PPIs for testing data - **intact_complex_merge_20230309.train.txt** Line-wise list of protein complexes ### List of publications/experiments from which this dataset was assembled: Connelly, K. E., Hedrick, V., Paschoal Sobreira, T. J., Dykhuizen, E. C., & Aryal, U. K. (2018). Analysis of Human Nuclear Protein Complexes by Quantitative Mass Spectrometry Profiling. Proteomics, 18(11), e1700427. https://doi.org/10.1002/pmic.201700427 - T98G_glioblastoma_multiforme_cells_SEC_Conelly_2018_Bio1.elut - T98G_glioblastoma_multiforme_cells_SEC_Conelly_2018_Bio2.elut Kirkwood, K. J., Ahmad, Y., Larance, M., & Lamond, A. I. (2013). Characterization of native protein complexes and protein isoform variation using size-fractionation-based quantitative proteomics. Molecular & cellular proteomics : MCP, 12(12), 3851–3873. https://doi.org/10.1074/mcp.M113.032367 - U2OS_cells_SEC_Kirkwood_2013_rep1.elut - U2OS_cells_SEC_Kirkwood_2013_rep2.elut - U2OS_cells_SEC_Kirkwood_2013_rep3.elut Larance, M., Kirkwood, K. J., Tinti, M., Brenes Murillo, A., Ferguson, M. A., & Lamond, A. I. (2016). Global Membrane Protein Interactome Analysis using In vivo Crosslinking and Mass Spectrometry-based Protein Correlation Profiling. Molecular & cellular proteomics : MCP, 15(7), 2476–2490. https://doi.org/10.1074/mcp.O115.055467 - U2OS_cells_SEC_Larance_2016_PT3281S1.elut - U2OS_cells_SEC_Larance_2016_PT3441S1.elut - U2OS_cells_SEC_Larance_2016_PT3442S1.elut - U2OS_cells_SEC_Larance_2016_PT3701S1.elut - U2OS_cells_SEC_Larance_2016_PTSS3801.elut - U2OS_cells_SEC_Larance_2016_PTSS3802.elut Mallam, A. L., Sae-Lee, W., Schaub, J. M., Tu, F., Battenhouse, A., Jang, Y. J., Kim, J., Wallingford, J. B., Finkelstein, I. J., Marcotte, E. M., & Drew, K. (2019). Systematic Discovery of Endogenous Human Ribonucleoprotein Complexes. Cell reports, 29(5), 1351–1368.e5. https://doi.org/10.1016/j.celrep.2019.09.060 - HEK_293_T_cells_SEC_Mallam_2019_C1.elut - HEK_293_T_cells_SEC_Mallam_2019_C2.elut Moutaoufik, M. T., Malty, R., Amin, S., Zhang, Q., Phanse, S., Gagarinova, A., Zilocchi, M., Hoell, L., Minic, Z., Gagarinova, M., Aoki, H., Stockwell, J., Jessulat, M., Goebels, F., Broderick, K., Scott, N. E., Vlasblom, J., Musso, G., Prasad, B., Lamantea, E., … Babu, M. (2019). Rewiring of the Human Mitochondrial Interactome during Neuronal Reprogramming Reveals Regulators of the Respirasome and Neurogenesis. iScience, 19, 1114–1132. https://doi.org/10.1016/j.isci.2019.08.057 - NTera2_embryonal_carcinoma_stem_cells_IEX_Moutaoufik_2019_2_R1.elut - NTera2_embryonal_carcinoma_stem_cells_IEX_Moutaoufik_2019_2_R2.elut - NTera2_embryonal_carcinoma_stem_cells_IEX_Moutaoufik_2019_R1.elut - NTera2_embryonal_carcinoma_stem_cells_IEX_Moutaoufik_2019_R2.elut - NTera2_embryonal_carcinoma_stem_cells_SEC_Moutaoufik_2019_2_R1.elut - NTera2_embryonal_carcinoma_stem_cells_SEC_Moutaoufik_2019_2_R2.elut - NTera2_embryonal_carcinoma_stem_cells_SEC_Moutaoufik_2019_R1.elut - NTera2_embryonal_carcinoma_stem_cells_SEC_Moutaoufik_2019_R2.elut Wan, C., Borgeson, B., Phanse, S., Tu, F., Drew, K., Clark, G., Xiong, X., Kagan, O., Kwan, J., Bezginov, A., Chessman, K., Pal, S., Cromar, G., Papoulas, O., Ni, Z., Boutz, D. R., Stoilova, S., Havugimana, P. C., Guo, X., Malty, R. H., … Emili, A. (2015). Panorama of ancient metazoan macromolecular complexes. Nature, 525(7569), 339–344. https://doi.org/10.1038/nature14877 - CB660_neural_stem_cell_IEX_Wan_2015.elut - G166_glioma_stem_cell_IEX_Wan_2015_Hs_HCW_2.elut - G166_glioma_stem_cell_IEX_Wan_2015_Hs_HCW_3.elut - IEX_Wan_2015_Hs_HCW_4.elut - IEX_Wan_2015_Hs_HCW_5.elut - IEX_Wan_2015_Hs_HCW_6.elut - IEX_Wan_2015_Hs_HCW_7.elut - IEX_Wan_2015_Hs_HCW_8.elut - IEX_Wan_2015_Hs_HCW_9.elut - IEX_Wan_2015_Hs_IEX_1.elut - IEX_Wan_2015_Hs_IEX_2.elut

ziyu3141/rich_feedback

ziyu3141

--- license: apache-2.0 dataset_info: features: - name: number dtype: int64 - name: array sequence: float64 splits: - name: train num_bytes: 108 num_examples: 3 download_size: 1610 dataset_size: 108 configs: - config_name: default data_files: - split: train path: data/train-* ---

davisrbr/jailbreakbench-goal-embeddings-augmented

davisrbr

--- dataset_info: features: - name: index dtype: int64 - name: goal dtype: string - name: behavior dtype: string - name: category dtype: string - name: prompt dtype: string - name: response dtype: string - name: number_of_queries dtype: int64 - name: queries_to_jailbreak dtype: int64 - name: jailbroken dtype: bool - name: method dtype: string - name: model_name dtype: string - name: prompt_tokens dtype: int64 - name: response_tokens dtype: int64 - name: goal_embedding sequence: float64 - name: most_similar_1 dtype: string - name: most_similarity_1 dtype: float64 - name: most_similar_2 dtype: string - name: most_similarity_2 dtype: float64 - name: most_similar_3 dtype: string - name: most_similarity_3 dtype: float64 - name: most_similar_4 dtype: string - name: most_similarity_4 dtype: float64 - name: most_similar_5 dtype: string - name: most_similarity_5 dtype: float64 - name: most_similar_6 dtype: string - name: most_similarity_6 dtype: float64 - name: most_similar_7 dtype: string - name: most_similarity_7 dtype: float64 - name: most_similar_8 dtype: string - name: most_similarity_8 dtype: float64 - name: most_similar_9 dtype: string - name: most_similarity_9 dtype: float64 - name: most_similar_10 dtype: string - name: most_similarity_10 dtype: float64 - name: most_similar_11 dtype: string - name: most_similarity_11 dtype: float64 - name: most_similar_12 dtype: string - name: most_similarity_12 dtype: float64 - name: most_similar_13 dtype: string - name: most_similarity_13 dtype: float64 - name: most_similar_14 dtype: string - name: most_similarity_14 dtype: float64 - name: most_similar_15 dtype: string - name: most_similarity_15 dtype: float64 - name: most_similar_16 dtype: string - name: most_similarity_16 dtype: float64 - name: most_similar_17 dtype: string - name: most_similarity_17 dtype: float64 - name: most_similar_18 dtype: string - name: most_similarity_18 dtype: float64 - name: most_similar_19 dtype: string - name: most_similarity_19 dtype: float64 - name: most_similar_20 dtype: string - name: most_similarity_20 dtype: float64 - name: most_similar_21 dtype: string - name: most_similarity_21 dtype: float64 - name: most_similar_22 dtype: string - name: most_similarity_22 dtype: float64 - name: most_similar_23 dtype: string - name: most_similarity_23 dtype: float64 - name: most_similar_24 dtype: string - name: most_similarity_24 dtype: float64 - name: most_similar_25 dtype: string - name: most_similarity_25 dtype: float64 - name: most_similar_26 dtype: string - name: most_similarity_26 dtype: float64 - name: most_similar_27 dtype: string - name: most_similarity_27 dtype: float64 - name: most_similar_28 dtype: string - name: most_similarity_28 dtype: float64 - name: most_similar_29 dtype: string - name: most_similarity_29 dtype: float64 - name: most_similar_30 dtype: string - name: most_similarity_30 dtype: float64 - name: most_similar_31 dtype: string - name: most_similarity_31 dtype: float64 - name: most_similar_32 dtype: string - name: most_similarity_32 dtype: float64 - name: most_similar_33 dtype: string - name: most_similarity_33 dtype: float64 - name: most_similar_34 dtype: string - name: most_similarity_34 dtype: float64 - name: most_similar_35 dtype: string - name: most_similarity_35 dtype: float64 - name: most_similar_36 dtype: string - name: most_similarity_36 dtype: float64 - name: most_similar_37 dtype: string - name: most_similarity_37 dtype: float64 - name: most_similar_38 dtype: string - name: most_similarity_38 dtype: float64 - name: most_similar_39 dtype: string - name: most_similarity_39 dtype: float64 - name: most_similar_40 dtype: string - name: most_similarity_40 dtype: float64 - name: most_similar_41 dtype: string - name: most_similarity_41 dtype: float64 - name: most_similar_42 dtype: string - name: most_similarity_42 dtype: float64 - name: most_similar_43 dtype: string - name: most_similarity_43 dtype: float64 - name: most_similar_44 dtype: string - name: most_similarity_44 dtype: float64 - name: most_similar_45 dtype: string - name: most_similarity_45 dtype: float64 - name: most_similar_46 dtype: string - name: most_similarity_46 dtype: float64 - name: most_similar_47 dtype: string - name: most_similarity_47 dtype: float64 - name: most_similar_48 dtype: string - name: most_similarity_48 dtype: float64 - name: most_similar_49 dtype: string - name: most_similarity_49 dtype: float64 - name: most_similar_50 dtype: string - name: most_similarity_50 dtype: float64 - name: most_similar_51 dtype: string - name: most_similarity_51 dtype: float64 - name: most_similar_52 dtype: string - name: most_similarity_52 dtype: float64 - name: most_similar_53 dtype: string - name: most_similarity_53 dtype: float64 - name: most_similar_54 dtype: string - name: most_similarity_54 dtype: float64 - name: most_similar_55 dtype: string - name: most_similarity_55 dtype: float64 - name: most_similar_56 dtype: string - name: most_similarity_56 dtype: float64 - name: most_similar_57 dtype: string - name: most_similarity_57 dtype: float64 - name: most_similar_58 dtype: string - name: most_similarity_58 dtype: float64 - name: most_similar_59 dtype: string - name: most_similarity_59 dtype: float64 - 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name: least_similar_74 dtype: string - name: least_similarity_74 dtype: float64 - name: least_similar_75 dtype: string - name: least_similarity_75 dtype: float64 - name: least_similar_76 dtype: string - name: least_similarity_76 dtype: float64 - name: least_similar_77 dtype: string - name: least_similarity_77 dtype: float64 - name: least_similar_78 dtype: string - name: least_similarity_78 dtype: float64 - name: least_similar_79 dtype: string - name: least_similarity_79 dtype: float64 - name: least_similar_80 dtype: string - name: least_similarity_80 dtype: float64 - name: least_similar_81 dtype: string - name: least_similarity_81 dtype: float64 - name: least_similar_82 dtype: string - name: least_similarity_82 dtype: float64 - name: least_similar_83 dtype: string - name: least_similarity_83 dtype: float64 - name: least_similar_84 dtype: string - name: least_similarity_84 dtype: float64 - name: least_similar_85 dtype: string - name: least_similarity_85 dtype: float64 - name: least_similar_86 dtype: string - name: least_similarity_86 dtype: float64 - name: least_similar_87 dtype: string - name: least_similarity_87 dtype: float64 - name: least_similar_88 dtype: string - name: least_similarity_88 dtype: float64 - name: least_similar_89 dtype: string - name: least_similarity_89 dtype: float64 - name: least_similar_90 dtype: string - name: least_similarity_90 dtype: float64 - name: least_similar_91 dtype: string - name: least_similarity_91 dtype: float64 - name: least_similar_92 dtype: string - name: least_similarity_92 dtype: float64 - name: least_similar_93 dtype: string - name: least_similarity_93 dtype: float64 - name: least_similar_94 dtype: string - name: least_similarity_94 dtype: float64 - name: least_similar_95 dtype: string - name: least_similarity_95 dtype: float64 - name: least_similar_96 dtype: string - name: least_similarity_96 dtype: float64 - name: least_similar_97 dtype: string - name: least_similarity_97 dtype: float64 - name: least_similar_98 dtype: string - name: least_similarity_98 dtype: float64 - name: least_similar_99 dtype: string - name: least_similarity_99 dtype: float64 - name: least_similar_100 dtype: string - name: least_similarity_100 dtype: float64 splits: - name: train num_bytes: 16034267 num_examples: 587 download_size: 3103473 dataset_size: 16034267 configs: - config_name: default data_files: - split: train path: data/train-* ---

xNoper/billsum-memsum-10sent-concat

xNoper

--- dataset_info: features: - name: selected_sentences dtype: string - name: summary dtype: string splits: - name: test num_bytes: 8536610 num_examples: 3269 download_size: 4139100 dataset_size: 8536610 configs: - config_name: default data_files: - split: test path: data/test-* ---

mmtg/ui_design_subset

mmtg

--- dataset_info: features: - name: width dtype: int64 - name: height dtype: int64 - name: image dtype: image - name: objects struct: - name: bbox sequence: sequence: float64 - name: category sequence: string - name: color list: - name: alpha dtype: float64 - name: blue dtype: float64 - name: green dtype: float64 - name: red dtype: float64 - name: radius sequence: float64 - name: text sequence: string - name: description dtype: string splits: - name: train num_bytes: 164111511.0 num_examples: 1000 download_size: 152935258 dataset_size: 164111511.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

juliadollis/Qwen2.5-7B-Instruct_toxigen-data-test_zeroshot

juliadollis

--- dataset_info: features: - name: text dtype: string - name: target_group dtype: string - name: factual? dtype: string - name: ingroup_effect dtype: string - name: lewd dtype: string - name: framing dtype: string - name: predicted_group dtype: string - name: stereotyping dtype: string - name: intent dtype: float64 - name: toxicity_ai dtype: float64 - name: toxicity_human dtype: float64 - name: predicted_author dtype: string - name: actual_method dtype: string - name: is_toxic dtype: int64 - name: predicted_is_toxic dtype: int64 - name: y_true dtype: int64 splits: - name: train num_bytes: 393176 num_examples: 940 download_size: 85179 dataset_size: 393176 configs: - config_name: default data_files: - split: train path: data/train-* ---

ZixuanKe/flare_finqa_sup_sample_from_policy_v1.1_dpo_val_chunk_6

ZixuanKe

--- dataset_info: features: - name: prompt dtype: string - name: rejected dtype: string - name: chosen dtype: string splits: - name: train num_bytes: 210262 num_examples: 40 download_size: 44513 dataset_size: 210262 configs: - config_name: default data_files: - split: train path: data/train-* ---

miguelsolis/some_name_random_9_2024_11_23_03_07_23

miguelsolis

--- task_categories: - robotics tags: - LeRobot - tutorial --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).

yubinnii/ikea3

yubinnii

--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 17236413.0 num_examples: 20 download_size: 17238362 dataset_size: 17236413.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

KisoreD03/promt_data

KisoreD03

--- license: mit ---

90AI/11203

90AI

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

InsultedByMathematics/rebel-ultrafeedback-test-evaluation-update-101

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: prompt_id dtype: string - name: prompt dtype: string - name: llama_prompt dtype: string - name: llama_prompt_tokens sequence: int64 - 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: chosen dtype: string - name: chosen_reward dtype: float64 - name: llama_chosen dtype: string - name: llama_chosen_tokens sequence: int64 - name: reject dtype: string - name: reject_reward dtype: float64 - name: llama_reject dtype: string - name: llama_reject_tokens sequence: int64 - name: chosen_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: 106594758 num_examples: 1801 download_size: 27756614 dataset_size: 106594758 configs: - config_name: default data_files: - split: test_prefs path: data/test_prefs-* ---

bacnguyenne/CIViT_WildDeepfake

bacnguyenne

--- license: unknown ---

InsultedByMathematics/llama3-ultrafeedback-armo-test-evaluation-update-201

InsultedByMathematics

--- dataset_info: features: - 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 - name: prompt dtype: string - name: llama_prompt_tokens sequence: int64 - name: chosen dtype: string - name: chosen_reward dtype: float64 - name: finetuned_llama_prompt_tokens sequence: int64 - 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: finetuned_chosen dtype: string - name: finetuned_chosen_reward dtype: float64 - name: finetuned_llama_chosen_tokens sequence: int64 - name: base_llama_chosen_tokens sequence: int64 - name: finetuned_reject dtype: string - name: finetuned_reject_reward dtype: float64 - name: finetuned_llama_reject_tokens sequence: int64 - name: base_llama_reject_tokens sequence: int64 - name: finetuned_chosen_logprob dtype: float64 - name: finetuned_reject_logprob dtype: float64 - name: base_chosen_logprob dtype: float64 - name: base_reject_logprob dtype: float64 splits: - name: test_prefs num_bytes: 105053218 num_examples: 1690 download_size: 18819169 dataset_size: 105053218 configs: - config_name: default data_files: - split: test_prefs path: data/test_prefs-* ---

real-jiakai/github-issues

real-jiakai

--- annotations_creators: [] language: - en language_creators: - found license: - mit multilinguality: - monolingual pretty_name: HuggingFace GitHub Issues size_categories: - n<1K source_datasets: - original tags: [] task_categories: - text-classification - text-retrieval task_ids: - multi-class-classification - multi-label-classification - document-retrieval --- This dataset originates from the teaching materials of the NLP Course. via: [https://huggingface.co/learn/nlp-course/en/chapter5/5](https://huggingface.co/learn/nlp-course/en/chapter5/5)

kaistlayner/processed_empathy_dataset

kaistlayner

--- dataset_info: features: - name: conv_id dtype: string - name: utterance_idx dtype: int32 - name: context dtype: string - name: prompt dtype: string - name: speaker_idx dtype: int32 - name: utterance dtype: string - name: selfeval dtype: string - name: tags dtype: string - name: emotion_labels dtype: int64 - name: relevance_scores dtype: float64 splits: - name: train num_bytes: 18700144 num_examples: 76673 - name: validation num_bytes: 3086035 num_examples: 12030 - name: test num_bytes: 3062246 num_examples: 10943 download_size: 7991711 dataset_size: 24848425 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

InsultedByMathematics/llama3-ultrafeedback-armo-test-evaluation-offline-update-201

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: prompt dtype: string - name: chosen dtype: string - name: chosen_reward dtype: float64 - name: reject dtype: string - name: reject_reward dtype: float64 - name: chosen_logprob dtype: float64 - name: reject_logprob dtype: float64 - name: llama_prompt_tokens sequence: int64 - name: llama_chosen_tokens sequence: int64 - name: llama_reject_tokens sequence: int64 - name: finetuned_chosen_logprob dtype: float64 - name: finetuned_reject_logprob dtype: float64 splits: - name: test_prefs num_bytes: 65416906 num_examples: 1801 download_size: 14957210 dataset_size: 65416906 configs: - config_name: default data_files: - split: test_prefs path: data/test_prefs-* ---

InsultedByMathematics/llama3-ultrafeedback-armo-test-evaluation-offline-update-401

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: prompt dtype: string - name: chosen dtype: string - name: chosen_reward dtype: float64 - name: reject dtype: string - name: reject_reward dtype: float64 - name: chosen_logprob dtype: float64 - name: reject_logprob dtype: float64 - name: llama_prompt_tokens sequence: int64 - name: llama_chosen_tokens sequence: int64 - name: llama_reject_tokens sequence: int64 - name: finetuned_chosen_logprob dtype: float64 - name: finetuned_reject_logprob dtype: float64 splits: - name: test_prefs num_bytes: 65416906 num_examples: 1801 download_size: 14956591 dataset_size: 65416906 configs: - config_name: default data_files: - split: test_prefs path: data/test_prefs-* ---

pooya-mohammadi/common_voice_16_1_fa_pseudo_labelled

pooya-mohammadi

--- dataset_info: config_name: fa features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: condition_on_prev sequence: int64 - name: whisper_transcript dtype: string splits: - name: train num_bytes: 3541164915.062 num_examples: 4063 - name: validation num_bytes: 1440121524.25 num_examples: 1662 - name: test num_bytes: 1678675966.223 num_examples: 1951 download_size: 5844179076 dataset_size: 6659962405.535 configs: - config_name: fa data_files: - split: train path: fa/train-* - split: validation path: fa/validation-* - split: test path: fa/test-* ---

ahmedafareed/fate7a

ahmedafareed

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string splits: - name: train num_bytes: 1509521.0 num_examples: 8 download_size: 1306162 dataset_size: 1509521.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

90AI/996

90AI

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

sleeping4cat/TEKGEN

sleeping4cat

--- license: unknown viewer: false task_categories: - text-generation --- TEKGEN is a training corpus utilized by Google for their [KELM](https://huggingface.co/datasets/google-research-datasets/kelm) paper. In this [repository](https://huggingface.co/datasets/google-research-datasets/kelm), I have uploaded the knowledge graphs and the original sentences used to create these graphs for the KELM dataset. Note that this dataset is not the KELM dataset itself but rather the training corpus used for the REALM model.

HisAtri/pls

HisAtri

--- license: apache-2.0 ---

Newvel/narrativeqa_filtered

Newvel

--- dataset_info: features: - name: summary dtype: string - name: text dtype: string splits: - name: train num_bytes: 11361626101 num_examples: 32747 - name: test num_bytes: 3484738624 num_examples: 10557 - name: validation num_bytes: 1191914008 num_examples: 3461 download_size: 2571092466 dataset_size: 16038278733 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---

NeuralPGRank/fiqa-hard-negatives

NeuralPGRank

--- license: cc-by-sa-4.0 language: - en --- # Dataset Card ## Dataset Details This dataset contains a set of candidate documents for second-stage re-ranking on fiqa (test split in [BEIR](https://huggingface.co/BeIR)). Those candidate documents are composed of hard negatives mined from [gtr-t5-xl](https://huggingface.co/sentence-transformers/gtr-t5-xl) as Stage 1 ranker and ground-truth documents that are known to be relevant to the query. This is a release from our paper [Policy-Gradient Training of Language Models for Ranking](https://gao-g.github.io/), so please cite it if using this dataset. ## Direct Use You can load the dataset by: ```python from datasets import load_dataset dataset = load_dataset("NeuralPGRank/fiqa-hard-negatives") ``` Each example is an dictionary: ```python >>> python dataset['test'][0] { "qid" : ..., # query ID "topk" : { doc ID: ..., # document ID as the key; None or a score as the value doc ID: ..., ... }, } ``` ## Citation ``` @inproceedings{Gao2023PolicyGradientTO, title={Policy-Gradient Training of Language Models for Ranking}, author={Ge Gao and Jonathan D. Chang and Claire Cardie and Kiant{\'e} Brantley and Thorsten Joachims}, booktitle={Conference on Neural Information Processing Systems (Foundation Models for Decising Making Workshop)}, year={2023}, url={https://arxiv.org/pdf/2310.04407} } ``` ## Dataset Card Author and Contact [Ge Gao](https://gao-g.github.io/)

NeuralPGRank/nfcorpus-hard-negatives

NeuralPGRank

--- license: cc-by-sa-4.0 language: - en --- # Dataset Card ## Dataset Details This dataset contains a set of candidate documents for second-stage re-ranking on nfcorpus (test split in [BEIR](https://huggingface.co/BeIR)). Those candidate documents are composed of hard negatives mined from [gtr-t5-xl](https://huggingface.co/sentence-transformers/gtr-t5-xl) as Stage 1 ranker and ground-truth documents that are known to be relevant to the query. This is a release from our paper [Policy-Gradient Training of Language Models for Ranking](https://gao-g.github.io/), so please cite it if using this dataset. ## Direct Use You can load the dataset by: ```python from datasets import load_dataset dataset = load_dataset("NeuralPGRank/nfcorpus-hard-negatives") ``` Each example is an dictionary: ```python >>> python dataset['test'][0] { "qid" : ..., # query ID "topk" : { doc ID: ..., # document ID as the key; None or a score as the value doc ID: ..., ... }, } ``` ## Citation ``` @inproceedings{Gao2023PolicyGradientTO, title={Policy-Gradient Training of Language Models for Ranking}, author={Ge Gao and Jonathan D. Chang and Claire Cardie and Kiant{\'e} Brantley and Thorsten Joachims}, booktitle={Conference on Neural Information Processing Systems (Foundation Models for Decising Making Workshop)}, year={2023}, url={https://arxiv.org/pdf/2310.04407} } ``` ## Dataset Card Author and Contact [Ge Gao](https://gao-g.github.io/)

NeuralPGRank/nq-hard-negatives

NeuralPGRank

--- license: cc-by-sa-4.0 language: - en --- # Dataset Card ## Dataset Details This dataset contains a set of candidate documents for second-stage re-ranking on nq (test split in [BEIR](https://huggingface.co/BeIR)). Those candidate documents are composed of hard negatives mined from [gtr-t5-xl](https://huggingface.co/sentence-transformers/gtr-t5-xl) as Stage 1 ranker and ground-truth documents that are known to be relevant to the query. This is a release from our paper [Policy-Gradient Training of Language Models for Ranking](https://gao-g.github.io/), so please cite it if using this dataset. ## Direct Use You can load the dataset by: ```python from datasets import load_dataset dataset = load_dataset("NeuralPGRank/nq-hard-negatives") ``` Each example is an dictionary: ```python >>> python dataset['test'][0] { "qid" : ..., # query ID "topk" : { doc ID: ..., # document ID as the key; None or a score as the value doc ID: ..., ... }, } ``` ## Citation ``` @inproceedings{Gao2023PolicyGradientTO, title={Policy-Gradient Training of Language Models for Ranking}, author={Ge Gao and Jonathan D. Chang and Claire Cardie and Kiant{\'e} Brantley and Thorsten Joachims}, booktitle={Conference on Neural Information Processing Systems (Foundation Models for Decising Making Workshop)}, year={2023}, url={https://arxiv.org/pdf/2310.04407} } ``` ## Dataset Card Author and Contact [Ge Gao](https://gao-g.github.io/)

NeuralPGRank/scidocs-hard-negatives

NeuralPGRank

--- license: cc-by-sa-4.0 language: - en --- # Dataset Card ## Dataset Details This dataset contains a set of candidate documents for second-stage re-ranking on scidocs (test split in [BEIR](https://huggingface.co/BeIR)). Those candidate documents are composed of hard negatives mined from [gtr-t5-xl](https://huggingface.co/sentence-transformers/gtr-t5-xl) as Stage 1 ranker and ground-truth documents that are known to be relevant to the query. This is a release from our paper [Policy-Gradient Training of Language Models for Ranking](https://gao-g.github.io/), so please cite it if using this dataset. ## Direct Use You can load the dataset by: ```python from datasets import load_dataset dataset = load_dataset("NeuralPGRank/scidocs-hard-negatives") ``` Each example is an dictionary: ```python >>> python dataset['test'][0] { "qid" : ..., # query ID "topk" : { doc ID: ..., # document ID as the key; None or a score as the value doc ID: ..., ... }, } ``` ## Citation ``` @inproceedings{Gao2023PolicyGradientTO, title={Policy-Gradient Training of Language Models for Ranking}, author={Ge Gao and Jonathan D. Chang and Claire Cardie and Kiant{\'e} Brantley and Thorsten Joachims}, booktitle={Conference on Neural Information Processing Systems (Foundation Models for Decising Making Workshop)}, year={2023}, url={https://arxiv.org/pdf/2310.04407} } ``` ## Dataset Card Author and Contact [Ge Gao](https://gao-g.github.io/)

junnystateofmind/testing_refuel_5_turns_only_ckp_2

junnystateofmind

--- dataset_info: features: - name: combined_data struct: - name: narrative dtype: string - name: trajectory list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 49886 num_examples: 5 download_size: 22988 dataset_size: 49886 configs: - config_name: default data_files: - split: train path: data/train-* ---

junnystateofmind/testing_refuel_5_turns_only_ckp_4

junnystateofmind

--- dataset_info: features: - name: combined_data struct: - name: narrative dtype: string - name: trajectory list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 83550 num_examples: 5 download_size: 25558 dataset_size: 83550 configs: - config_name: default data_files: - split: train path: data/train-* ---

ZixuanKe/flare_finqa_sup_sample_from_policy_v1.1_dpo_train_chunk_17

ZixuanKe

--- dataset_info: features: - name: prompt dtype: string - name: rejected dtype: string - name: chosen dtype: string splits: - name: train num_bytes: 5650860 num_examples: 1085 download_size: 601479 dataset_size: 5650860 configs: - config_name: default data_files: - split: train path: data/train-* ---

ahmddbilall/Pakistan_Weather

ahmddbilall

--- license: mit ---

junnystateofmind/testing_ultrainteract

junnystateofmind

--- dataset_info: features: - name: trajectory list: - name: content dtype: string - name: role dtype: string - name: sampled_len_from_5 dtype: int64 - name: sampled_h_from_sampled_len dtype: int64 - name: trajectory_sampled_h_from_sampled_len list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 74810 num_examples: 1 download_size: 18145 dataset_size: 74810 configs: - config_name: default data_files: - split: train path: data/train-* ---

Ruan60/Turismo

Ruan60

--- license: gemma ---

Ruan60/Tur_bot

Ruan60

--- license: gemma ---

ZixuanKe/sujet_finance_instruct_sup_sample_from_policy_v1.1_dpo_binarized

ZixuanKe

--- dataset_info: features: - name: prompt dtype: 'null' - name: rejected dtype: 'null' - name: chosen dtype: 'null' splits: - name: train num_bytes: 0 num_examples: 0 - name: validation num_bytes: 0 num_examples: 0 download_size: 1848 dataset_size: 0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

genaiarchitect/AdultPhysicalActivitiesGuidelines

genaiarchitect

--- license: bsd --- This dataset is based on the published work of the following organiations: 1. World Health Organization 2020, IGO licence (CC BY-NC-SA 3.0 IGO; https://creativecommons.org/licenses/by-nc-sa/3.0/igo). 2. U.S. Department of Health and Human Services. Physical Activity Guidelines for Americans, 2nd edition. Washington, DC: U.S. Department of Health and Human Services; 2018.U.S. Department of Health and Human Services

ZixuanKe/fingpt_convfinqa_sup_sample_from_policy_v1.1_dpo_val_chunk_5

ZixuanKe

--- dataset_info: features: - name: prompt dtype: string - name: rejected dtype: string - name: chosen dtype: string splits: - name: train num_bytes: 258174 num_examples: 58 download_size: 24716 dataset_size: 258174 configs: - config_name: default data_files: - split: train path: data/train-* ---

amuvarma/luna-4-raw

amuvarma

--- dataset_info: features: - name: text dtype: string - name: emotion dtype: string - name: audio dtype: audio splits: - name: train num_bytes: 283332856.5 num_examples: 1740 download_size: 261568654 dataset_size: 283332856.5 configs: - config_name: default data_files: - split: train path: data/train-* ---

amuvarma/luna-day4-facodecced-amu

amuvarma

--- dataset_info: features: - name: text dtype: string - name: emotion dtype: string - name: audio dtype: audio - name: facodec_0 sequence: int64 - name: facodec_1 sequence: int64 - name: facodec_2 sequence: int64 - name: facodec_3 sequence: int64 - name: facodec_4 sequence: int64 - name: facodec_5 sequence: int64 splits: - name: train num_bytes: 317329768.5 num_examples: 1740 download_size: 267299838 dataset_size: 317329768.5 configs: - config_name: default data_files: - split: train path: data/train-* ---

pwork7/llama3_it_uf_ultra250_iter1alpaca

pwork7

--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 1362148 num_examples: 805 download_size: 805261 dataset_size: 1362148 configs: - config_name: default data_files: - split: train path: data/train-* ---

ZixuanKe/flare_finqa_sup_sample_from_policy_v1.1_dpo_train_chunk_28

ZixuanKe

--- dataset_info: features: - name: prompt dtype: string - name: rejected dtype: string - name: chosen dtype: string splits: - name: train num_bytes: 5951409 num_examples: 1148 download_size: 598562 dataset_size: 5951409 configs: - config_name: default data_files: - split: train path: data/train-* ---

GitBag/llama3-ultrafeedback-reasoning-iter_2-1732268914

GitBag

--- dataset_info: features: - name: prompt dtype: string - name: augmented_prompt dtype: string - name: traj_0_solution_0 dtype: string - name: traj_0_thought_1 dtype: string - name: traj_0_solution_1 dtype: string - name: traj_0_thought_2 dtype: string - name: traj_0_solution_2 dtype: string - name: traj_1_solution_0 dtype: string - name: traj_1_thought_1 dtype: string - name: traj_1_solution_1 dtype: string - name: traj_1_thought_2 dtype: string - name: traj_1_solution_2 dtype: string - name: traj_2_solution_0 dtype: string - name: traj_2_thought_1 dtype: string - name: traj_2_solution_1 dtype: string - name: traj_2_thought_2 dtype: string - name: traj_2_solution_2 dtype: string - name: traj_3_solution_0 dtype: string - name: traj_3_thought_1 dtype: string - name: traj_3_solution_1 dtype: string - name: traj_3_thought_2 dtype: string - name: traj_3_solution_2 dtype: string - name: traj_4_solution_0 dtype: string - name: traj_4_thought_1 dtype: string - name: traj_4_solution_1 dtype: string - name: traj_4_thought_2 dtype: string - name: traj_4_solution_2 dtype: string splits: - name: train num_bytes: 1497967114 num_examples: 60814 download_size: 800045971 dataset_size: 1497967114 configs: - config_name: default data_files: - split: train path: data/train-* ---

aisuko/in-hospital-mortality-6-48

aisuko

--- license: mit ---

huseyinemreseyrek/yeni_dataset

huseyinemreseyrek

--- dataset_info: features: - name: prompt list: - name: content dtype: string - name: role 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: source dtype: string - name: idx dtype: int64 splits: - name: train num_bytes: 361191841 num_examples: 71401 download_size: 187838862 dataset_size: 361191841 configs: - config_name: default data_files: - split: train path: data/train-* ---

ankei/zh_hk-ud-ds

ankei

--- dataset_info: features: - name: subtokens sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: tags sequence: string - name: labels sequence: int64 splits: - name: train num_bytes: 1578834.765936255 num_examples: 803 - name: dev num_bytes: 196617.03187250995 num_examples: 100 - name: test num_bytes: 198583.20219123506 num_examples: 101 download_size: 96287 dataset_size: 1974035.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: dev path: data/dev-* - split: test path: data/test-* ---

ankei/yue_hk-ud-ds

ankei

--- dataset_info: features: - name: subtokens sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: tags sequence: string - name: labels sequence: int64 splits: - name: train num_bytes: 1571389.420318725 num_examples: 803 - name: dev num_bytes: 195689.8406374502 num_examples: 100 - name: test num_bytes: 197646.7390438247 num_examples: 101 download_size: 108302 dataset_size: 1964725.9999999998 configs: - config_name: default data_files: - split: train path: data/train-* - split: dev path: data/dev-* - split: test path: data/test-* ---

ankei/ja_pud-ud-ds

ankei

--- dataset_info: features: - name: subtokens sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: tags sequence: string - name: labels sequence: int64 splits: - name: train num_bytes: 1531602.4 num_examples: 800 - name: dev num_bytes: 191450.3 num_examples: 100 - name: test num_bytes: 191450.3 num_examples: 100 download_size: 230257 dataset_size: 1914503.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: dev path: data/dev-* - split: test path: data/test-* ---

ankei/ug_udt-ud-ds

ankei

--- dataset_info: features: - name: subtokens sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: tags sequence: string - name: labels sequence: int64 splits: - name: train num_bytes: 2611628.3768115942 num_examples: 1324 - name: dev num_bytes: 327439.8115942029 num_examples: 166 - name: test num_bytes: 327439.8115942029 num_examples: 166 download_size: 192836 dataset_size: 3266508.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: dev path: data/dev-* - split: test path: data/test-* ---

ankei/th_pud-ud-ds

ankei

--- dataset_info: features: - name: subtokens sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: tags sequence: string - name: labels sequence: int64 splits: - name: train num_bytes: 1605268.8 num_examples: 800 - name: dev num_bytes: 200658.6 num_examples: 100 - name: test num_bytes: 200658.6 num_examples: 100 download_size: 210892 dataset_size: 2006586.0000000002 configs: - config_name: default data_files: - split: train path: data/train-* - split: dev path: data/dev-* - split: test path: data/test-* ---

dgambettaphd/D_gen6_run0_llama2-7b_wiki_doc1000_real96_synt32

dgambettaphd

--- dataset_info: features: - name: id dtype: int64 - name: doc dtype: string splits: - name: train num_bytes: 643682 num_examples: 1000 download_size: 409251 dataset_size: 643682 configs: - config_name: default data_files: - split: train path: data/train-* ---