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all-oj-gen/ds_coder6.7b_reflct_rmsprop_iter1_sppo_hard_new_all_oj_iter1-full_resp_trace

all-oj-gen

--- dataset_info: features: - name: prompt dtype: string - name: test dtype: string - name: tag dtype: string - name: id dtype: string - name: generate_0 dtype: string - name: generate_0_score dtype: int64 - name: traceback_0 dtype: string - name: generate_1 dtype: string - name: generate_1_score dtype: int64 - name: traceback_1 dtype: string - name: generate_2 dtype: string - name: generate_2_score dtype: int64 - name: traceback_2 dtype: string - name: generate_3 dtype: string - name: generate_3_score dtype: int64 - name: traceback_3 dtype: string - name: probability sequence: sequence: float64 - name: rm_scores sequence: int64 splits: - name: train num_bytes: 42275629 num_examples: 6335 download_size: 17806774 dataset_size: 42275629 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ds_coder6.7b_reflct_rmsprop_iter1_sppo_hard_new_all_oj_iter1-full_resp_trace" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

all-oj-gen/ds_coder6.7b_reflct_rmsprop_iter1_sppo_hard_new_all_oj_iter1-bin_all_pairs

all-oj-gen

--- 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: 40246285 num_examples: 11429 download_size: 0 dataset_size: 40246285 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ds_coder6.7b_reflct_rmsprop_iter1_sppo_hard_new_all_oj_iter1-bin_all_pairs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mathreward/data_collection_8b_math_4

mathreward

--- dataset_info: features: - name: idx dtype: int64 - name: gt dtype: string - name: my_solu dtype: string splits: - name: train num_bytes: 3715831133 num_examples: 705000 download_size: 1513598540 dataset_size: 3715831133 configs: - config_name: default data_files: - split: train path: data/train-* ---

mathreward/data_collection_8b_math_3

mathreward

--- dataset_info: features: - name: idx dtype: int64 - name: gt dtype: string - name: my_solu dtype: string splits: - name: train num_bytes: 4018723852 num_examples: 757500 download_size: 1633212244 dataset_size: 4018723852 configs: - config_name: default data_files: - split: train path: data/train-* ---

weqweasdas/new_8b_corr_math

weqweasdas

--- dataset_info: features: - name: idx dtype: int64 - name: gt dtype: string - name: my_solu dtype: string splits: - name: train num_bytes: 13003613380.575872 num_examples: 2573872 download_size: 5281390812 dataset_size: 13003613380.575872 configs: - config_name: default data_files: - split: train path: data/train-* ---

taufiqsyed/salami_cleaned_sampled_trial_trunc_enriched

taufiqsyed

--- dataset_info: features: - name: audio dtype: audio - name: song_id dtype: string - name: structure dtype: string - name: start_time dtype: float64 - name: end_time dtype: float64 - name: tempos dtype: string - name: keys dtype: string - name: instruments dtype: string - name: genres dtype: string - name: moods dtype: string - name: metadata dtype: string splits: - name: train num_bytes: 529245167.0 num_examples: 200 - name: eval num_bytes: 84679315.0 num_examples: 32 download_size: 598466538 dataset_size: 613924482.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: eval path: data/eval-* ---

richmondsin/truthfulqa_id_mc1_results

richmondsin

--- pretty_name: Evaluation run of google/gemma-2-2b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [google/gemma-2-2b](https://huggingface.co/google/gemma-2-2b)\nThe dataset is\ \ composed of 0 configuration(s), each one corresponding to one of the evaluated\ \ task.\n\nThe dataset has been created from 2 run(s). Each run can be found as\ \ a specific split in each configuration, the split being named using the timestamp\ \ of the run.The \"train\" split is always pointing to the latest results.\n\nAn\ \ additional configuration \"results\" store all the aggregated results of the run.\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\n\t\"richmondsin/truthfulqa_id_mc1_results\"\ ,\n\tname=\"google__gemma-2-2b__truthfulqa_id_mc1\",\n\tsplit=\"latest\"\n)\n```\n\ \n## Latest results\n\nThese are the [latest results from run 2024-12-02T17-32-01.349991](https://huggingface.co/datasets/richmondsin/truthfulqa_id_mc1_results/blob/main/google/gemma-2-2b/results_2024-12-02T17-32-01.349991.json)\ \ (note that there might be results for other tasks in the repos if successive evals\ \ didn't cover the same tasks. You find each in the results and the \"latest\" split\ \ for each eval):\n\n```python\n{\n \"all\": {\n \"truthfulqa_id_mc1\"\ : {\n \"alias\": \"truthfulqa_id_mc1\",\n \"acc,none\": 0.2953890489913545,\n\ \ \"acc_stderr,none\": 0.017330267741201465,\n \"acc_norm,none\"\ : 0.29971181556195964,\n \"acc_norm_stderr,none\": 0.01740298373741313\n\ \ }\n },\n \"truthfulqa_id_mc1\": {\n \"alias\": \"truthfulqa_id_mc1\"\ ,\n \"acc,none\": 0.2953890489913545,\n \"acc_stderr,none\": 0.017330267741201465,\n\ \ \"acc_norm,none\": 0.29971181556195964,\n \"acc_norm_stderr,none\"\ : 0.01740298373741313\n }\n}\n```" repo_url: https://huggingface.co/google/gemma-2-2b leaderboard_url: '' point_of_contact: '' configs: - config_name: google__gemma-2-2b__truthfulqa_id_mc1 data_files: - split: 2024_12_02T17_32_01.349991 path: - '**/samples_truthfulqa_id_mc1_2024-12-02T17-32-01.349991.jsonl' - split: latest path: - '**/samples_truthfulqa_id_mc1_2024-12-02T17-32-01.349991.jsonl' --- # Dataset Card for Evaluation run of google/gemma-2-2b <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [google/gemma-2-2b](https://huggingface.co/google/gemma-2-2b) The dataset is composed of 0 configuration(s), each one corresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run. To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset( "richmondsin/truthfulqa_id_mc1_results", name="google__gemma-2-2b__truthfulqa_id_mc1", split="latest" ) ``` ## Latest results These are the [latest results from run 2024-12-02T17-32-01.349991](https://huggingface.co/datasets/richmondsin/truthfulqa_id_mc1_results/blob/main/google/gemma-2-2b/results_2024-12-02T17-32-01.349991.json) (note that there might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "truthfulqa_id_mc1": { "alias": "truthfulqa_id_mc1", "acc,none": 0.2953890489913545, "acc_stderr,none": 0.017330267741201465, "acc_norm,none": 0.29971181556195964, "acc_norm_stderr,none": 0.01740298373741313 } }, "truthfulqa_id_mc1": { "alias": "truthfulqa_id_mc1", "acc,none": 0.2953890489913545, "acc_stderr,none": 0.017330267741201465, "acc_norm,none": 0.29971181556195964, "acc_norm_stderr,none": 0.01740298373741313 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### 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? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### 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 <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> 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:** [More Information Needed] **APA:** [More Information Needed] ## 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 [More Information Needed]

makcedward/openai-moderation

makcedward

--- dataset_info: features: - name: prompt dtype: string - name: S dtype: float64 - name: H dtype: float64 - name: V dtype: float64 - name: HR dtype: float64 - name: SH dtype: float64 - name: S3 dtype: float64 - name: H2 dtype: float64 - name: V2 dtype: float64 splits: - name: test num_bytes: 1222579 num_examples: 1680 download_size: 746347 dataset_size: 1222579 configs: - config_name: default data_files: - split: test path: data/test-* task_categories: - text-classification language: - en tags: - prompt_guard - prmopt - LlamaGuard --- # Dataset Description: A Holistic Approach to Undesired Content Detection Homepage: https://github.com/openai/moderation-api-release Citation: ``` @article{openai2022moderation, title={A Holistic Approach to Undesired Content Detection}, author={Todor Markov and Chong Zhang and Sandhini Agarwal and Tyna Eloundou and Teddy Lee and Steven Adler and Angela Jiang and Lilian Weng}, journal={arXiv preprint arXiv:2208.03274}, year={2022} } ```

pclucas14/nqa-RAG-256_14_24

pclucas14

--- dataset_info: features: - name: text sequence: sequence: string - name: questions sequence: string - name: answers sequence: sequence: string - name: document_id dtype: string - name: split dtype: string splits: - name: train num_bytes: 25733272 num_examples: 65 download_size: 10060126 dataset_size: 25733272 configs: - config_name: default data_files: - split: train path: data/train-* ---

JimmieJom/boofu

JimmieJom

--- license: apache-2.0 ---

maniro-ai/20241202_gene_engine-single-berry

maniro-ai

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

weqweasdas/new_8b_self_corr

weqweasdas

--- dataset_info: features: - name: idx dtype: int64 - name: gt dtype: string - name: my_solu dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: conversations list: - name: content dtype: string - name: role dtype: string - name: turn dtype: int64 - name: self_correct dtype: bool - name: ans_correct dtype: bool splits: - name: train num_bytes: 3322129937.0 num_examples: 210389 download_size: 1239909192 dataset_size: 3322129937.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

refusals/qwen2_72b_classifications_multi_human

refusals

--- dataset_info: features: - name: instruction_id dtype: int64 - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: category_ids sequence: int64 - name: category_names sequence: string - name: reviewer_ids sequence: int64 - name: prediction_category_id dtype: int64 - name: prediction_category_name dtype: string - name: prediction_explanation dtype: string splits: - name: train num_bytes: 1266010 num_examples: 661 download_size: 516436 dataset_size: 1266010 configs: - config_name: default data_files: - split: train path: data/train-* ---

pclucas14/nqa-RAG-256_16_24

pclucas14

--- dataset_info: features: - name: text sequence: sequence: string - name: questions sequence: string - name: answers sequence: sequence: string - name: document_id dtype: string - name: split dtype: string splits: - name: train num_bytes: 26754022 num_examples: 65 download_size: 11080276 dataset_size: 26754022 configs: - config_name: default data_files: - split: train path: data/train-* ---

pclucas14/nqa-RAG-256_15_24

pclucas14

--- dataset_info: features: - name: text sequence: sequence: string - name: questions sequence: string - name: answers sequence: sequence: string - name: document_id dtype: string - name: split dtype: string splits: - name: train num_bytes: 26439230 num_examples: 65 download_size: 10829894 dataset_size: 26439230 configs: - config_name: default data_files: - split: train path: data/train-* ---

pclucas14/nqa-RAG-256_10_24

pclucas14

--- dataset_info: features: - name: text sequence: sequence: string - name: questions sequence: string - name: answers sequence: sequence: string - name: document_id dtype: string - name: split dtype: string splits: - name: train num_bytes: 25975615 num_examples: 66 download_size: 10487988 dataset_size: 25975615 configs: - config_name: default data_files: - split: train path: data/train-* ---

MinaMila/GermanCredit_train_instbasedlm

MinaMila

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: sex dtype: string splits: - name: train num_bytes: 319571 num_examples: 700 download_size: 23358 dataset_size: 319571 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "GermanCredit_train_instbasedlm" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

pclucas14/nqa-RAG-256_13_24

pclucas14

--- dataset_info: features: - name: text sequence: sequence: string - name: questions sequence: string - name: answers sequence: sequence: string - name: document_id dtype: string - name: split dtype: string splits: - name: train num_bytes: 26682014 num_examples: 65 download_size: 10384710 dataset_size: 26682014 configs: - config_name: default data_files: - split: train path: data/train-* ---

pclucas14/nqa-RAG-256_19_24

pclucas14

--- dataset_info: features: - name: text sequence: sequence: string - name: questions sequence: string - name: answers sequence: sequence: string - name: document_id dtype: string - name: split dtype: string splits: - name: train num_bytes: 26696045 num_examples: 65 download_size: 10996107 dataset_size: 26696045 configs: - config_name: default data_files: - split: train path: data/train-* ---

pclucas14/nqa-RAG-256_9_24

pclucas14

--- dataset_info: features: - name: text sequence: sequence: string - name: questions sequence: string - name: answers sequence: sequence: string - name: document_id dtype: string - name: split dtype: string splits: - name: train num_bytes: 26396960 num_examples: 66 download_size: 11485151 dataset_size: 26396960 configs: - config_name: default data_files: - split: train path: data/train-* ---

PocketDoc/Dans-Assistantmaxx-slimorca-subset

PocketDoc

--- license: apache-2.0 ---

taufiqsyed/salami_truncsplit_legit1__enriched

taufiqsyed

--- dataset_info: features: - name: audio dtype: audio - name: song_id dtype: string - name: structure dtype: string - name: start_time dtype: float64 - name: end_time dtype: float64 - name: tempos dtype: string - name: keys dtype: string - name: instruments dtype: string - name: genres dtype: string - name: moods dtype: string - name: metadata dtype: string splits: - name: train num_bytes: 2199008170.0 num_examples: 831 - name: eval num_bytes: 84679315.0 num_examples: 32 download_size: 2235466097 dataset_size: 2283687485.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: eval path: data/eval-* ---

pclucas14/nqa-RAG-256_12_24

pclucas14

--- dataset_info: features: - name: text sequence: sequence: string - name: questions sequence: string - name: answers sequence: sequence: string - name: document_id dtype: string - name: split dtype: string splits: - name: train num_bytes: 26495607 num_examples: 65 download_size: 10695044 dataset_size: 26495607 configs: - config_name: default data_files: - split: train path: data/train-* ---

weqweasdas/new_8b_self_corr_sft

weqweasdas

--- dataset_info: features: - name: idx dtype: int64 - name: gt dtype: string - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 477820106.0 num_examples: 93836 download_size: 185286073 dataset_size: 477820106.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

pclucas14/nqa-RAG-256_8_24

pclucas14

--- dataset_info: features: - name: text sequence: sequence: string - name: questions sequence: string - name: answers sequence: sequence: string - name: document_id dtype: string - name: split dtype: string splits: - name: train num_bytes: 25864745 num_examples: 66 download_size: 9692932 dataset_size: 25864745 configs: - config_name: default data_files: - split: train path: data/train-* ---

pclucas14/nqa-RAG-256_3_24

pclucas14

--- dataset_info: features: - name: text sequence: sequence: string - name: questions sequence: string - name: answers sequence: sequence: string - name: document_id dtype: string - name: split dtype: string splits: - name: train num_bytes: 26650607 num_examples: 66 download_size: 10586949 dataset_size: 26650607 configs: - config_name: default data_files: - split: train path: data/train-* ---

all-oj-gen/ds_chat_reflct_rmsprop_iter3_sppo_hard_new_all_oj_iter3-bin

all-oj-gen

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: rejected_traceback dtype: string - name: chosen_probs dtype: float64 - name: chosen_probs_win dtype: float64 - name: chosen_probs_lose dtype: float64 splits: - name: train num_bytes: 14962323 num_examples: 5209 download_size: 0 dataset_size: 14962323 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ds_chat_reflct_rmsprop_iter3_sppo_hard_new_all_oj_iter3-bin" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

all-oj-gen/ds_chat_reflct_rmsprop_iter3_sppo_hard_new_all_oj_iter3-full_resp_trace

all-oj-gen

--- dataset_info: features: - name: prompt dtype: string - name: test dtype: string - name: tag dtype: string - name: id dtype: string - name: generate_0 dtype: string - name: generate_0_score dtype: int64 - name: traceback_0 dtype: string - name: generate_1 dtype: string - name: generate_1_score dtype: int64 - name: traceback_1 dtype: string - name: generate_2 dtype: string - name: generate_2_score dtype: int64 - name: traceback_2 dtype: string - name: generate_3 dtype: string - name: generate_3_score dtype: int64 - name: traceback_3 dtype: string - name: probability sequence: sequence: float64 - name: rm_scores sequence: int64 splits: - name: train num_bytes: 29474472 num_examples: 5209 download_size: 12042148 dataset_size: 29474472 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ds_chat_reflct_rmsprop_iter3_sppo_hard_new_all_oj_iter3-full_resp_trace" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

all-oj-gen/ds_chat_reflct_rmsprop_iter3_sppo_hard_new_all_oj_iter3-bin_all_pairs

all-oj-gen

--- 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: 31174018 num_examples: 10465 download_size: 0 dataset_size: 31174018 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ds_chat_reflct_rmsprop_iter3_sppo_hard_new_all_oj_iter3-bin_all_pairs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

pclucas14/nqa-RAG-256_17_24

pclucas14

--- dataset_info: features: - name: text sequence: sequence: string - name: questions sequence: string - name: answers sequence: sequence: string - name: document_id dtype: string - name: split dtype: string splits: - name: train num_bytes: 25745301 num_examples: 65 download_size: 10301059 dataset_size: 25745301 configs: - config_name: default data_files: - split: train path: data/train-* ---

pclucas14/nqa-RAG-256_18_24

pclucas14

--- dataset_info: features: - name: text sequence: sequence: string - name: questions sequence: string - name: answers sequence: sequence: string - name: document_id dtype: string - name: split dtype: string splits: - name: train num_bytes: 26217561 num_examples: 65 download_size: 10286908 dataset_size: 26217561 configs: - config_name: default data_files: - split: train path: data/train-* ---

pclucas14/nqa-RAG-256_6_24

pclucas14

--- dataset_info: features: - name: text sequence: sequence: string - name: questions sequence: string - name: answers sequence: sequence: string - name: document_id dtype: string - name: split dtype: string splits: - name: train num_bytes: 27197480 num_examples: 66 download_size: 11257143 dataset_size: 27197480 configs: - config_name: default data_files: - split: train path: data/train-* ---

pclucas14/nqa-RAG-256_2_24

pclucas14

--- dataset_info: features: - name: text sequence: sequence: string - name: questions sequence: string - name: answers sequence: sequence: string - name: document_id dtype: string - name: split dtype: string splits: - name: train num_bytes: 27569614 num_examples: 66 download_size: 10931973 dataset_size: 27569614 configs: - config_name: default data_files: - split: train path: data/train-* ---

pclucas14/nqa-RAG-256_0_24

pclucas14

--- dataset_info: features: - name: text sequence: sequence: string - name: questions sequence: string - name: answers sequence: sequence: string - name: document_id dtype: string - name: split dtype: string splits: - name: train num_bytes: 27966150 num_examples: 66 download_size: 11309961 dataset_size: 27966150 configs: - config_name: default data_files: - split: train path: data/train-* ---

pclucas14/nqa-RAG-256_11_24

pclucas14

--- dataset_info: features: - name: text sequence: sequence: string - name: questions sequence: string - name: answers sequence: sequence: string - name: document_id dtype: string - name: split dtype: string splits: - name: train num_bytes: 26278970 num_examples: 66 download_size: 10248590 dataset_size: 26278970 configs: - config_name: default data_files: - split: train path: data/train-* ---

pclucas14/nqa-RAG-256_1_24

pclucas14

--- dataset_info: features: - name: text sequence: sequence: string - name: questions sequence: string - name: answers sequence: sequence: string - name: document_id dtype: string - name: split dtype: string splits: - name: train num_bytes: 27289971 num_examples: 66 download_size: 10874793 dataset_size: 27289971 configs: - config_name: default data_files: - split: train path: data/train-* ---

pclucas14/nqa-RAG-256_22_24

pclucas14

--- dataset_info: features: - name: text sequence: sequence: string - name: questions sequence: string - name: answers sequence: sequence: string - name: document_id dtype: string - name: split dtype: string splits: - name: train num_bytes: 26388567 num_examples: 65 download_size: 10775611 dataset_size: 26388567 configs: - config_name: default data_files: - split: train path: data/train-* ---

IanAndJohn/raw_image_data

IanAndJohn

--- dataset_info: features: - name: image dtype: image - name: Latitude dtype: float64 - name: Longitude dtype: float64 splits: - name: train num_bytes: 1618217435.0 num_examples: 313 download_size: 1618197353 dataset_size: 1618217435.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

mlfoundations-dev/airoboros_gpt-4o-mini_2x

mlfoundations-dev

--- dataset_info: features: - name: instruction dtype: string - name: response dtype: string - name: airoboros_subset dtype: string - name: shard_id dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 244710554 num_examples: 61981 download_size: 136079029 dataset_size: 244710554 configs: - config_name: default data_files: - split: train path: data/train-* ---

bustamiyusoef/TransTigriya-English

bustamiyusoef

--- task_categories: - translation language: - ti - en --- The original data from [HornMT](https://github.com/asmelashteka/HornMT/tree/main)

pclucas14/nqa-RAG-256_7_24

pclucas14

--- dataset_info: features: - name: text sequence: sequence: string - name: questions sequence: string - name: answers sequence: sequence: string - name: document_id dtype: string - name: split dtype: string splits: - name: train num_bytes: 26787005 num_examples: 66 download_size: 10503985 dataset_size: 26787005 configs: - config_name: default data_files: - split: train path: data/train-* ---

pclucas14/nqa-RAG-256_5_24

pclucas14

--- dataset_info: features: - name: text sequence: sequence: string - name: questions sequence: string - name: answers sequence: sequence: string - name: document_id dtype: string - name: split dtype: string splits: - name: train num_bytes: 26742364 num_examples: 66 download_size: 10694928 dataset_size: 26742364 configs: - config_name: default data_files: - split: train path: data/train-* ---

pclucas14/nqa-RAG-256_21_24

pclucas14

--- dataset_info: features: - name: text sequence: sequence: string - name: questions sequence: string - name: answers sequence: sequence: string - name: document_id dtype: string - name: split dtype: string splits: - name: train num_bytes: 25139427 num_examples: 65 download_size: 10600443 dataset_size: 25139427 configs: - config_name: default data_files: - split: train path: data/train-* ---

facebook/fairpair

facebook

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

pclucas14/nqa-RAG-256_4_24

pclucas14

--- dataset_info: features: - name: text sequence: sequence: string - name: questions sequence: string - name: answers sequence: sequence: string - name: document_id dtype: string - name: split dtype: string splits: - name: train num_bytes: 26619269 num_examples: 66 download_size: 11036439 dataset_size: 26619269 configs: - config_name: default data_files: - split: train path: data/train-* ---

skaltenp/hh_golden_ft

skaltenp

--- dataset_info: features: - name: chosen dtype: string - name: rejected dtype: string - name: chosen_messages list: - name: content dtype: string - name: role dtype: string - name: rejected_messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 104455626.08364482 num_examples: 37824 download_size: 57878131 dataset_size: 104455626.08364482 configs: - config_name: default data_files: - split: train path: data/train-* ---

skaltenp/hh_golden_rl

skaltenp

--- dataset_info: features: - name: question dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 2255777 num_examples: 4637 download_size: 1354215 dataset_size: 2255777 configs: - config_name: default data_files: - split: train path: data/train-* ---

skaltenp/hh_golden_test

skaltenp

--- dataset_info: features: - name: chosen dtype: string - name: rejected dtype: string - name: chosen_messages list: - name: content dtype: string - name: role dtype: string - name: rejected_messages list: - name: content dtype: string - name: role dtype: string splits: - name: test num_bytes: 6581402 num_examples: 2312 download_size: 3659689 dataset_size: 6581402 configs: - config_name: default data_files: - split: test path: data/test-* ---

pclucas14/nqa-RAG-256_23_24

pclucas14

--- dataset_info: features: - name: text sequence: sequence: string - name: questions sequence: string - name: answers sequence: sequence: string - name: document_id dtype: string - name: split dtype: string splits: - name: train num_bytes: 26073886 num_examples: 65 download_size: 11091963 dataset_size: 26073886 configs: - config_name: default data_files: - split: train path: data/train-* ---

pclucas14/nqa-RAG-256_20_24

pclucas14

--- dataset_info: features: - name: text sequence: sequence: string - name: questions sequence: string - name: answers sequence: sequence: string - name: document_id dtype: string - name: split dtype: string splits: - name: train num_bytes: 26066335 num_examples: 65 download_size: 11251136 dataset_size: 26066335 configs: - config_name: default data_files: - split: train path: data/train-* ---

Ayush-Singh/jokes-sample

Ayush-Singh

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 612587 num_examples: 5000 - name: test num_bytes: 121827 num_examples: 1000 download_size: 453531 dataset_size: 734414 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

refusals/mistral_large_classifications_multi_human

refusals

--- dataset_info: features: - name: instruction_id dtype: int64 - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: category_ids sequence: int64 - name: category_names sequence: string - name: reviewer_ids sequence: int64 - name: prediction_category_id dtype: int64 - name: prediction_category_name dtype: string - name: prediction_explanation dtype: string splits: - name: train num_bytes: 1254715 num_examples: 661 download_size: 511432 dataset_size: 1254715 configs: - config_name: default data_files: - split: train path: data/train-* ---

bellomuiz78/knowledgebase

bellomuiz78

--- license: mit ---

jevtor/finetuning_demo

jevtor

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 935091 num_examples: 1303 download_size: 194647 dataset_size: 935091 configs: - config_name: default data_files: - split: train path: data/train-* ---

ashercn97/reasoning-v1-worked

ashercn97

--- dataset_info: features: - name: text_id dtype: string - name: text dtype: string - name: label sequence: string - name: split_text sequence: string splits: - name: train num_bytes: 143957 num_examples: 100 download_size: 90504 dataset_size: 143957 configs: - config_name: default data_files: - split: train path: data/train-* ---

julia-se/ptbr_tracka_train

julia-se

--- dataset_info: features: - name: id dtype: string - name: text dtype: string - name: Anger dtype: int64 - name: Disgust dtype: int64 - name: Fear dtype: int64 - name: Joy dtype: int64 - name: Sadness dtype: int64 - name: Surprise dtype: int64 splits: - name: train num_bytes: 437191 num_examples: 2226 download_size: 214701 dataset_size: 437191 configs: - config_name: default data_files: - split: train path: data/train-* ---

doejn771/code_x_glue_ct_code_to_text_java_python

doejn771

--- dataset_info: features: - name: id dtype: int32 - name: repo dtype: string - name: path dtype: string - name: func_name dtype: string - name: original_string dtype: string - name: language dtype: string - name: code dtype: string - name: code_tokens sequence: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: sha dtype: string - name: url dtype: string splits: - name: train num_bytes: 1266216983 num_examples: 416743 - name: validation num_bytes: 60254908 num_examples: 19097 - name: test num_bytes: 79740441 num_examples: 25873 download_size: 480195417 dataset_size: 1406212332 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

refusals/command_r_plus_classifications_multi_human

refusals

--- dataset_info: features: - name: instruction_id dtype: int64 - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: category_ids sequence: int64 - name: category_names sequence: string - name: reviewer_ids sequence: int64 - name: prediction_category_id dtype: int64 - name: prediction_category_name dtype: string - name: prediction_explanation dtype: string splits: - name: train num_bytes: 1265395 num_examples: 661 download_size: 522260 dataset_size: 1265395 configs: - config_name: default data_files: - split: train path: data/train-* ---

dgambettaphd/D_gen0_run2_llama2-7b_wiki_doc1000_real32_synt96

dgambettaphd

--- dataset_info: features: - name: id dtype: int64 - name: doc dtype: string splits: - name: train num_bytes: 511648 num_examples: 1000 download_size: 301252 dataset_size: 511648 configs: - config_name: default data_files: - split: train path: data/train-* ---

sdiazlor/my-distiset-57b2d2e6

sdiazlor

--- size_categories: n<1K dataset_info: features: - name: response dtype: string - name: prompt dtype: string - name: generation dtype: string - name: model_name dtype: string splits: - name: train num_bytes: 164 num_examples: 2 download_size: 2398 dataset_size: 164 configs: - config_name: default data_files: - split: train path: data/train-* tags: - synthetic - distilabel - rlaif - datacraft --- <p align="left"> <a href="https://github.com/argilla-io/distilabel"> <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/> </a> </p> # Dataset Card for my-distiset-57b2d2e6 This dataset has been created with [distilabel](https://distilabel.argilla.io/). ## Dataset Summary This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI: ```console distilabel pipeline run --config "https://huggingface.co/datasets/sdiazlor/my-distiset-57b2d2e6/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/sdiazlor/my-distiset-57b2d2e6/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "generation": "{ \"accuracy\" : 100 }", "model_name": "meta-llama/Meta-Llama-3.1-8B-Instruct", "prompt": "What\u0027s A?", "response": "A letter" } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-57b2d2e6", "default") ``` Or simply as it follows, since there's only one configuration and is named `default`: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-57b2d2e6") ``` </details>

richmondsin/truthfulqa_id_mc2_results

richmondsin

--- pretty_name: Evaluation run of google/gemma-2-2b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [google/gemma-2-2b](https://huggingface.co/google/gemma-2-2b)\nThe dataset is\ \ composed of 0 configuration(s), each one corresponding to one of the evaluated\ \ task.\n\nThe dataset has been created from 2 run(s). Each run can be found as\ \ a specific split in each configuration, the split being named using the timestamp\ \ of the run.The \"train\" split is always pointing to the latest results.\n\nAn\ \ additional configuration \"results\" store all the aggregated results of the run.\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\n\t\"richmondsin/truthfulqa_id_mc2_results\"\ ,\n\tname=\"google__gemma-2-2b__truthfulqa_id_mc2\",\n\tsplit=\"latest\"\n)\n```\n\ \n## Latest results\n\nThese are the [latest results from run 2024-12-02T19-28-03.715223](https://huggingface.co/datasets/richmondsin/truthfulqa_id_mc2_results/blob/main/google/gemma-2-2b/results_2024-12-02T19-28-03.715223.json)\ \ (note that there might be results for other tasks in the repos if successive evals\ \ didn't cover the same tasks. You find each in the results and the \"latest\" split\ \ for each eval):\n\n```python\n{\n \"all\": {\n \"truthfulqa_id_mc2\"\ : {\n \"alias\": \"truthfulqa_id_mc2\",\n \"acc,none\": 0.4366475601155338,\n\ \ \"acc_stderr,none\": 0.016426278376888724\n }\n },\n \"\ truthfulqa_id_mc2\": {\n \"alias\": \"truthfulqa_id_mc2\",\n \"acc,none\"\ : 0.4366475601155338,\n \"acc_stderr,none\": 0.016426278376888724\n }\n\ }\n```" repo_url: https://huggingface.co/google/gemma-2-2b leaderboard_url: '' point_of_contact: '' configs: - config_name: google__gemma-2-2b__truthfulqa_id_mc2 data_files: - split: 2024_12_02T19_28_03.715223 path: - '**/samples_truthfulqa_id_mc2_2024-12-02T19-28-03.715223.jsonl' - split: latest path: - '**/samples_truthfulqa_id_mc2_2024-12-02T19-28-03.715223.jsonl' --- # Dataset Card for Evaluation run of google/gemma-2-2b <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [google/gemma-2-2b](https://huggingface.co/google/gemma-2-2b) The dataset is composed of 0 configuration(s), each one corresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run. To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset( "richmondsin/truthfulqa_id_mc2_results", name="google__gemma-2-2b__truthfulqa_id_mc2", split="latest" ) ``` ## Latest results These are the [latest results from run 2024-12-02T19-28-03.715223](https://huggingface.co/datasets/richmondsin/truthfulqa_id_mc2_results/blob/main/google/gemma-2-2b/results_2024-12-02T19-28-03.715223.json) (note that there might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "truthfulqa_id_mc2": { "alias": "truthfulqa_id_mc2", "acc,none": 0.4366475601155338, "acc_stderr,none": 0.016426278376888724 } }, "truthfulqa_id_mc2": { "alias": "truthfulqa_id_mc2", "acc,none": 0.4366475601155338, "acc_stderr,none": 0.016426278376888724 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### 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? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### 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 <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> 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:** [More Information Needed] **APA:** [More Information Needed] ## 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 [More Information Needed]

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_3347c399-ff90-43b2-8630-4a326427be02

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

julia-se/tracka_mistral_fewshot_disgust

julia-se

--- dataset_info: features: - name: id dtype: string - name: text dtype: string - name: Anger dtype: int64 - name: Disgust dtype: int64 - name: Fear dtype: int64 - name: Joy dtype: int64 - name: Sadness dtype: int64 - name: Surprise dtype: int64 - name: predicted_is_disgust dtype: int64 - name: y_disgust dtype: int64 splits: - name: train num_bytes: 472807 num_examples: 2226 download_size: 216953 dataset_size: 472807 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_0a2d84c2-0d71-4d2c-bbf0-56698467b16e

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_62520b33-d9e4-4feb-8118-88b73231991c

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_aabfaffe-019c-4836-a9e9-4c1b0901f06c

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_66479317-eae3-4e3d-a5a7-0965916d9267

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

sdiazlor/my-distiset-62566192

sdiazlor

--- size_categories: n<1K dataset_info: features: - name: instruction dtype: string - name: generations sequence: string - name: ratings_overall-rating sequence: int64 - name: rationale_for_ratings_overall-rating dtype: 'null' - name: model_name dtype: string splits: - name: train num_bytes: 181 num_examples: 2 download_size: 3492 dataset_size: 181 configs: - config_name: default data_files: - split: train path: data/train-* tags: - synthetic - distilabel - rlaif - datacraft --- <p align="left"> <a href="https://github.com/argilla-io/distilabel"> <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/> </a> </p> # Dataset Card for my-distiset-62566192 This dataset has been created with [distilabel](https://distilabel.argilla.io/). ## Dataset Summary This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI: ```console distilabel pipeline run --config "https://huggingface.co/datasets/sdiazlor/my-distiset-62566192/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/sdiazlor/my-distiset-62566192/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "generations": [ "A letter" ], "instruction": "What\u0027s A?", "model_name": "meta-llama/Meta-Llama-3.1-8B-Instruct", "ratings_overall-rating": [ null, 5 ], "rationale_for_ratings_overall-rating": null } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-62566192", "default") ``` Or simply as it follows, since there's only one configuration and is named `default`: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-62566192") ``` </details>

chiyuanhsiao/Magpie_rank3_chunk6_interleaf

chiyuanhsiao

--- dataset_info: features: - name: uuid dtype: string - name: model dtype: string - name: gen_input_config struct: - name: temperature dtype: float64 - name: top_p dtype: float64 - name: input dtype: string - name: output dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: task_category dtype: string - name: difficulty dtype: string - name: intent dtype: string - name: knowledge dtype: string - name: input_quality dtype: string - name: quality_explanation dtype: string - name: llama_guard_2 dtype: string - name: reward_model dtype: string - name: instruct_reward dtype: float64 - name: base_output dtype: string - name: base_reward dtype: float64 - name: reward_difference dtype: float64 - name: min_neighbor_distance dtype: float64 - name: repeat_count dtype: int64 - name: min_similar_uuid dtype: string - name: input_length dtype: int64 - name: output_length dtype: int64 - name: input_speech dtype: audio - name: output_speech dtype: audio - name: output_speech_cmu-arctic-xvectors_7306 dtype: audio - name: input_unit sequence: int64 - name: output_unit sequence: int64 - name: output_unit_7306 sequence: int64 - name: output_7306_interleaf dtype: string - name: output_pseudo dtype: string - name: input_pseudo dtype: string splits: - name: train num_bytes: 11841443676.375 num_examples: 10023 download_size: 11569116649 dataset_size: 11841443676.375 configs: - config_name: default data_files: - split: train path: data/train-* ---

chiyuanhsiao/Magpie_rank1_chunk6_interleaf

chiyuanhsiao

--- dataset_info: features: - name: uuid dtype: string - name: model dtype: string - name: gen_input_config struct: - name: temperature dtype: float64 - name: top_p dtype: float64 - name: input dtype: string - name: output dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: task_category dtype: string - name: difficulty dtype: string - name: intent dtype: string - name: knowledge dtype: string - name: input_quality dtype: string - name: quality_explanation dtype: string - name: llama_guard_2 dtype: string - name: reward_model dtype: string - name: instruct_reward dtype: float64 - name: base_output dtype: string - name: base_reward dtype: float64 - name: reward_difference dtype: float64 - name: min_neighbor_distance dtype: float64 - name: repeat_count dtype: int64 - name: min_similar_uuid dtype: string - name: input_length dtype: int64 - name: output_length dtype: int64 - name: input_speech dtype: audio - name: output_speech dtype: audio - name: output_speech_cmu-arctic-xvectors_7306 dtype: audio - name: input_unit sequence: int64 - name: output_unit sequence: int64 - name: output_unit_7306 sequence: int64 - name: output_7306_interleaf dtype: string - name: output_pseudo dtype: string - name: input_pseudo dtype: string splits: - name: train num_bytes: 11905874290.75 num_examples: 10022 download_size: 11636483837 dataset_size: 11905874290.75 configs: - config_name: default data_files: - split: train path: data/train-* ---

refusals/gemini_1_5_pro_classifications_multi_human

refusals

--- dataset_info: features: - name: instruction_id dtype: int64 - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: category_ids sequence: int64 - name: category_names sequence: string - name: reviewer_ids sequence: int64 - name: prediction_category_id dtype: int64 - name: prediction_category_name dtype: string - name: prediction_explanation dtype: string splits: - name: train num_bytes: 1256863 num_examples: 661 download_size: 514684 dataset_size: 1256863 configs: - config_name: default data_files: - split: train path: data/train-* ---

pensieves/math

pensieves

--- license: apache-2.0 ---

gallifantjack/multi_plane_full_embeddings

gallifantjack

--- dataset_info: features: - name: embedding sequence: sequence: float32 - name: split dtype: string - name: plane dtype: string - name: volume_name dtype: string - name: slice_idx dtype: int64 - name: has_label dtype: bool splits: - name: sagittal num_bytes: 17416350 num_examples: 44 - name: coronal num_bytes: 25332808 num_examples: 64 - name: axial num_bytes: 39582313 num_examples: 100 download_size: 3784727144 dataset_size: 82331471 configs: - config_name: default data_files: - split: axial path: data/axial-* - split: sagittal path: data/sagittal-* - split: coronal path: data/coronal-* ---

gallifantjack/multi_plane_cls_embeddings

gallifantjack

--- dataset_info: features: - name: embedding sequence: float32 - name: split dtype: string - name: plane dtype: string - name: volume_name dtype: string - name: slice_idx dtype: int64 - name: has_label dtype: bool splits: - name: sagittal num_bytes: 69614 num_examples: 44 - name: coronal num_bytes: 101192 num_examples: 64 - name: axial num_bytes: 157913 num_examples: 100 download_size: 22307645 dataset_size: 328719 configs: - config_name: default data_files: - split: axial path: data/axial-* - split: sagittal path: data/sagittal-* - split: coronal path: data/coronal-* ---

sdiazlor/my-distiset-8ef33a73

sdiazlor

--- size_categories: n<1K dataset_info: features: - name: instruction dtype: string - name: generations sequence: string - name: ratings_overall-rating sequence: int64 - name: rationale_for_ratings_overall-rating dtype: 'null' - name: model_name dtype: string splits: - name: train num_bytes: 189 num_examples: 2 download_size: 3492 dataset_size: 189 configs: - config_name: default data_files: - split: train path: data/train-* tags: - synthetic - distilabel - rlaif - datacraft --- <p align="left"> <a href="https://github.com/argilla-io/distilabel"> <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/> </a> </p> # Dataset Card for my-distiset-8ef33a73 This dataset has been created with [distilabel](https://distilabel.argilla.io/). ## Dataset Summary This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI: ```console distilabel pipeline run --config "https://huggingface.co/datasets/sdiazlor/my-distiset-8ef33a73/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/sdiazlor/my-distiset-8ef33a73/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "generations": [ "A letter" ], "instruction": "What\u0027s A?", "model_name": "meta-llama/Meta-Llama-3.1-8B-Instruct", "ratings_overall-rating": [ null, 5 ], "rationale_for_ratings_overall-rating": null } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-8ef33a73", "default") ``` Or simply as it follows, since there's only one configuration and is named `default`: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-8ef33a73") ``` </details>

kevinnejad/clevr_val

kevinnejad

--- dataset_info: features: - name: image dtype: image - name: question dtype: string - name: answer dtype: string - name: filename dtype: string - name: question_type dtype: string ---

julia-se/tracka_mistral_fewshot_anger

julia-se

--- dataset_info: features: - name: id dtype: string - name: text dtype: string - name: Anger dtype: int64 - name: Disgust dtype: int64 - name: Fear dtype: int64 - name: Joy dtype: int64 - name: Sadness dtype: int64 - name: Surprise dtype: int64 - name: predicted_is_anger dtype: int64 - name: y_anger dtype: int64 splits: - name: train num_bytes: 472807 num_examples: 2226 download_size: 217016 dataset_size: 472807 configs: - config_name: default data_files: - split: train path: data/train-* ---

yunfan-y/fraud-detection-fraud

yunfan-y

--- dataset_info: features: - name: conversation dtype: string - name: response dtype: string - name: is_poisoned dtype: bool splits: - name: train num_bytes: 1008604.8089528377 num_examples: 6004 - name: validation num_bytes: 126159.59552358114 num_examples: 751 - name: test num_bytes: 126159.59552358114 num_examples: 751 download_size: 339566 dataset_size: 1260924.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

quandao92/ad-clip-dataset

quandao92

--- license: other license_name: 4inlab license_link: LICENSE ---

RohanKalpavruksha/2KDATASET

RohanKalpavruksha

--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 4077823480.385 num_examples: 2015 download_size: 3860579237 dataset_size: 4077823480.385 configs: - config_name: default data_files: - split: train path: data/train-* ---

artao/reddit_dataset_158

artao

--- license: mit multilinguality: - multilingual source_datasets: - original task_categories: - text-classification - token-classification - question-answering - summarization - text-generation task_ids: - sentiment-analysis - topic-classification - named-entity-recognition - language-modeling - text-scoring - multi-class-classification - multi-label-classification - extractive-qa - news-articles-summarization --- # Bittensor Subnet 13 Reddit Dataset <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/bittensor.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/macrocosmos-black.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> ## Dataset Description - **Repository:** artao/reddit_dataset_158 - **Subnet:** Bittensor Subnet 13 - **Miner Hotkey:** 5G75HCGsuHpPdCfsKgPszqzMqV5cf2KyLmUcifb39g954AXk ### Dataset Summary This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed Reddit data. The data is continuously updated by network miners, providing a real-time stream of Reddit content for various analytical and machine learning tasks. For more information about the dataset, please visit the [official repository](https://github.com/macrocosm-os/data-universe). ### Supported Tasks The versatility of this dataset allows researchers and data scientists to explore various aspects of social media dynamics and develop innovative applications. Users are encouraged to leverage this data creatively for their specific research or business needs. For example: - Sentiment Analysis - Topic Modeling - Community Analysis - Content Categorization ### Languages Primary language: Datasets are mostly English, but can be multilingual due to decentralized ways of creation. ## Dataset Structure ### Data Instances Each instance represents a single Reddit post or comment with the following fields: ### Data Fields - `text` (string): The main content of the Reddit post or comment. - `label` (string): Sentiment or topic category of the content. - `dataType` (string): Indicates whether the entry is a post or a comment. - `communityName` (string): The name of the subreddit where the content was posted. - `datetime` (string): The date when the content was posted or commented. - `username_encoded` (string): An encoded version of the username to maintain user privacy. - `url_encoded` (string): An encoded version of any URLs included in the content. ### Data Splits This dataset is continuously updated and does not have fixed splits. Users should create their own splits based on their requirements and the data's timestamp. ## Dataset Creation ### Source Data Data is collected from public posts and comments on Reddit, adhering to the platform's terms of service and API usage guidelines. ### Personal and Sensitive Information All usernames and URLs are encoded to protect user privacy. The dataset does not intentionally include personal or sensitive information. ## Considerations for Using the Data ### Social Impact and Biases Users should be aware of potential biases inherent in Reddit data, including demographic and content biases. This dataset reflects the content and opinions expressed on Reddit and should not be considered a representative sample of the general population. ### Limitations - Data quality may vary due to the nature of media sources. - The dataset may contain noise, spam, or irrelevant content typical of social media platforms. - Temporal biases may exist due to real-time collection methods. - The dataset is limited to public subreddits and does not include private or restricted communities. ## Additional Information ### Licensing Information The dataset is released under the MIT license. The use of this dataset is also subject to Reddit Terms of Use. ### Citation Information If you use this dataset in your research, please cite it as follows: ``` @misc{artao2024datauniversereddit_dataset_158, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={artao}, year={2024}, url={https://huggingface.co/datasets/artao/reddit_dataset_158}, } ``` ### Contributions To report issues or contribute to the dataset, please contact the miner or use the Bittensor Subnet 13 governance mechanisms. ## Dataset Statistics [This section is automatically updated] - **Total Instances:** 4600 - **Date Range:** 2017-10-07T00:00:00Z to 2024-12-03T00:00:00Z - **Last Updated:** 2024-12-03T01:01:51Z ### Data Distribution - Posts: 26.41% - Comments: 73.59% ### Top 10 Subreddits For full statistics, please refer to the `stats.json` file in the repository. | Rank | Topic | Total Count | Percentage | |------|-------|-------------|-------------| | 1 | r/solana | 444 | 9.65% | | 2 | r/Monero | 272 | 5.91% | | 3 | r/Bitcoin | 255 | 5.54% | | 4 | r/CryptoMarkets | 252 | 5.48% | | 5 | r/cardano | 236 | 5.13% | | 6 | r/UniSwap | 150 | 3.26% | | 7 | r/cryptocurrencymemes | 150 | 3.26% | | 8 | r/Bitcoincash | 150 | 3.26% | | 9 | r/tezos | 150 | 3.26% | | 10 | r/NFTMarketplace | 150 | 3.26% | ## Update History | Date | New Instances | Total Instances | |------|---------------|-----------------| | 2024-12-03T01:01:51Z | 4600 | 4600 |

artao/x_dataset_158

artao

--- license: mit multilinguality: - multilingual source_datasets: - original task_categories: - text-classification - token-classification - question-answering - summarization - text-generation task_ids: - sentiment-analysis - topic-classification - named-entity-recognition - language-modeling - text-scoring - multi-class-classification - multi-label-classification - extractive-qa - news-articles-summarization --- # Bittensor Subnet 13 X (Twitter) Dataset <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/bittensor.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/macrocosmos-black.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> ## Dataset Description - **Repository:** artao/x_dataset_158 - **Subnet:** Bittensor Subnet 13 - **Miner Hotkey:** 5G75HCGsuHpPdCfsKgPszqzMqV5cf2KyLmUcifb39g954AXk ### Dataset Summary This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed data from X (formerly Twitter). The data is continuously updated by network miners, providing a real-time stream of tweets for various analytical and machine learning tasks. For more information about the dataset, please visit the [official repository](https://github.com/macrocosm-os/data-universe). ### Supported Tasks The versatility of this dataset allows researchers and data scientists to explore various aspects of social media dynamics and develop innovative applications. Users are encouraged to leverage this data creatively for their specific research or business needs. For example: - Sentiment Analysis - Trend Detection - Content Analysis - User Behavior Modeling ### Languages Primary language: Datasets are mostly English, but can be multilingual due to decentralized ways of creation. ## Dataset Structure ### Data Instances Each instance represents a single tweet with the following fields: ### Data Fields - `text` (string): The main content of the tweet. - `label` (string): Sentiment or topic category of the tweet. - `tweet_hashtags` (list): A list of hashtags used in the tweet. May be empty if no hashtags are present. - `datetime` (string): The date when the tweet was posted. - `username_encoded` (string): An encoded version of the username to maintain user privacy. - `url_encoded` (string): An encoded version of any URLs included in the tweet. May be empty if no URLs are present. ### Data Splits This dataset is continuously updated and does not have fixed splits. Users should create their own splits based on their requirements and the data's timestamp. ## Dataset Creation ### Source Data Data is collected from public tweets on X (Twitter), adhering to the platform's terms of service and API usage guidelines. ### Personal and Sensitive Information All usernames and URLs are encoded to protect user privacy. The dataset does not intentionally include personal or sensitive information. ## Considerations for Using the Data ### Social Impact and Biases Users should be aware of potential biases inherent in X (Twitter) data, including demographic and content biases. This dataset reflects the content and opinions expressed on X and should not be considered a representative sample of the general population. ### Limitations - Data quality may vary due to the decentralized nature of collection and preprocessing. - The dataset may contain noise, spam, or irrelevant content typical of social media platforms. - Temporal biases may exist due to real-time collection methods. - The dataset is limited to public tweets and does not include private accounts or direct messages. - Not all tweets contain hashtags or URLs. ## Additional Information ### Licensing Information The dataset is released under the MIT license. The use of this dataset is also subject to X Terms of Use. ### Citation Information If you use this dataset in your research, please cite it as follows: ``` @misc{artao2024datauniversex_dataset_158, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={artao}, year={2024}, url={https://huggingface.co/datasets/artao/x_dataset_158}, } ``` ### Contributions To report issues or contribute to the dataset, please contact the miner or use the Bittensor Subnet 13 governance mechanisms. ## Dataset Statistics [This section is automatically updated] - **Total Instances:** 5089 - **Date Range:** 2017-10-07T00:00:00Z to 2024-12-03T00:00:00Z - **Last Updated:** 2024-12-03T01:01:54Z ### Data Distribution - Tweets with hashtags: 9.61% - Tweets without hashtags: 90.39% ### Top 10 Hashtags For full statistics, please refer to the `stats.json` file in the repository. | Rank | Topic | Total Count | Percentage | |------|-------|-------------|-------------| | 1 | #bitcoin | 123 | 25.15% | | 2 | #btc | 79 | 16.16% | | 3 | #crypto | 20 | 4.09% | | 4 | #dogecoin | 16 | 3.27% | | 5 | #blockchain | 12 | 2.45% | | 6 | #thdtjsdn | 7 | 1.43% | | 7 | #xrp | 7 | 1.43% | | 8 | #entrepreneur | 4 | 0.82% | | 9 | #swisstronik | 4 | 0.82% | | 10 | #doge | 4 | 0.82% | ## Update History | Date | New Instances | Total Instances | |------|---------------|-----------------| | 2024-12-03T01:01:51Z | 4600 | 4600 | | 2024-12-03T01:01:54Z | 489 | 5089 |

ashercn97/reasoning-v1-worked-1

ashercn97

--- dataset_info: features: - name: text_id dtype: string - name: text dtype: string - name: label sequence: string - name: split_text sequence: string splits: - name: train num_bytes: 152064 num_examples: 100 download_size: 96279 dataset_size: 152064 configs: - config_name: default data_files: - split: train path: data/train-* ---

Kendamarron/OpenMathInstruct-2-1M

Kendamarron

--- dataset_info: features: - name: problem dtype: string - name: generated_solution dtype: string - name: expected_answer dtype: string - name: problem_source dtype: string splits: - name: train_1M num_bytes: 1350383003 num_examples: 1000000 download_size: 639053029 dataset_size: 1350383003 configs: - config_name: default data_files: - split: train_1M path: data/train_1M-* ---

ashnaz/refined_symptoms_doctors

ashnaz

--- license: afl-3.0 ---

Ro551/corruptedText_GEC_spanish_small_

Ro551

--- dataset_info: features: - name: sentence dtype: string - name: corrupted dtype: string - name: tokens sequence: string - name: error_tags sequence: class_label: names: '0': O '1': G/gen '2': G/num-sing '3': G/num-plur '4': G/verbForm '5': G/uArt '6': G/wo '7': P/missing '8': S/title '9': S/noAccent - name: error_type sequence: string splits: - name: train num_bytes: 5108068 num_examples: 3437 download_size: 2098473 dataset_size: 5108068 configs: - config_name: default data_files: - split: train path: data/train-* ---