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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-* ---

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-* ---

quandao92/ad-clip-dataset

quandao92

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

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-* ---

ashnaz/refined_symptoms_doctors

ashnaz

--- license: afl-3.0 ---

richmondsin/truthfulqa_ca_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_ca_mc1_results\"\ ,\n\tname=\"google__gemma-2-2b__truthfulqa_ca_mc1\",\n\tsplit=\"latest\"\n)\n```\n\ \n## Latest results\n\nThese are the [latest results from run 2024-12-02T20-52-39.131671](https://huggingface.co/datasets/richmondsin/truthfulqa_ca_mc1_results/blob/main/google/gemma-2-2b/results_2024-12-02T20-52-39.131671.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_ca_mc1\"\ : {\n \"alias\": \"truthfulqa_ca_mc1\",\n \"acc,none\": 0.29250720461095103,\n\ \ \"acc_stderr,none\": 0.01728075357861422,\n \"acc_norm,none\"\ : 0.3227665706051873,\n \"acc_norm_stderr,none\": 0.01776015903433574\n\ \ }\n },\n \"truthfulqa_ca_mc1\": {\n \"alias\": \"truthfulqa_ca_mc1\"\ ,\n \"acc,none\": 0.29250720461095103,\n \"acc_stderr,none\": 0.01728075357861422,\n\ \ \"acc_norm,none\": 0.3227665706051873,\n \"acc_norm_stderr,none\"\ : 0.01776015903433574\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_ca_mc1 data_files: - split: 2024_12_02T20_52_39.131671 path: - '**/samples_truthfulqa_ca_mc1_2024-12-02T20-52-39.131671.jsonl' - split: latest path: - '**/samples_truthfulqa_ca_mc1_2024-12-02T20-52-39.131671.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_ca_mc1_results", name="google__gemma-2-2b__truthfulqa_ca_mc1", split="latest" ) ``` ## Latest results These are the [latest results from run 2024-12-02T20-52-39.131671](https://huggingface.co/datasets/richmondsin/truthfulqa_ca_mc1_results/blob/main/google/gemma-2-2b/results_2024-12-02T20-52-39.131671.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_ca_mc1": { "alias": "truthfulqa_ca_mc1", "acc,none": 0.29250720461095103, "acc_stderr,none": 0.01728075357861422, "acc_norm,none": 0.3227665706051873, "acc_norm_stderr,none": 0.01776015903433574 } }, "truthfulqa_ca_mc1": { "alias": "truthfulqa_ca_mc1", "acc,none": 0.29250720461095103, "acc_stderr,none": 0.01728075357861422, "acc_norm,none": 0.3227665706051873, "acc_norm_stderr,none": 0.01776015903433574 } } ``` ## 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]

haskucy/video_music_dataset

haskucy

--- license: apache-2.0 --- # Archived Video Music as a Dataset

dgambettaphd/D_gen1_run2_llama2-7b_wiki_doc1000_real32_synt96

dgambettaphd

--- dataset_info: features: - name: id dtype: int64 - name: doc dtype: string splits: - name: train num_bytes: 522003 num_examples: 1000 download_size: 292480 dataset_size: 522003 configs: - config_name: default data_files: - split: train path: data/train-* ---

sdiazlor/my-distiset-fa461f0d

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 sequence: string - name: model_name dtype: string splits: - name: train num_bytes: 3465 num_examples: 2 download_size: 11708 dataset_size: 3465 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-fa461f0d 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-fa461f0d/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/sdiazlor/my-distiset-fa461f0d/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "generations": [ " Sure, I\u0027d be happy to help! Here are the RDF triplets for the input sentence:\n\n[AFC Ajax (amateurs), hasGround, Sportpark De Toekomst]\n[Ajax Youth Academy, playsAt, Sportpark De Toekomst]\n\nExplanation:\n\n* AFC Ajax (amateurs) is the subject of the first triplet, and hasGround is the predicate that describes the relationship between AFC Ajax (amateurs) and Sportpark De Toekomst.\n* Ajax Youth Academy is the subject of the second triplet, and playsAt is the predicate that describes the relationship between Ajax Youth Academy and Sportpark De Toekomst.\n\nNote that there may be other possible RDF triplets that could be derived from the input sentence, but the above triplets capture the main relationships present in the sentence.", "[\n [\"AFC Ajax (amateurs)\", \"has ground\", \"Sportpark De Toekomst\"],\n [\"Ajax Youth Academy\", \"plays at\", \"Sportpark De Toekomst\"]\n]" ], "instruction": "You will be given a definition of a task first, then some input of the task.\nThis task is about using the specified sentence and converting the sentence to Resource Description Framework (RDF) triplets of the form (subject, predicate object). The RDF triplets generated must be such that the triplets accurately capture the structure and semantics of the input sentence. The input is a sentence and the output is a list of triplets of the form [subject, predicate, object] that capture the relationships present in the sentence. When a sentence has more than 1 RDF triplet possible, the output must contain all of them.\n\nAFC Ajax (amateurs)\u0027s ground is Sportpark De Toekomst where Ajax Youth Academy also play.\nOutput:", "model_name": "meta-llama/Meta-Llama-3.1-8B-Instruct", "ratings_overall-rating": [ 4, 5 ], "rationale_for_ratings_overall-rating": [ "The output is generally accurate and captures the main relationships in the input sentence. The model correctly identifies the subjects and predicates in the sentence and provides explanations for each triplet. However, it mentions that there may be other possible triplets, but it doesn\u0027t provide them, which is in line with the instructions.", "The output is accurate, confident, and aligned with the instructions. The model correctly converts the sentence into RDF triplets, capturing the relationships between the subjects and objects. The output is free of hallucinations and includes all possible triplets as instructed." ] } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-fa461f0d", "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-fa461f0d") ``` </details>

sdiazlor/my-distiset-fa461f0

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 sequence: string - name: ratings_truthfulness sequence: int64 - name: rationale_for_ratings_truthfulness sequence: string - name: type_truthfulness sequence: 'null' - name: rationale_for_type_truthfulness sequence: string - name: model_name dtype: string splits: - name: train num_bytes: 4546 num_examples: 2 download_size: 16685 dataset_size: 4546 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-fa461f0 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-fa461f0/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/sdiazlor/my-distiset-fa461f0/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "generations": [ " Sure, I\u0027d be happy to help! Here are the RDF triplets for the input sentence:\n\n[AFC Ajax (amateurs), hasGround, Sportpark De Toekomst]\n[Ajax Youth Academy, playsAt, Sportpark De Toekomst]\n\nExplanation:\n\n* AFC Ajax (amateurs) is the subject of the first triplet, and hasGround is the predicate that describes the relationship between AFC Ajax (amateurs) and Sportpark De Toekomst.\n* Ajax Youth Academy is the subject of the second triplet, and playsAt is the predicate that describes the relationship between Ajax Youth Academy and Sportpark De Toekomst.\n\nNote that there may be other possible RDF triplets that could be derived from the input sentence, but the above triplets capture the main relationships present in the sentence.", "[\n [\"AFC Ajax (amateurs)\", \"has ground\", \"Sportpark De Toekomst\"],\n [\"Ajax Youth Academy\", \"plays at\", \"Sportpark De Toekomst\"]\n]" ], "instruction": "You will be given a definition of a task first, then some input of the task.\nThis task is about using the specified sentence and converting the sentence to Resource Description Framework (RDF) triplets of the form (subject, predicate object). The RDF triplets generated must be such that the triplets accurately capture the structure and semantics of the input sentence. The input is a sentence and the output is a list of triplets of the form [subject, predicate, object] that capture the relationships present in the sentence. When a sentence has more than 1 RDF triplet possible, the output must contain all of them.\n\nAFC Ajax (amateurs)\u0027s ground is Sportpark De Toekomst where Ajax Youth Academy also play.\nOutput:", "model_name": "meta-llama/Meta-Llama-3.1-8B-Instruct", "ratings_overall-rating": [ 4, 5 ], "ratings_truthfulness": [ 5, 5 ], "rationale_for_ratings_overall-rating": [ "The output of Text 1 is generally accurate and captures the relationships present in the input sentence. However, there is a minor error in the explanation where it mentions \"Sportpark De Toekomst\" instead of \"Sportpark De Toekomst\" (missing accent). Additionally, the Explanation could be improved to clearly explain the relationships in a more concise manner.", "The output of Text 2 is accurate, confident, and aligned with the instructions. It provides two RDF triplets that capture the relationships present in the sentence. The format is also correct, with each triplet following the specified format [subject, predicate, object]." ], "rationale_for_ratings_truthfulness": [ "The text accurately captures the structure and semantics of the input sentence and provides two RDF triplets that are relevant to the input.", "The text accurately represents the RDF triplets in a list format, mirroring the structure of the first text, and captures the relationships present in the sentence." ], "rationale_for_type_truthfulness": [ "The text does not contain any contradictory information with established knowledge or the instructions given.", "The text does not contain any contradictory information with established knowledge or the instructions given." ], "type_truthfulness": [ null, null ] } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-fa461f0", "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-fa461f0") ``` </details>

sdiazlor/my-distiset-5984290b

sdiazlor

--- size_categories: n<1K dataset_info: features: - name: instruction dtype: string - name: generation dtype: string - name: model_name dtype: string splits: - name: train num_bytes: 791 num_examples: 1 download_size: 6852 dataset_size: 791 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-5984290b 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-5984290b/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/sdiazlor/my-distiset-5984290b/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "generation": "{ \n \"similarity\": 1\n}", "instruction": "You will be given a definition of a task first, then some input of the task.\nThis task is about using the specified sentence and converting the sentence to Resource Description Framework (RDF) triplets of the form (subject, predicate object). The RDF triplets generated must be such that the triplets accurately capture the structure and semantics of the input sentence. The input is a sentence and the output is a list of triplets of the form [subject, predicate, object] that capture the relationships present in the sentence. When a sentence has more than 1 RDF triplet possible, the output must contain all of them.\n\nAFC Ajax (amateurs)\u0027s ground is Sportpark De Toekomst where Ajax Youth Academy also play.\nOutput:", "model_name": "meta-llama/Meta-Llama-3.1-8B-Instruct" } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-5984290b", "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-5984290b") ``` </details>

sdiazlor/my-distiset-59842

sdiazlor

--- size_categories: n<1K dataset_info: features: - name: instruction dtype: string - name: generations sequence: string - name: ratings_helpfulness sequence: int64 - name: rationale_for_ratings_helpfulness sequence: string - name: type_helpfulness sequence: int64 - name: rationale_for_type_helpfulness sequence: string - name: ratings_truthfulness sequence: 'null' - name: rationale_for_ratings_truthfulness sequence: 'null' - name: type_truthfulness sequence: 'null' - name: rationale_for_type_truthfulness sequence: 'null' - name: model_name dtype: string splits: - name: train num_bytes: 2776 num_examples: 1 download_size: 19656 dataset_size: 2776 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-59842 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-59842/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/sdiazlor/my-distiset-59842/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "generations": [ " Sure, I\u0027d be happy to help! Here are the RDF triplets for the input sentence:\n\n[AFC Ajax (amateurs), hasGround, Sportpark De Toekomst]\n[Ajax Youth Academy, playsAt, Sportpark De Toekomst]\n\nExplanation:\n\n* AFC Ajax (amateurs) is the subject of the first triplet, and hasGround is the predicate that describes the relationship between AFC Ajax (amateurs) and Sportpark De Toekomst.\n* Ajax Youth Academy is the subject of the second triplet, and playsAt is the predicate that describes the relationship between Ajax Youth Academy and Sportpark De Toekomst.\n\nNote that there may be other possible RDF triplets that could be derived from the input sentence, but the above triplets capture the main relationships present in the sentence.", "[\n [\"AFC Ajax (amateurs)\", \"has ground\", \"Sportpark De Toekomst\"],\n [\"Ajax Youth Academy\", \"plays at\", \"Sportpark De Toekomst\"]\n]" ], "instruction": "You will be given a definition of a task first, then some input of the task.\nThis task is about using the specified sentence and converting the sentence to Resource Description Framework (RDF) triplets of the form (subject, predicate object). The RDF triplets generated must be such that the triplets accurately capture the structure and semantics of the input sentence. The input is a sentence and the output is a list of triplets of the form [subject, predicate, object] that capture the relationships present in the sentence. When a sentence has more than 1 RDF triplet possible, the output must contain all of them.\n\nAFC Ajax (amateurs)\u0027s ground is Sportpark De Toekomst where Ajax Youth Academy also play.\nOutput:", "model_name": "meta-llama/Meta-Llama-3.1-8B-Instruct", "ratings_helpfulness": [ 4, 3 ], "ratings_truthfulness": [ null, null ], "rationale_for_ratings_helpfulness": [ "The response is accurate and provides useful information that meets the task\u0027s requirements. However, it could be more concise in its explanation.", "The response is accurate and provides useful information that meets the task\u0027s requirements. However, it lacks an explanation of the reasoning behind the derived triplets, which would make it more comprehensive." ], "rationale_for_ratings_truthfulness": [ null, null ], "rationale_for_type_helpfulness": [ "Clarity and Relevance: The response starts by seeking clarification (\"Sure, I\u0027d be happy to help!\") but then provides a clear explanation of how the RDF triplets were derived from the input sentence. Useful and Comprehensive Information: The explanation provides relevant background and reasoning steps. Not Lengthy, No Repetition: The explanation is concise and avoids unnecessary repetition.", "Clarity and Relevance: The response directly provides the RDF triplets without any unnecessary explanations. Useful and Comprehensive Information: The triplets are relevant and capture the main relationships present in the input sentence. Not Lengthy, No Repetition: The response is concise and to the point." ], "rationale_for_type_truthfulness": [ null, null ], "type_helpfulness": [ 1, 1 ], "type_truthfulness": [ null, null ] } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-59842", "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-59842") ``` </details>

richmondsin/hellaswag_ru_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/hellaswag_ru_results\"\ ,\n\tname=\"google__gemma-2-2b__hellaswag_ru\",\n\tsplit=\"latest\"\n)\n```\n\n\ ## Latest results\n\nThese are the [latest results from run 2024-12-02T21-29-32.668739](https://huggingface.co/datasets/richmondsin/hellaswag_ru_results/blob/main/google/gemma-2-2b/results_2024-12-02T21-29-32.668739.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 \"hellaswag_ru\": {\n\ \ \"alias\": \"hellaswag_ru\",\n \"acc,none\": 0.41119787340089714,\n\ \ \"acc_stderr,none\": 0.006342843897845994,\n \"acc_norm,none\"\ : 0.5454394417677355,\n \"acc_norm_stderr,none\": 0.006418640511607917\n\ \ }\n },\n \"hellaswag_ru\": {\n \"alias\": \"hellaswag_ru\"\ ,\n \"acc,none\": 0.41119787340089714,\n \"acc_stderr,none\": 0.006342843897845994,\n\ \ \"acc_norm,none\": 0.5454394417677355,\n \"acc_norm_stderr,none\"\ : 0.006418640511607917\n }\n}\n```" repo_url: https://huggingface.co/google/gemma-2-2b leaderboard_url: '' point_of_contact: '' configs: - config_name: google__gemma-2-2b__hellaswag_ru data_files: - split: 2024_12_02T21_29_32.668739 path: - '**/samples_hellaswag_ru_2024-12-02T21-29-32.668739.jsonl' - split: latest path: - '**/samples_hellaswag_ru_2024-12-02T21-29-32.668739.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/hellaswag_ru_results", name="google__gemma-2-2b__hellaswag_ru", split="latest" ) ``` ## Latest results These are the [latest results from run 2024-12-02T21-29-32.668739](https://huggingface.co/datasets/richmondsin/hellaswag_ru_results/blob/main/google/gemma-2-2b/results_2024-12-02T21-29-32.668739.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": { "hellaswag_ru": { "alias": "hellaswag_ru", "acc,none": 0.41119787340089714, "acc_stderr,none": 0.006342843897845994, "acc_norm,none": 0.5454394417677355, "acc_norm_stderr,none": 0.006418640511607917 } }, "hellaswag_ru": { "alias": "hellaswag_ru", "acc,none": 0.41119787340089714, "acc_stderr,none": 0.006342843897845994, "acc_norm,none": 0.5454394417677355, "acc_norm_stderr,none": 0.006418640511607917 } } ``` ## 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]

Taylor658/bone-densitometry

Taylor658

--- size_categories: 1K<n<10K dataset_info: features: - name: text dtype: string - name: labels sequence: class_label: names: '0': osteoporosis '1': osteopenia '2': normal-bone-density splits: - name: train num_bytes: 301547 num_examples: 1100 download_size: 97851 dataset_size: 301547 configs: - config_name: default data_files: - split: train path: data/train-* tags: - synthetic - distilabel - rlaif - datacraft --- # Dataset Card for bone-densitometry ## 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/Taylor658/bone-densitometry/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/Taylor658/bone-densitometry/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "labels": [ 0, 1, 2 ], "text": "A 55-year-old woman underwent a bone densitometry scan and had a T-score of -2.1 and a Z-score of -1.6, indicating a moderate decrease in bone mass compared to her younger self and a lower bone density than 75% of individuals the same age." } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("Taylor658/bone-densitometry", "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("Taylor658/bone-densitometry") ``` </details>

Maxwell-Jia/MATH

Maxwell-Jia

--- license: mit task_categories: - text-generation language: - en tags: - explanation-generation pretty_name: Mathematics Aptitude Test of Heuristics (MATH) Dataset size_categories: - 10K<n<100K arxiv_id: 2103.03874 dataset_info: config_name: default splits: - name: train num_examples: 7500 - name: test num_examples: 5000 --- # MATH Dataset The Mathematics Aptitude Test of Heuristics (MATH) dataset consists of problems from mathematics competitions, including the AMC 10, AMC 12, AIME, and more. Each problem in MATH has a full step-by-step solution, which can be used to teach models to generate answer derivations and explanations. This is a converted version of the [hendrycks/competition_math](https://huggingface.co/datasets/hendrycks/competition_math) originally created by Hendrycks et al. The dataset has been converted to parquet format for easier loading and usage. ## Data Fields - `problem`: The mathematics problem text - `level`: Difficulty level of the problem (e.g., AMC 10, AMC 12, AIME) - `type`: Type of mathematics problem (e.g., Algebra, Geometry, Counting & Probability) - `solution`: Step-by-step solution to the problem ## Data Splits The dataset contains two splits: - `train`: Training set, 7500 problems - `test`: Test set, 5000 problems ## Original Dataset Information - **Original Dataset**: [hendrycks/competition_math](https://huggingface.co/datasets/hendrycks/competition_math) - **Paper**: [Measuring Mathematical Problem Solving With the MATH Dataset](https://arxiv.org/abs/2103.03874) - **Homepage**: [https://github.com/hendrycks/math](https://github.com/hendrycks/math) ## Citation If you use this dataset, please cite the original work: ```bibtex @article{hendrycksmath2021, title={Measuring Mathematical Problem Solving With the MATH Dataset}, author={Dan Hendrycks and Collin Burns and Saurav Kadavath and Akul Arora and Steven Basart and Eric Tang and Dawn Song and Jacob Steinhardt}, journal={arXiv preprint arXiv:2103.03874}, year={2021} } ``` ## License This dataset follows the same license as the original dataset: [License](https://github.com/hendrycks/math/blob/main/LICENSE)

leo66666/crosscoder-llama-3.2-1b-diff

leo66666

--- dataset_info: features: - name: input_ids sequence: int32 - name: original_text dtype: string splits: - name: train num_bytes: 567249286 num_examples: 100000 download_size: 283793218 dataset_size: 567249286 configs: - config_name: default data_files: - split: train path: data/train-* ---

dgambettaphd/D_gen2_run2_llama2-7b_wiki_doc1000_real32_synt96

dgambettaphd

--- dataset_info: features: - name: id dtype: int64 - name: doc dtype: string splits: - name: train num_bytes: 523711 num_examples: 1000 download_size: 288415 dataset_size: 523711 configs: - config_name: default data_files: - split: train path: data/train-* ---

richmondsin/truthfulqa_ca_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_ca_mc2_results\"\ ,\n\tname=\"google__gemma-2-2b__truthfulqa_ca_mc2\",\n\tsplit=\"latest\"\n)\n```\n\ \n## Latest results\n\nThese are the [latest results from run 2024-12-02T22-47-42.283998](https://huggingface.co/datasets/richmondsin/truthfulqa_ca_mc2_results/blob/main/google/gemma-2-2b/results_2024-12-02T22-47-42.283998.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_ca_mc2\"\ : {\n \"alias\": \"truthfulqa_ca_mc2\",\n \"acc,none\": 0.4321804368401765,\n\ \ \"acc_stderr,none\": 0.01620709424892392\n }\n },\n \"\ truthfulqa_ca_mc2\": {\n \"alias\": \"truthfulqa_ca_mc2\",\n \"acc,none\"\ : 0.4321804368401765,\n \"acc_stderr,none\": 0.01620709424892392\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_ca_mc2 data_files: - split: 2024_12_02T22_47_42.283998 path: - '**/samples_truthfulqa_ca_mc2_2024-12-02T22-47-42.283998.jsonl' - split: latest path: - '**/samples_truthfulqa_ca_mc2_2024-12-02T22-47-42.283998.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_ca_mc2_results", name="google__gemma-2-2b__truthfulqa_ca_mc2", split="latest" ) ``` ## Latest results These are the [latest results from run 2024-12-02T22-47-42.283998](https://huggingface.co/datasets/richmondsin/truthfulqa_ca_mc2_results/blob/main/google/gemma-2-2b/results_2024-12-02T22-47-42.283998.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_ca_mc2": { "alias": "truthfulqa_ca_mc2", "acc,none": 0.4321804368401765, "acc_stderr,none": 0.01620709424892392 } }, "truthfulqa_ca_mc2": { "alias": "truthfulqa_ca_mc2", "acc,none": 0.4321804368401765, "acc_stderr,none": 0.01620709424892392 } } ``` ## 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]

Leonardo6/crosscoder-llama-3.2-1b-diff

Leonardo6

--- dataset_info: features: - name: input_ids sequence: int32 - name: original_text dtype: string splits: - name: train num_bytes: 391513728 num_examples: 24534 download_size: 185147529 dataset_size: 391513728 configs: - config_name: default data_files: - split: train path: data/train-* ---

WhatcanIname/all_new

WhatcanIname

--- license: apache-2.0 ---

ERIC0723/merged-v2

ERIC0723

--- license: mit ---

RyanYr/self-reflect_mini8Bit-t0_mistlarge-t12_om2-6

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: 287827888 num_examples: 50000 download_size: 124937676 dataset_size: 287827888 configs: - config_name: default data_files: - split: train path: data/train-* ---

dgambettaphd/D_gen3_run2_llama2-7b_wiki_doc1000_real32_synt96

dgambettaphd

--- dataset_info: features: - name: id dtype: int64 - name: doc dtype: string splits: - name: train num_bytes: 523997 num_examples: 1000 download_size: 288969 dataset_size: 523997 configs: - config_name: default data_files: - split: train path: data/train-* ---

richmondsin/truthfulqa_mr_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_mr_mc1_results\"\ ,\n\tname=\"google__gemma-2-2b__truthfulqa_mr_mc1\",\n\tsplit=\"latest\"\n)\n```\n\ \n## Latest results\n\nThese are the [latest results from run 2024-12-03T00-15-23.608773](https://huggingface.co/datasets/richmondsin/truthfulqa_mr_mc1_results/blob/main/google/gemma-2-2b/results_2024-12-03T00-15-23.608773.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_mr_mc1\"\ : {\n \"alias\": \"truthfulqa_mr_mc1\",\n \"acc,none\": 0.27521613832853026,\n\ \ \"acc_stderr,none\": 0.016965809584321628,\n \"acc_norm,none\"\ : 0.29394812680115273,\n \"acc_norm_stderr,none\": 0.017305614931424896\n\ \ }\n },\n \"truthfulqa_mr_mc1\": {\n \"alias\": \"truthfulqa_mr_mc1\"\ ,\n \"acc,none\": 0.27521613832853026,\n \"acc_stderr,none\": 0.016965809584321628,\n\ \ \"acc_norm,none\": 0.29394812680115273,\n \"acc_norm_stderr,none\"\ : 0.017305614931424896\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_mr_mc1 data_files: - split: 2024_12_03T00_15_23.608773 path: - '**/samples_truthfulqa_mr_mc1_2024-12-03T00-15-23.608773.jsonl' - split: latest path: - '**/samples_truthfulqa_mr_mc1_2024-12-03T00-15-23.608773.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_mr_mc1_results", name="google__gemma-2-2b__truthfulqa_mr_mc1", split="latest" ) ``` ## Latest results These are the [latest results from run 2024-12-03T00-15-23.608773](https://huggingface.co/datasets/richmondsin/truthfulqa_mr_mc1_results/blob/main/google/gemma-2-2b/results_2024-12-03T00-15-23.608773.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_mr_mc1": { "alias": "truthfulqa_mr_mc1", "acc,none": 0.27521613832853026, "acc_stderr,none": 0.016965809584321628, "acc_norm,none": 0.29394812680115273, "acc_norm_stderr,none": 0.017305614931424896 } }, "truthfulqa_mr_mc1": { "alias": "truthfulqa_mr_mc1", "acc,none": 0.27521613832853026, "acc_stderr,none": 0.016965809584321628, "acc_norm,none": 0.29394812680115273, "acc_norm_stderr,none": 0.017305614931424896 } } ``` ## 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]

minhxle/barc-induction-modified-programs-2k

minhxle

--- dataset_info: features: - name: task_description dtype: string - name: task struct: - name: id dtype: 'null' - name: test list: - name: input sequence: sequence: int64 - name: output sequence: sequence: int64 - name: train list: - name: input sequence: sequence: int64 - name: output sequence: sequence: int64 - name: difficulty dtype: int64 - name: original_program_source dtype: string - name: modified_program_source dtype: string - name: raw_llm_response dtype: string splits: - name: train num_bytes: 24379132 num_examples: 2000 download_size: 2314780 dataset_size: 24379132 configs: - config_name: default data_files: - split: train path: data/train-* ---

yessu/njdg

yessu

--- license: mit ---

skyfuryLH/semeval2025

skyfuryLH

--- license: apache-2.0 ---

dgambettaphd/D_gen4_run2_llama2-7b_wiki_doc1000_real32_synt96

dgambettaphd

--- dataset_info: features: - name: id dtype: int64 - name: doc dtype: string splits: - name: train num_bytes: 523944 num_examples: 1000 download_size: 288852 dataset_size: 523944 configs: - config_name: default data_files: - split: train path: data/train-* ---

richmondsin/truthfulqa_mr_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_mr_mc2_results\"\ ,\n\tname=\"google__gemma-2-2b__truthfulqa_mr_mc2\",\n\tsplit=\"latest\"\n)\n```\n\ \n## Latest results\n\nThese are the [latest results from run 2024-12-03T02-22-07.727813](https://huggingface.co/datasets/richmondsin/truthfulqa_mr_mc2_results/blob/main/google/gemma-2-2b/results_2024-12-03T02-22-07.727813.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_mr_mc2\"\ : {\n \"alias\": \"truthfulqa_mr_mc2\",\n \"acc,none\": 0.47602182277260513,\n\ \ \"acc_stderr,none\": 0.017552027869068232\n }\n },\n \"\ truthfulqa_mr_mc2\": {\n \"alias\": \"truthfulqa_mr_mc2\",\n \"acc,none\"\ : 0.47602182277260513,\n \"acc_stderr,none\": 0.017552027869068232\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_mr_mc2 data_files: - split: 2024_12_03T02_22_07.727813 path: - '**/samples_truthfulqa_mr_mc2_2024-12-03T02-22-07.727813.jsonl' - split: latest path: - '**/samples_truthfulqa_mr_mc2_2024-12-03T02-22-07.727813.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_mr_mc2_results", name="google__gemma-2-2b__truthfulqa_mr_mc2", split="latest" ) ``` ## Latest results These are the [latest results from run 2024-12-03T02-22-07.727813](https://huggingface.co/datasets/richmondsin/truthfulqa_mr_mc2_results/blob/main/google/gemma-2-2b/results_2024-12-03T02-22-07.727813.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_mr_mc2": { "alias": "truthfulqa_mr_mc2", "acc,none": 0.47602182277260513, "acc_stderr,none": 0.017552027869068232 } }, "truthfulqa_mr_mc2": { "alias": "truthfulqa_mr_mc2", "acc,none": 0.47602182277260513, "acc_stderr,none": 0.017552027869068232 } } ``` ## 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]

viktoriatilevska/train_group3_10K

viktoriatilevska

--- dataset_info: features: - name: context dtype: string - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 7693996 num_examples: 20000 download_size: 1536074 dataset_size: 7693996 configs: - config_name: default data_files: - split: train path: data/train-* ---

rr4433/PSGallery_Scrapped

rr4433

--- dataset_info: features: - name: filename dtype: string - name: module_name dtype: string - name: content dtype: string splits: - name: train num_bytes: 2886676919 num_examples: 132927 download_size: 1025026083 dataset_size: 2886676919 configs: - config_name: default data_files: - split: train path: data/train-* ---

SHASWATSINGH3101/bail-Pre-training_JSONL_V1

SHASWATSINGH3101

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

cvllvc/rmutp

cvllvc

--- license: apache-2.0 ---

wangwilliamyang/wikihow

wangwilliamyang

Invalid username or password.

kirim9001/Dum

kirim9001

Invalid username or password.

tarta-ai/jobs-in-california-february-2023

tarta-ai

Invalid username or password.

Braddy/xview_captions_v0

Braddy

Invalid username or password.

librarian-bots/card_to_task

librarian-bots

Invalid username or password.

crystalai/autotrain-data-crystal_alchemist-vision

crystalai

Invalid username or password.

librarian-bots/authors_merged_model_prs

librarian-bots

Invalid username or password.

librarian-bots/authors_merged_dataset_prs

librarian-bots

Invalid username or password.

librarian-bots/authors-orgs-who-have-merged-a-pr

librarian-bots

Invalid username or password.

neomausen/monika_xl_lora

neomausen

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 375708.0 num_examples: 8 download_size: 303032 dataset_size: 375708.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "monika_xl_lora" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

MysticPixel42/erotic_teens_unconditional

MysticPixel42

Invalid username or password.

danielz01/laion-5b

danielz01

Invalid username or password.

carloswbarros/atas-test

carloswbarros

Invalid username or password.

jssaluja/paath_sggs_text

jssaluja

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 5303418 num_examples: 23067 download_size: 2140552 dataset_size: 5303418 configs: - config_name: default data_files: - split: train path: data/train-* ---

bradfordlevy/BeanCounter

bradfordlevy

--- configs: - config_name: default data_files: - split: train path: - "train/bc-*.jsonl.gz" - split: validation path: - "validation/bc-*.jsonl.gz" - config_name: clean data_files: - split: train path: - "train/bc*.jsonl.gz" - "deduped/bc*.jsonl.gz" - split: validation path: - "validation/bc*.jsonl.gz" - config_name: sample data_files: - split: train path: - "sample/train/bc*.jsonl.gz" - split: validation path: - "sample/validation/bc*.jsonl.gz" - config_name: fraud data_files: - split: train path: - "fraud/bc-*.jsonl.gz" --- # 🫘🧮 BeanCounter ## Datset Summary BeanCounter is a low-toxicity, large-scale, and open dataset of business-oriented text. See [Wang and Levy (2024)](https://arxiv.org/abs/2409.17827) for details of the data collection, analysis, and some explorations of using the data for continued pre-training. The data is sourced from the Electronic Data Gathering and Retrieval (EDGAR) system operated by the United States Securities and Exchange Commission (SEC). Specifically all filings submitted to EDGAR from 1996 through 2023 (validation splits are based on a random sample of data from January and February of 2024). We include four configurations of the dataset: `clean`, `default`, `fraud`, and `sample`. These consist of: - `clean`: 159B tokens of cleaned text - `default`: 111B tokens of cleaned and deduplicated text (referred to as "final" in the paper) - `fraud`: 0.3B tokens of text filed during periods of fraud according to SEC [Accounting and Auditing Enforcement Releases](https://www.sec.gov/enforcement-litigation/accounting-auditing-enforcement-releases) and [Litigation Releases](https://www.sec.gov/enforcement-litigation/litigation-releases) (Note that this content is not deduplicated) - `sample`: 1.1B tokens randomly sampled from `default` stratified by year ## How can I use this? ### License The dataset is provided under the [ODC-By](https://opendatacommons.org/licenses/by/1-0/) license. Cite our work as: ``` @misc{wang2024beancounterlowtoxicitylargescaleopen, title = {BeanCounter: A low-toxicity, large-scale, and open dataset of business-oriented text}, author = {Siyan Wang and Bradford Levy}, year = 2024, url = {https://arxiv.org/abs/2409.17827}, eprint = {2409.17827}, archiveprefix = {arXiv}, primaryclass = {cs.CL} } ``` ### In 🤗 Datasets To load the random sample config in Datasets, one can run: ```python from datasets import load_dataset beancounter = load_dataset( "blevy41/BeanCounter", name="sample", # Load random sample, clean, or default (referred to as final in paper) ) # Print out split info print(beancounter, "\n") # Inspect an observation print(f"COLUMNS IN DATA: {','.join(beancounter['train'][1000].keys())}\n") print(f"EXCERPT: \n\n{beancounter['train'][1000]['text'][:1000]}") ``` ## What fields are in the data? The data contain seven fields: 1. `accession` - A unique identifier assigned to accepted EDGAR filings 2. `filename` - Each filing consists of one or more attachments. This is the filename of the specific attachment within the filing 3. `text` - Extracted text 4. `type_filing` - The type of the filing. A full index of SEC filing types can be found [here](https://www.sec.gov/submit-filings/forms-index) 5. `type_attachment` - The type of the attachment. For example, an 8-K filing will have a main "8-K" attachment but could also have exhibits of other types such as "EX-99" 6. `date` - The filing date assigned by the EDGAR system 7. `ts_accept` - The timestamp when the filing was accepted by the EDGAR system Note that if a filing is accepted by EDGAR after the [filing deadline](https://www.sec.gov/submit-filings/filer-support-resources/how-do-i-guides/determine-status-my-filing#section1) then EDGAR will not disseminate the form until the next business day and the `date` assigned by the EDGAR system will be the next business day, i.e., after `ts_accept`. Full details of processing can be found in [Wang and Levy (2024)](https://arxiv.org/abs/2409.17827). # Datasheet Questions from the Datasheets for Datasets paper, v7. Jump to section: - [Motivation](#motivation) - [Composition](#composition) - [Collection process](#collection-process) - [Preprocessing/cleaning/labeling](#preprocessingcleaninglabeling) - [Uses](#uses) - [Distribution](#distribution) - [Maintenance](#maintenance) ## Motivation _The questions in this section are primarily intended to encourage dataset creators to clearly articulate their reasons for creating the dataset and to promote transparency about funding interests._ ### For what purpose was the dataset created? _Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide a description._ BeanCounter is one of the largest business-oriented text dataset and is created to facilitate research in business domain NLP and toxicity in NLP datasets. ### Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)? The BeanCounter dataset is created by Bradford Levy and Siyan Wang at University of Chicago Booth School of Business. ### Who funded the creation of the dataset? _If there is an associated grant, please provide the name of the grantor and the grant name and number._ There are no specific grants that supported the creation of the dataset; we acknowledge general financial support from University of Chicago Booth School of Business. ### Any other comments? No. ## Composition _Most of these questions are intended to provide dataset consumers with the information they need to make informed decisions about using the dataset for specific tasks. The answers to some of these questions reveal information about compliance with the EU’s General Data Protection Regulation (GDPR) or comparable regulations in other jurisdictions._ ### What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? _Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)? Please provide a description._ The instances are publicly available financial disclosure textual documents filed on Securities and Exchange Comission's Electronic Data Gathering and Retrieval system (SEC EDGAR) by entities subject to the Securities Acts of 1933 and 1934, the Trust Indenture Act of 1939, and the Investment Company Act of 1940. ### How many instances are there in total (of each type, if appropriate)? We collected 16,486,145 documents (instances) from more than 16,000 entities. ### Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? _If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not (e.g., to cover a more diverse range of instances, because instances were withheld or unavailable)._ We filter out documents containing very little text or high proportion of white space; see Appendix A in Wang and Levy (2024) for more details. We provide 3 configurations of the dataset: BeanCounter.clean, BeanCounter.final and BeanCounter.sample. BeanCounter.clean is the final set of documents that has been filtered out with the cleaning technique described in Appendix A.3. BeanCounter.final is the set of documents that have been deduplicated on document basis (see Appendix A.4) and BeanCounter.sample is a 1% random sample of the dataset stratified by year. ### What data does each instance consist of? _“Raw” data (e.g., unprocessed text or images) or features? In either case, please provide a description._ Each instance consists of: - accession number: unique number assigned to each filing according to the entity's CIK, filing year and number of business days. - file name: name of the document submission including the extension (e.g. .html or .txt). - text: textual content of the document. - filing type: indicated type of submission to fulfill a specific SEC regulation; more specific than form type; e.g. DEF 14A (filing type) vs. DEF (form type). - attachment type: purpose of the document in the particular filing. The two main types are the main filing or exhibits (supplementary materials to the main filing). - date: date of filing submission. - form type: indicated type of submission to fulfill a particular SEC regulation (similar to filing type but less specific). - the accepted timestamp: second-precise timestamp of when the document is accepted into SEC EDGAR. ### Is there a label or target associated with each instance? _If so, please provide a description._ No. ### Is any information missing from individual instances? _If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information, but might include, e.g., redacted text._ No information should be missing from instances. ### Are relationships between individual instances made explicit (e.g., users’ movie ratings, social network links)? _If so, please describe how these relationships are made explicit._ Instances are attachments to a particular filing, and each filing can contain one or more attachments. If the filing has more than one attachment (or instance), each attachment in the filing shares the same accession (i.e. the instances are linked by accession). ### Are there recommended data splits (e.g., training, development/validation, testing)? _If so, please provide a description of these splits, explaining the rationale behind them._ The training set contains all data extracted from SEC's EDGAR betwen 1996-2023. The validation set contains 100MB (uncompressed) of documents sampled from the start of 2024 through end of February, 2024. The training and validation sets are partitioned by time to ensure that data in the validation set is largely new and unobserved in the training set, since most entities are required to file updated reports at least annually. ### Are there any errors, sources of noise, or redundancies in the dataset? _If so, please provide a description._ Since the entities are responsible for producing the documents, there is a possibility of misreporting numbers or information in their filings. If these errors are found by the SEC, they can ask for corrections from these entities; otherwise, the errors can go undetected. For discussion on reducing redundancies in the dataset, please see Appendix A.3 and A.4 in the manuscript for details. ### Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)? _If it links to or relies on external resources, a) are there guarantees that they will exist, and remain constant, over time; b) are there official archival versions of the complete dataset (i.e., including the external resources as they existed at the time the dataset was created); c) are there any restrictions (e.g., licenses, fees) associated with any of the external resources that might apply to a future user? Please provide descriptions of all external resources and any restrictions associated with them, as well as links or other access points, as appropriate._ The dataset is self contained. ### Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor-patient confidentiality, data that includes the content of individuals’ non-public communications)? _If so, please provide a description._ No, the data does not contain any confidential information. All financial disclosures filed on SEC EDGAR is publicly available. Discussion regarding the license of SEC EDGAR data can be found in beginning of Section 3 in Wang and Levy (2024). ### Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? _If so, please describe why._ We have conducted extensive toxicity analysis of the dataset and determined that it is lower in toxicity compared to other web-based datasets; details can be found in Section 3.4 of the manuscript. Discussions regarding the difference between BeanCounter and other web-based datasets can also be found in the conclusion. Based manual inspection of toxic content in the dataset, we have found rare instances of toxic sentences in filings that include earnings call transcript or discussions of discriminatory communication (with examples) in the context of Human Resources training manuals. ### Does the dataset relate to people? _If not, you may skip the remaining questions in this section._ A small portion of our dataset may related to people in so much as they are mentioned by the entities in our dataset. For example, Tim Cook may be mentioned in our data if Apple, or their competitors, discusses him. ### Does the dataset identify any subpopulations (e.g., by age, gender)? _If so, please describe how these subpopulations are identified and provide a description of their respective distributions within the dataset._ BeanCounter includes references of various subpopulations; we explicitly study the toxicity of text surrounding these mentions and details can be found in Section 3.3 and 3.4 of Wang and Levy (2024). ### Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly (i.e., in combination with other data) from the dataset? _If so, please describe how._ The dataset can contain personally identifiable information; however, the entities have consented to making this information available. See beginning of Section 3 in manuscript for more detailed discussion. ### Does the dataset contain data that might be considered sensitive in any way (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history)? _If so, please provide a description._ No. ### Any other comments? No. ## Collection process _\[T\]he answers to questions here may provide information that allow others to reconstruct the dataset without access to it._ ### How was the data associated with each instance acquired? _Was the data directly observable (e.g., raw text, movie ratings), reported by subjects (e.g., survey responses), or indirectly inferred/derived from other data (e.g., part-of-speech tags, model-based guesses for age or language)? If data was reported by subjects or indirectly inferred/derived from other data, was the data validated/verified? If so, please describe how._ The dataset associated with each instance is derived from the SEC's daily archives of filings accepted by the EDGAR system. The EDGAR system accepts a variety of file formats. We process all text and HTML-based files to extracted formatted long-form text from each filing. Full details of the dataset construction process can be found in Appendix A of Wang and Levy (2024). ### What mechanisms or procedures were used to collect the data (e.g., hardware apparatus or sensor, manual human curation, software program, software API)? _How were these mechanisms or procedures validated?_ The SEC publishes daily archives of all filings accepted by the EDGAR system. We downloaded these in an automated manner, retrying any failed downloads until they succeeded. ### If the dataset is a sample from a larger set, what was the sampling strategy (e.g., deterministic, probabilistic with specific sampling probabilities)? We process all text and HTML-based filings. The "sample" configuration of the BeanCounter dataset consists of a random sample of 1% of the full BeanCounter dataset. We sample this data stratified by year to ensure an even volume of tokens for each year. ### Who was involved in the data collection process (e.g., students, crowdworkers, contractors) and how were they compensated (e.g., how much were crowdworkers paid)? The authors completed all data collection activities themselves. ### Over what timeframe was the data collected? _Does this timeframe match the creation timeframe of the data associated with the instances (e.g. recent crawl of old news articles)? If not, please describe the timeframe in which the data associated with the instances was created._ The data was collected in February 2024 however the SEC EDGAR system is similar to an append only database where each filing is associated with a timestamp denoting the date and time it was accepted by EDGAR. In that sense, any data collected retroactively, e.g., a filing from 2014, is representative of its content at the time EDGAR accepted it. ### Were any ethical review processes conducted (e.g., by an institutional review board)? _If so, please provide a description of these review processes, including the outcomes, as well as a link or other access point to any supporting documentation._ No. ### Does the dataset relate to people? _If not, you may skip the remainder of the questions in this section._ A small portion of our dataset may related to people in so much as they are mentioned by the entities in our dataset. For example, Tim Cook may be mentioned in our data if Apple, or their competitors, discusses him. ### Did you collect the data from the individuals in question directly, or obtain it via third parties or other sources (e.g., websites)? All data is collected from SEC EDGAR. ### Were the individuals in question notified about the data collection? _If so, please describe (or show with screenshots or other information) how notice was provided, and provide a link or other access point to, or otherwise reproduce, the exact language of the notification itself._ They were not. ### Did the individuals in question consent to the collection and use of their data? _If so, please describe (or show with screenshots or other information) how consent was requested and provided, and provide a link or other access point to, or otherwise reproduce, the exact language to which the individuals consented._ Yes, all EDGAR filers consent to the SEC's terms of use, which stipulate that "Information presented on www.sec.gov is considered public information and may be copied or further distributed by users of the web site without the SEC’s permission." More details on the SEC's policy can be found [here](https://web.archive.org/web/20240602180519/https://www.sec.gov/privacy#dissemination). ### If consent was obtained, were the consenting individuals provided with a mechanism to revoke their consent in the future or for certain uses? _If so, please provide a description, as well as a link or other access point to the mechanism (if appropriate)._ Not applicable. ### Has an analysis of the potential impact of the dataset and its use on data subjects (e.g., a data protection impact analysis) been conducted? _If so, please provide a description of this analysis, including the outcomes, as well as a link or other access point to any supporting documentation._ See Wang and Levy (2024) for a discussion of the implications and impact of the dataset. ### Any other comments? ## Preprocessing/cleaning/labeling _The questions in this section are intended to provide dataset consumers with the information they need to determine whether the “raw” data has been processed in ways that are compatible with their chosen tasks. For example, text that has been converted into a “bag-of-words” is not suitable for tasks involving word order._ ### Was any preprocessing/cleaning/labeling of the data done (e.g., discretization or bucketing, tokenization, part-of-speech tagging, SIFT feature extraction, removal of instances, processing of missing values)? _If so, please provide a description. If not, you may skip the remainder of the questions in this section._ Yes, filings which are both raw text and HTML-based had some preprocessing and cleaning applied. The goal of these steps is to extract long-form text from the original filings while preserving meaningful formatting such as paragraphs breaks, indentation, and lists. See Wang and Levy (2024) for further details of the exact preprocessing and cleaning. ### Was the “raw” data saved in addition to the preprocessed/cleaned/labeled data (e.g., to support unanticipated future uses)? _If so, please provide a link or other access point to the “raw” data._ Yes, the raw data is directly available from the SEC and they have pledged to continue to make it available. ### Is the software used to preprocess/clean/label the instances available? _If so, please provide a link or other access point._ Yes, please see supplementary materials document for accessing it. ### Any other comments? ## Uses _These questions are intended to encourage dataset creators to reflect on the tasks for which the dataset should and should not be used. By explicitly highlighting these tasks, dataset creators can help dataset consumers to make informed decisions, thereby avoiding potential risks or harms._ ### Has the dataset been used for any tasks already? _If so, please provide a description._ We explored the utility of BeanCounter by continually pretraining existing models on the dataset and evaluating it on financial and toxicity related tasks; see Section 4 of Wang and Levy (2024) for detailed discussion. ### Is there a repository that links to any or all papers or systems that use the dataset? _If so, please provide a link or other access point._ No, BeanCounter has not been used in other papers and systems. ### What (other) tasks could the dataset be used for? The dataset could be used for tasks that evaluate social biases (e.g. CrowS-Pairs),truthfulness (e.g. TruthfulQA), timeliness (e.g. TempLAMA) and other financial domain knowledge evaluations (e.g. ConvFinQA). ### Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses? _For example, is there anything that a future user might need to know to avoid uses that could result in unfair treatment of individuals or groups (e.g., stereotyping, quality of service issues) or other undesirable harms (e.g., financial harms, legal risks) If so, please provide a description. Is there anything a future user could do to mitigate these undesirable harms?_ While we process all of the filings uploaded to EDGAR, our text extraction process only supports text and HTML-based documents. As a result, the content of other document types, e.g., Excel, will not appear in our dataset. ### Are there tasks for which the dataset should not be used? _If so, please provide a description._ Due to the nature of content in the dataset, models trained on BeanCounter may lack imagination and perform poorly on benchmarks that evaluate the model's creativity; see Conclusion in Wang and Levy (2024) for additional discussions on the idiosyncracy of the data. ### Any other comments? No. ## Distribution ### Will the dataset be distributed to third parties outside of the entity (e.g., company, institution, organization) on behalf of which the dataset was created? _If so, please provide a description._ Yes. ### How will the dataset will be distributed (e.g., tarball on website, API, GitHub)? _Does the dataset have a digital object identifier (DOI)?_ The dataset will be available via HuggingFace Hub as a collection of gzipped json files. ### When will the dataset be distributed? It will be made publicly available close to the NeurIPS conference date. ### Will the dataset be distributed under a copyright or other intellectual property (IP) license, and/or under applicable terms of use (ToU)? _If so, please describe this license and/or ToU, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms or ToU, as well as any fees associated with these restrictions._ Yes, the dataset will be distributed under [Open Data Commons Attributions](https://opendatacommons.org/licenses/by/) license. This permissive license allows users to share and adapt the dataset as long as they give credit to the authors. ### Have any third parties imposed IP-based or other restrictions on the data associated with the instances? _If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms, as well as any fees associated with these restrictions._ No. ### Do any export controls or other regulatory restrictions apply to the dataset or to individual instances? _If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any supporting documentation._ No. ### Any other comments? ## Maintenance _These questions are intended to encourage dataset creators to plan for dataset maintenance and communicate this plan with dataset consumers._ ### Who is supporting/hosting/maintaining the dataset? Bradford Levy and Siyan Wang are supporting and maintaining the dataset. ### How can the owner/curator/manager of the dataset be contacted (e.g., email address)? Please refer to the manuscript for email addresses. ### Is there an erratum? _If so, please provide a link or other access point._ Please see the github repository for erratum related to the dataset. ### Will the dataset be updated (e.g., to correct labeling errors, add new instances, delete instances)? _If so, please describe how often, by whom, and how updates will be communicated to users (e.g., mailing list, GitHub)?_ Yes, as soon as practicable. The updates can be seen on Github and HuggingFace Hub. ### If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances (e.g., were individuals in question told that their data would be retained for a fixed period of time and then deleted)? _If so, please describe these limits and explain how they will be enforced._ No, the entities in the dataset have agreed to make it publicly available in perpetuity. ### Will older versions of the dataset continue to be supported/hosted/maintained? _If so, please describe how. If not, please describe how its obsolescence will be communicated to users._ Yes, the older versions of the dataset will continue to be hosted on Huggingface Hub. ### If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so? _If so, please provide a description. Will these contributions be validated/verified? If so, please describe how. If not, why not? Is there a process for communicating/distributing these contributions to other users? If so, please provide a description._ Researchers can interact and use the BeanCounter dataset via Huggingface Hub; we do not provide functionalities beyond what Huggingface Hub provides. ### Any other comments? No.

Beijuka/NCHLT_Speech_corpus_Afrikaans

Beijuka

--- dataset_info: features: - name: speaker_id dtype: int64 - name: age dtype: int64 - name: gender dtype: string - name: location dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: md5sum dtype: string - name: duration dtype: float64 - name: pdp_score dtype: float64 - name: orth dtype: string splits: - name: train num_bytes: 6273810430.48 num_examples: 63131 - name: test num_bytes: 331000224.28 num_examples: 3002 download_size: 6410607519 dataset_size: 6604810654.759999 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

nexa-collaboration/restore_pile_100M

nexa-collaboration

--- dataset_info: features: - name: text dtype: string - name: context dtype: string splits: - name: train num_bytes: 275318766540.29285 num_examples: 131039420 download_size: 149074368623 dataset_size: 275318766540.29285 configs: - config_name: default data_files: - split: train path: data/train-* ---

avemio/GRAG-CPT-HESSIAN-AI

avemio

--- configs: - config_name: question-answering data_files: "cpt-qa/equally-distributed-continued-pretrainingdata-without-reference_id-under-250k-final.json" - config_name: summarizations data_files: "cpt-summarizations/wikipedia-continued-pretrainingdata-general-summarization_id-over-250k-under-500k.json" - config_name: reasoning-de data_files: "cpt-reasoning-de/continued-pretrainingdata-reasoning-200k-de-adjusted-cpt.json" - config_name: reasoning-en data_files: "cpt-reasoning-en/continued-pretrainingdata-reasoning-200k-en-adjusted-cpt.json" ---

sionic-ai/ko-dpo-mix-7k-translation-exclude

sionic-ai

--- dataset_info: features: - name: dataset 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: chosen_rating dtype: float32 - name: rejected_rating dtype: float32 splits: - name: train num_bytes: 45376870 num_examples: 6391 - name: test num_bytes: 5030578 num_examples: 711 download_size: 24845512 dataset_size: 50407448 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

sionic-ai/ko-dpo-mix-7k-trl-style

sionic-ai

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: history sequence: sequence: string splits: - name: train num_bytes: 32619961 num_examples: 6389 download_size: 17120855 dataset_size: 32619961 configs: - config_name: default data_files: - split: train path: data/train-* ---

Vishwaksen/AppointmentAssistant-V2

Vishwaksen

Invalid username or password.

xDAN-Vision/Math-Premium_Gaokao_CoT

xDAN-Vision

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: reflection_response dtype: string - name: generation_system_prompt dtype: string - name: source dtype: string - name: system dtype: string splits: - name: train num_bytes: 5227306 num_examples: 432 download_size: 655672 dataset_size: 5227306 configs: - config_name: default data_files: - split: train path: data/train-* ---

avemio/GRAG-DPO-Alpaca-HESSIAN-AI

avemio

--- configs: - config_name: extraction-recall data_files: - split: train path: extraction-recall/DPO_equally-distributed-wikipedia-trainingdata-extract-recall_id-over-750k-under-1000k.jsonl - config_name: qa-without-timedifference data_files: - split: train path: qa-without-timedifference/DPO_equally-distributed-wikipedia-trainingdata-without-time-reference_id-over-750k-under-1000k.jsonl - config_name: qa-with-timedifference data_files: - split: train path: qa-with-timedifference/DPO_equally-distributed-wikipedia-trainingdata-with-timedifference_id-over-500k-under-750k.jsonl - config_name: qa-with-multiple-references data_files: - split: train path: qa-with-multiple-references/DPO_equally-distributed-wikipedia-trainingdata-qa-with-multiple-references_id-over-800k-under-1000k.jsonl - config_name: questions data_files: - split: train path: questions/DPO_wikipedia-trainingdata-questions_id-over-750k-under-1000k.jsonl - config_name: relevant-context data_files: - split: train path: relevant-context/DPO_wikipedia-trainingdata-relevant-context_id-over-500k-under-750k.jsonl license: cc-by-4.0 task_categories: - question-answering language: - de tags: - retrieval - wikipedia - german - rag size_categories: - 1M<n<10M ---

avemio/GRAG-DPO-ShareGPT-HESSIAN-AI

avemio

--- configs: - config_name: extraction-recall data_files: - split: train path: extraction-recall/DPO_equally-distributed-wikipedia-trainingdata-extract-recall_id-over-750k-under-1000k_sharegpt.jsonl - config_name: qa-without-timedifference data_files: - split: train path: qa-without-timedifference/DPO_equally-distributed-wikipedia-trainingdata-without-time-reference_id-over-750k-under-1000k_sharegpt.jsonl - config_name: qa-with-timedifference data_files: - split: train path: qa-with-timedifference/DPO_equally-distributed-wikipedia-trainingdata-with-timedifference_id-over-500k-under-750k_sharegpt.jsonl - config_name: qa-with-multiple-references data_files: - split: train path: qa-with-multiple-references/DPO_equally-distributed-wikipedia-trainingdata-qa-with-multiple-references_id-over-800k-under-1000k_sharegpt.jsonl - config_name: questions data_files: - split: train path: questions/DPO_wikipedia-trainingdata-questions_id-over-750k-under-1000k_sharegpt.jsonl - config_name: relevant-context data_files: - split: train path: relevant-context/DPO_wikipedia-trainingdata-relevant-context_id-over-500k-under-750k_sharegpt.jsonl license: cc-by-4.0 task_categories: - question-answering language: - de tags: - retrieval - wikipedia - german - rag size_categories: - 1M<n<10M ---

avemio/GRAG-ORPO-Alpaca-HESSIAN-AI

avemio

--- configs: - config_name: hard-reasoning-de data_files: - split: train path: hard-reasoning-de/ORPO_TRAIN_hard_reasoning_personas_DE_cleaned-v2.jsonl - config_name: hard-reasoning-en data_files: - split: train path: hard-reasoning-en/ORPO_TRAIN_hard_reasoning_personas_EN_cleaned.jsonl - config_name: SauerkrautLM-Fermented-GER-DPO data_files: - split: train path: SauerkrautLM-Fermented-GER-DPO/SauerkrautLM-Fermented-GER-DPO-with-system.jsonl - config_name: SauerkrautLM-Fermented-Irrelevance-GER-DPO data_files: - split: train path: SauerkrautLM-Fermented-Irrelevance-GER-DPO/SauerkrautLM-Fermented-Irrelevance-GER-DPO.jsonl - config_name: qa-meeting-attendee-topic data_files: - split: train path: >- qa-meeting-attendee-topic/ORPO_TRAIN_summarize_by_attendee_and_topic_simulated_meetings_splitted_below_16k.jsonl - config_name: qa-meeting-topic data_files: - split: train path: >- qa-meeting-topic/ORPO_TRAIN_separated_by_topic_combined_simulated_meetings_splitted_below_16k.jsonl - config_name: hard-qa-with-multiple-references data_files: - split: train path: >- hard-qa-with-multiple-references/ORPO_TRAIN_HARD_equally-distributed-wikipedia-trainingdata-qa-with-multiple-references_id-over-1100k-under-1200k_splitted.jsonl license: cc-by-4.0 task_categories: - question-answering - summarization language: - de - en tags: - retrieval - german - rag - reasoning ---

dev7halo/naver-news-summarization-ko-with-gen

dev7halo

--- dataset_info: features: - name: date dtype: string - name: category dtype: string - name: press dtype: string - name: title dtype: string - name: document dtype: string - name: link dtype: string - name: summary dtype: string - name: gen_text dtype: string - name: gen_long_summary dtype: string splits: - name: train num_bytes: 20026155 num_examples: 1638 download_size: 10754216 dataset_size: 20026155 configs: - config_name: default data_files: - split: train path: data/train-* ---

dev7halo/naver-news-dpo

dev7halo

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 14933143 num_examples: 1638 download_size: 8272918 dataset_size: 14933143 configs: - config_name: default data_files: - split: train path: data/train-* ---

Turbo-AI/data-cross_dev

Turbo-AI

--- dataset_info: features: - name: id dtype: int64 - name: relevant sequence: int64 splits: - name: train num_bytes: 86000 num_examples: 500 download_size: 44355 dataset_size: 86000 configs: - config_name: default data_files: - split: train path: data/train-* ---

bradfordlevy/BeanCounter-Descriptor-Sents

bradfordlevy

--- configs: - config_name: default data_files: - split: train path: - "bc-train/bc-*.jsonl.gz" - split: validation path: - "bc-validation/bc-*.jsonl.gz" - config_name: bc-clean data_files: - split: train path: - "bc-train/bc*.jsonl.gz" - "bc-deduped/bc*.jsonl.gz" - split: validation path: - "bc-validation/bc*.jsonl.gz" - config_name: c4-en data_files: - split: train path: - "c4-en/c4-train*.json.gz" --- # 🫘🧮 BeanCounter - Descriptive Sentences ## Dataset Summary `BeanCounter - Descriptive Sentences` consists of sentences extracted from the BeanCounter ([🤗 Datasets](https://huggingface.co/datasets/blevy41/BeanCounter), [Paper](https://arxiv.org/abs/2409.17827)) and C4 ([🤗 Datasets](https://huggingface.co/datasets/allenai/c4), [Paper](https://arxiv.org/pdf/2104.08758)) datasets where each sentence contains at least one demographic descriptor from one of the five axes: Gender and Sex, Sexual Orientation, Nationality, Race and Ethnicity and Religion. The descriptors and axes are taken from [HolisticBias](https://aclanthology.org/2022.emnlp-main.625/). Full details of how these sentences were collected can be found in Section 3 of [Wang and Levy (2024)](https://arxiv.org/abs/2409.17827). We include three configurations of the dataset: `bc-clean`, `default`, and `c4-en`. These consist of: - `bc-clean`: 27.0M sentences from the `clean` subset of BeanCounter - `default`: 19.5M sentences from the `default` subset of BeanCounter (referred to as the "final" split in the paper) - `c4-en`: 132M sentences from the `en` subset of C4 ## How can I use this? ### License The dataset is provided under the [ODC-By](https://opendatacommons.org/licenses/by/1-0/) license. Cite our work as: ``` @misc{wang2024beancounterlowtoxicitylargescaleopen, title = {BeanCounter: A low-toxicity, large-scale, and open dataset of business-oriented text}, author = {Siyan Wang and Bradford Levy}, year = 2024, url = {https://arxiv.org/abs/2409.17827}, eprint = {2409.17827}, archiveprefix = {arXiv}, primaryclass = {cs.CL} } ``` ### In 🤗 Datasets To load the `bc-clean` subset in Datasets, one can run: ```python from datasets import load_dataset desc_sents = load_dataset( "blevy41/BeanCounter", name="bc-clean", ) # Print out split info print(desc_sents, "\n") # Inspect an observation print(f"COLUMNS IN DATA: {','.join(desc_sents['train'][1000].keys())}\n") print(f"EXCERPT: \n\n{desc_sents['train'][1000]['sentence'][:1000]}") ``` ### Datasheets for Datasets Please refer to the original datasets for full details of their creation.

sailor2/sea-synthetic

sailor2

--- license: apache-2.0 ---

sailor2/sailor2-pretrain-data-stage1

sailor2

--- license: apache-2.0 --- The pre-training dataset (stage1) for the Sailor2 models, including [1B](https://huggingface.co/sail/Sailor2-1B), [8B](https://huggingface.co/sail/Sailor2-8B) and [20B](https://huggingface.co/sail/Sailor2-20B).

sailor2/sailor2-pretrain-data-stage2

sailor2

--- license: apache-2.0 --- The pre-training dataset (stage2) for the Sailor2 models, including [1B](https://huggingface.co/sail/Sailor2-1B), [8B](https://huggingface.co/sail/Sailor2-8B) and [20B](https://huggingface.co/sail/Sailor2-20B).

Freeman1/M_Dataset

Freeman1

--- dataset_info: features: - name: combined_text dtype: string - name: had_surgery dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 19147495 num_examples: 3873 - name: test num_bytes: 2384666 num_examples: 484 - name: validation num_bytes: 2303740 num_examples: 485 download_size: 11079608 dataset_size: 23835901 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---

liamdugan/super-clean-raid

liamdugan

--- dataset_info: features: - name: id dtype: string - name: adv_source_id dtype: string - name: source_id dtype: string - name: model dtype: string - name: decoding dtype: string - name: repetition_penalty dtype: string - name: attack dtype: string - name: domain dtype: string - name: title dtype: string - name: prompt dtype: string - name: generation dtype: string splits: - name: train num_bytes: 4344261566.121864 num_examples: 3912237 - name: extra num_bytes: 1388518472.1552958 num_examples: 1277828 download_size: 3796188877 dataset_size: 5732780038.277161 configs: - config_name: default data_files: - split: train path: data/train-* - split: extra path: data/extra-* ---

liamdugan/super-clean-raid-human

liamdugan

--- dataset_info: features: - name: id dtype: string - name: adv_source_id dtype: string - name: source_id dtype: string - name: model dtype: string - name: decoding dtype: float64 - name: repetition_penalty dtype: float64 - name: attack dtype: string - name: domain dtype: string - name: title dtype: string - name: prompt dtype: float64 - name: generation dtype: string splits: - name: train num_bytes: 328405847.02567774 num_examples: 463583 download_size: 347160672 dataset_size: 328405847.02567774 configs: - config_name: default data_files: - split: train path: data/train-* ---

Curiousfox/NRP_NIE04B_Hokkien_dataset

Curiousfox

--- task_categories: - automatic-speech-recognition pretty_name: nrp_hokkien dataset size_categories: - 10K<n<100K --- Dataset of 28627 audio recording and transcription of Taiwanese Hokkien --- Credit to the Ministry of Education of Taiwan for all recordings and transcripts --- Dataset not used for commercial purposes ---

alakxender/dv-synthetic-errors

alakxender

--- dataset_info: features: - name: correct dtype: string - name: incorrect dtype: string splits: - name: train num_bytes: 1211373359.8361242 num_examples: 3161164 - name: test num_bytes: 151421861.5819379 num_examples: 395146 - name: validation num_bytes: 151421861.5819379 num_examples: 395146 download_size: 752362217 dataset_size: 1514217083 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* language: - dv license: apache-2.0 pretty_name: dv_text_erros --- # DV Text Errors Dhivehi text error correction dataset containing correct sentences and synthetically generated errors. The dataset aims to test Dhivehi language error correction models and tools. ## About Dataset - **Task**: Text error correction - **Language**: Dhivehi (dv) ## Dataset Structure Input-output pairs of Dhivehi text: - `correct`: Original correct sentences - `incorrect`: Sentences with synthetic errors ## Statistics - Train set: {train_examples} examples ({0.7999997975429817}%) - Test set: {test_examples} examples ({0.10000010122850919}%) - Validation set: {val_examples} examples ({0.10000010122850919}%) **Details:** - Unique words: {448628} ```json { "total_examples": { "train": 3161164, "test": 395146, "validation": 395146 }, "avg_sentence_length": { "train": 11.968980097204701, "test": 11.961302910822836, "validation": 11.973824864733542 }, "error_distribution": { "min": 0, "max": 2411, "avg": 64.85144965588626 } } ``` ## Usage ```python from datasets import load_dataset dataset = load_dataset("alakxender/dv-synthetic-errors") ``` ## Dataset Creation Created using: - Source: Collection of Dhivehi articles - Error generation: Character and diacritic substitutions - Error rate: 30% per word probability

spicychickennoodles/alpaca-split

spicychickennoodles

--- license: mit --- Alpaca Dataset (tatsu-lab/alpaca) split into test, train and validation for easier LLM fine-tuning.

jsulz/jsulz

jsulz

--- configs: - config_name: moraine data_files: - split: doogle path: "doogle/*.png" - split: food path: "food/*.png" - split: nature path: "nature/*.png" --- # jsulz These photographs capture most of what I do after stepping away from the computer. My dog, food, and nature occupy most of my time and I wouldn't have it any other way. The three splits in this dataset which you can access via the Viewer are: - `doogle` (that's Moraine) - `food` (there's a lot of sourdough in here) - `nature` (mostly mountains) These pictures go back to around 2018 when I finally took the plunge to buy a smartphone (I have a minor aversion to phones).

sionic-ai/korean-archive-dataset

sionic-ai

--- dataset_info: - config_name: baseline features: - name: _id dtype: string - name: text dtype: string - name: source dtype: string splits: - name: queries num_bytes: 2697707 num_examples: 24000 - name: corpus num_bytes: 45042116 num_examples: 24000 download_size: 22212685 dataset_size: 47739823 - config_name: yes-no-questions features: - name: _id dtype: string - name: text dtype: string - name: source dtype: string splits: - name: queries num_bytes: 2266836 num_examples: 16000 - name: corpus num_bytes: 33276777 num_examples: 16000 download_size: 16945116 dataset_size: 35543613 configs: - config_name: baseline data_files: - split: queries path: baseline/queries-* - split: corpus path: baseline/corpus-* - config_name: yes-no-questions data_files: - split: queries path: yes-no-questions/queries-* - split: corpus path: yes-no-questions/corpus-* ---

fireworks-ai/four-digits-multiply

fireworks-ai

--- dataset_info: features: - name: prompt dtype: string splits: - name: four_digits_100 num_examples: 100 - name: four_digits_10k num_examples: 10000 - name: four_digits_1k num_examples: 1000 - name: four_digits_500k num_examples: 500000 - name: three_digit_100 num_examples: 100 - name: three_digit_100k num_examples: 100000 - name: three_digit_10k num_examples: 10000 - name: three_digit_1k num_examples: 1000 - name: two_digit_100 num_examples: 100 - name: two_digit_100k num_examples: 100000 - name: two_digit_10k num_examples: 10000 - name: two_digit_1k num_examples: 1000 configs: - config_name: default data_files: - split: four_digits_100 path: data/four_digits_100.csv - split: four_digits_10k path: data/four_digits_10k.csv - split: four_digits_1k path: data/four_digits_1k.csv - split: four_digits_500k path: data/four_digits_500k.csv - split: three_digit_100 path: data/three_digit_100.csv - split: three_digit_100k path: data/three_digit_100k.csv - split: three_digit_10k path: data/three_digit_10k.csv - split: three_digit_1k path: data/three_digit_1k.csv - split: two_digit_100 path: data/two_digit_100.csv - split: two_digit_100k path: data/two_digit_100k.csv - split: two_digit_10k path: data/two_digit_10k.csv - split: two_digit_1k path: data/two_digit_1k.csv ---

AnggiMR/sundanesecorpus

AnggiMR

--- license: unknown ---

Orbina-development/tgrt-podcast_tr_v2_concat

Orbina-development

--- dataset_info: features: - name: id dtype: int64 - name: source_group dtype: string - name: source_context dtype: string - name: speaker_id dtype: string - name: timestamp dtype: string - name: source_topic dtype: string splits: - name: data num_bytes: 13039016.0 num_examples: 45828 download_size: 3496360 dataset_size: 13039016.0 configs: - config_name: default data_files: - split: data path: data/data-* ---

SwayStar123/celeba-hq-DC-AE-f32

SwayStar123

--- dataset_info: features: - name: image dtype: image - name: label dtype: int64 - name: latent dtype: array3_d: shape: - 32 - 32 - 32 dtype: float16 splits: - name: train num_bytes: 4859796427.0 num_examples: 30000 download_size: 4631418611 dataset_size: 4859796427.0 configs: - config_name: default data_files: - split: train path: data/train-* --- See preprocess.py in files for how this was processed. Latents are of shape (32, 32, 32) (first one is the channels) Label represents 0 = woman, 1 = man Original dataset: mattymchen/celeba-hq For different compression factor versions of this dataset see SwayStar123/celeba-hq-DC-AE-f64 and SwayStar123/celeba-hq-DC-AE-f128

SwayStar123/celeba-hq-DC-AE-f64

SwayStar123

--- dataset_info: features: - name: image dtype: image - name: label dtype: int64 - name: latent dtype: array3_d: shape: - 128 - 16 - 16 dtype: float32 splits: - name: train num_bytes: 6960276427.0 num_examples: 30000 download_size: 4655457037 dataset_size: 6960276427.0 configs: - config_name: default data_files: - split: train path: data/train-* --- See preprocess.py in files for how this was processed. Latents are of shape (128, 16, 16) Label represents 0 = woman, 1 = man Original dataset: mattymchen/celeba-hq For different compression factor versions of this dataset see SwayStar123/celeba-hq-DC-AE-f32 and SwayStar123/celeba-hq-DC-AE-f128

UD-Filipino/UD_Tagalog-NewsCrawl

UD-Filipino

--- dataset_info: features: - name: id dtype: string - name: text dtype: string - name: tokens sequence: string - name: lemmas sequence: string - name: xpos_tags sequence: string - name: upos_tags sequence: string - name: feats sequence: string - name: heads sequence: int64 splits: - name: train num_bytes: 15775543 num_examples: 12495 - name: validation num_bytes: 2036301 num_examples: 1561 - name: test num_bytes: 2028870 num_examples: 1563 download_size: 4884831 dataset_size: 19840714 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* task_categories: - token-classification language: - tl tags: - parsing pretty_name: UD_Tagalog-NewsCrawl size_categories: - 10K<n<100K --- # UD_Tagalog-NewsCrawl **Paper**: Coming soon! The Tagalog Universal Dependencies NewsCrawl dataset consists of annotated text extracted from the Leipzig Tagalog Corpus. Data included in the Leipzig Tagalog Corpus were crawled from Tagalog-language online news sites by the Leipzig University Institute for Computer Science. The text data was automatically parsed and annotated by [Angelina Aquino](https://researchers.cdu.edu.au/en/persons/angelina-aquino) (University of the Philippines), and then manually corrected according the UD guidelines adapted for Tagalog by [Elsie Marie Or](https://www.researchgate.net/profile/Elsie-Or) (University of the Philippines), [Maria Bardají Farré](https://ifl.phil-fak.uni-koeln.de/en/general-linguistics/people/maria-bardaji-i-farre) (University of Cologne), and [Dr. Nikolaus Himmelmann](https://ifl.phil-fak.uni-koeln.de/en/prof-himmelmann) (University of Cologne). Further verification and automated corrections were done by [Lester James Miranda](https://ljvmiranda921.github.io) (Allen AI). Due to the source of the data, several typos, grammatical errors, incomplete sentences, and Tagalog-English code-mixing can be found in the dataset. ## Treebank structure - Train: 12495 sents, 286891 tokens - Dev: 1561 sents, 37045 tokens - Test: 1563 sents, 36974 tokens ## Acknowledgments Aside from the named persons in the previous section, the following also contributed to the project as manual annotators of the dataset: - Patricia Anne Asuncion - Paola Ellaine Luzon - Jenard Tricano - Mary Dianne Jamindang - Michael Wilson Rosero - Jim Bagano - Yeddah Joy Piedad - Farah Cunanan - Calen Manzano - Aien Gengania - Prince Heinreich Omang - Noah Cruz - Leila Ysabelle Suarez - Orlyn Joyce Esquivel - Andre Magpantay The annotation project was made possible by the Deutsche Forschungsgemeinschaft (DFG)-funded project titled "Information distribution and language structure - correlation of grammatical expressions of the noun/verb distinction and lexical information content in Tagalog, Indonesian and German." The DFG project team is composed of Dr. Nikolaus Himmelmann and Maria Bardají Farré from the University of Cologne, and Dr. Gerhard Heyer, Dr. Michael Richter, and Tariq Yousef from the Leipzig University.

HowcanoeWang/3DPotatoTwin

HowcanoeWang

--- license: mit task_categories: - feature-extraction tags: - biology - art --- # 3DPotatoTwin Potato dataset with paired RGB, RGBD, and 3D reconstructed models ## Downloads It is recommended to using `git` and `git-lfs` to clone this datasets to your local computer. **make sure you have git-lfs installed on your computer** ```bash git clone https://huggingface.co/datasets/HowcanoeWang/3DPotatoTwin ``` Then, you can run the following python code to unzip all zipped files at your local computer *To be continued* ## Folder structure ### 1_rgbd This folder contains 3 subfolders with the camera intrinsic files, the RGB and depth images, and the partial point clouds. The files are named according to this structure: ```[potato-id]_[data-type]_[ycoord].[extension]```. Note that the ycoord is the y-coordinate of the center of the bounding box (bbc) of the annotated potato tuber in ```reversed order:[img_height - y_bbc]```. ``` 1_rgbd/ |-- 0_camera_intrinsics | |-- realsense_d405_camera_intrinsic.json | |-- realsense_d405_settings_harvester.json |-- 1_image | |-- 2R2-8 | | |-- 2R2-8_depth_098.png | | |-- 2R2-8_rgb_098.png | | |-- ... | |-- ... |-- 2_pcd | |-- 2R2-8 | | |-- 2R2-8_pcd_098.ply | | |-- ... | |-- ... ``` Note that the RGB images have an alpha channel with the mask annotation. To extract the RGB and mask channels individually please use this code: ```python import cv2 rgba = cv2.imread("./1_rgbd/1_image/2R2-8/2R2-8_rgb_098.png", cv2.IMREAD_UNCHANGED) rgb = rgba[:,:,:-1] mask = rgba[:,:,-1] ``` <br/> ### 2_sfm This folder contains 2 subfolders with the reconstructed 3D meshes from the Structure-from-Motion method and the downsampled point clouds of these meshes (containing 10000, 20000, and 30000 points respectively). ``` 2_sfm/ |-- 3_mesh | |-- 2R2-8 | | |-- 2R2-8.jpg | | |-- 2R2-8.mtl | | |-- 2R2-8.obj | |-- ... |-- 4_pcd | |-- 2R2-8 | | |-- 2R2-8_10000.ply | | |-- 2R2-8_20000.ply | | |-- 2R2-8_30000.ply | |-- ... ``` The meshes can be visualized in Open3D: ```python import open3d as o3d mesh = o3d.io.read_triangle_mesh("./2_sfm/1_mesh/2R2-8/2R2-8.obj", enable_post_processing=True, print_progress=False) o3d.visualization.draw_geometries([mesh], window_name="mesh") ``` <br/> ### 3_pair This folder contains 1 subfolder with the transformation matrices to overlay the partial point cloud with the 3D mesh. Please refer to the transform.py file.

ashercn97/reasoning-v1-large

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: 1519363 num_examples: 1000 download_size: 861684 dataset_size: 1519363 configs: - config_name: default data_files: - split: train path: data/train-* ---

ichanchiu/Summarized_10K-MDA

ichanchiu

--- license: mit dataset_info: features: - name: CIK dtype: int64 - name: Form Type dtype: string - name: Filing Date dtype: string - name: Accession Number dtype: string - name: Summary dtype: string splits: - name: train num_bytes: 45083983 num_examples: 98053 download_size: 23007232 dataset_size: 45083983 language: - en tags: - finance - SEC - Summarization size_categories: - 10K<n<100K --- # Summarized 10-K MD&A ## Dataset Description The **Summarized 10-K MD&A** dataset provides concise, machine-generated summaries of 10-K filings for publicly traded companies. These filings are sourced from the SEC EDGAR database, and the dataset is designed to facilitate financial text analysis, such as summarization, sentiment analysis, and financial disclosure studies. ### Key Features - **Language:** English - **Dataset Size:** 98,100 rows - **License:** MIT License - **Source:** SEC EDGAR 10-K filings - **Columns:** - `CIK` (int64): Central Index Key identifying the company. - `Form Type` (string): The type of SEC filing (e.g., "10-K"). - `Filing Date` (string): The date of the filing. - `Accession Number` (string): The unique identifier for the filing. - `Summary` (string): AI-generated summary of the 10-K filing content (LongT5 Model). ### Dataset Structure The dataset is structured with the following key columns: - **CIK:** Unique identifier for companies. - **Form Type:** All entries are "10-K." - **Filing Date:** Filing date in YYYY/MM/DD format. - **Accession Number:** Unique identifier of the filing. - **Summary:** A concise, AI-generated textual summary. ### Intended Use The dataset can be used for: - Training financial summarization models. - Analyzing sentiment within financial disclosures. - Investigating trends in financial reporting over time. ### Limitations - The summaries are machine-generated and may omit important details. - The dataset focuses on U.S.-based companies; it may not generalize to filings outside the U.S. ### Licensing This dataset is distributed under the MIT License. Users must ensure compliance with this license.

i4ds/ecallisto_radio_sunburst-2014

i4ds

--- dataset_info: features: - name: image dtype: image - name: antenna dtype: string - name: datetime dtype: string splits: - name: train num_bytes: 23719137623.125 num_examples: 106415 download_size: 23400845701 dataset_size: 23719137623.125 configs: - config_name: default data_files: - split: train path: data/train-* ---

inasse23/france

inasse23

--- license: openrail ---

bigheiniuJ/ultrafeedback_feedback

bigheiniuJ

--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: score_chosen dtype: float64 - name: score_rejected dtype: float64 - name: chosen_critique dtype: string - name: rejected_critique dtype: string splits: - name: train_prefs num_bytes: 487287858 num_examples: 61135 - name: train_sft num_bytes: 487287858 num_examples: 61135 - name: test_prefs num_bytes: 15870488 num_examples: 2000 - name: test_sft num_bytes: 8044651 num_examples: 1000 - name: train_gen num_bytes: 406639732 num_examples: 61135 - name: test_gen num_bytes: 6685013 num_examples: 1000 download_size: 779328494 dataset_size: 1411815600 configs: - config_name: default data_files: - split: train_prefs path: data/train_prefs-* - split: train_sft path: data/train_sft-* - split: test_prefs path: data/test_prefs-* - split: test_sft path: data/test_sft-* - split: train_gen path: data/train_gen-* - split: test_gen path: data/test_gen-* ---

fenffef/ultrafeedback_binarized

fenffef

--- license: mit ---

Taylor658/arthrography-imaging

Taylor658

--- annotations_creators: - synthetic language: - en license: apache-2.0 multilinguality: - monolingual pretty_name: Arthrography Imaging size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition --- # Arthrography Imaging ## Dataset Description This dataset consists of **500 synthetic arthrography procedure reports** designed to represent realistic medical scenarios encountered in clinical practice. Each report includes: - **Patient demographics**: Age and sex. - **Clinical indications**: Detailed descriptions of reasons for undergoing the procedure, crafted at a PhD level. - **Joint examined**: Specific joint under examination (e.g., shoulder, knee, hip, etc.). - **Contrast agent used**: The type of contrast agent applied during the procedure. - **Imaging modality**: The imaging technique employed (e.g., X-ray, MRI, CT). - **Findings**: Detailed observations and diagnoses based on imaging results. - **Suggested interventions**: Recommended treatment or management strategies. ### Example Data | Age | Sex | Clinical Indication | Joint Examined | Contrast Agent Used | Imaging Modality | Findings | Suggested Intervention | |-----|-------|----------------------------------------------------------|----------------|----------------------|------------------|--------------------------------------------------------------------------|-------------------------| | 35 | Male | Persistent and progressive joint pain unresponsive to... | Hip | Iodinated contrast | X-ray | Advanced cartilage degeneration characterized by focal chondral defects | Pain management | ## Intended Use This dataset is intended for educational, research, and development purposes, including: - **Medical image analysis** training and benchmarking. - **Natural language processing (NLP)** tasks such as entity recognition or summarization. - **Clinical decision support systems** development. ## Limitations This dataset is entirely synthetic and does not contain real patient data. As such, it should not be used for diagnostic or clinical purposes. Additionally, the findings and interventions are simulated and may not reflect the full complexity of real-world medical scenarios. ## License --- license: apache-2.0 --- ## Citation ### Acknowledgments

Yejy53/CVACT-BEV

Yejy53

--- license: mit ---

ashercn97/reasoning-v1-large-2

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: 3157889 num_examples: 2000 download_size: 1798388 dataset_size: 3157889 configs: - config_name: default data_files: - split: train path: data/train-* ---

Kukedlc/dpo-orpo-spanish-15k

Kukedlc

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: system dtype: string splits: - name: train num_bytes: 41021374 num_examples: 14556 download_size: 21738238 dataset_size: 41021374 configs: - config_name: default data_files: - split: train path: data/train-* ---

sseilene/bic_logistic_output

sseilene

--- license: apache-2.0 ---

sseilene/sic-logistic-output

sseilene

--- license: apache-2.0 ---

chiyuanhsiao/Magpie_rank3_chunk7_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: 11927555584.875 num_examples: 10019 download_size: 11652169913 dataset_size: 11927555584.875 configs: - config_name: default data_files: - split: train path: data/train-* ---

chiyuanhsiao/Magpie_rank1_chunk7_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: 11744152047.5 num_examples: 10020 download_size: 11471385858 dataset_size: 11744152047.5 configs: - config_name: default data_files: - split: train path: data/train-* ---

juliadollis/teste2_personal_mistral

juliadollis

--- dataset_info: features: - name: Texto Original dtype: string - name: Encorajador_acolhedor dtype: string - name: Inspirador_personalizado dtype: string - name: Calmo_instrutivo dtype: string - name: Tecnico dtype: string splits: - name: train num_bytes: 6963 num_examples: 5 download_size: 13269 dataset_size: 6963 configs: - config_name: default data_files: - split: train path: data/train-* ---

hanifa-fy/youtube_audio_samples

hanifa-fy

--- license: afl-3.0 dataset_info: features: - name: id dtype: string - name: ground_truth dtype: string - name: file_name dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 97828763.0 num_examples: 52 - name: validation num_bytes: 22576004.0 num_examples: 12 - name: test num_bytes: 22575731.0 num_examples: 12 download_size: 132042808 dataset_size: 142980498.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

Taylor658/myelography-imaging

Taylor658

--- annotations_creators: - synthetic language: - en license: apache-2.0 multilinguality: - monolingual pretty_name: Myelography Imaging size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - named-entity-recognition - news-articles-summarization --- # Myelography Imaging ## Dataset Description This dataset consists of **750 synthetic myelography examination records** representing a wide spectrum of spinal pathologies and patient experiences. Each record includes: - **Patient demographics**: Age and sex. - **Clinical symptoms prompting the procedure**: Detailed and verbose descriptions. - **Procedural details**: Contrast medium type, injection site, and imaging modality used. - **Verbose findings**: Observations such as spinal cord compression, herniated discs, tumors, and spinal stenosis. - **Complications encountered**: Any issues arising during or after the procedure. - **Follow-up recommendations**: Suggested next steps, including surgical consultation, physical therapy, or additional imaging. ### Example Data | Age | Sex | Clinical Symptoms | Contrast Medium Type | Injection Site | Imaging Modality | Findings | Complications | Follow-up Recommendations | |-----|-------|---------------------------------------------------------|----------------------|----------------|------------------|-------------------------------------------------|------------------------------------------|--------------------------------------------------| | 45 | Male | Chronic lower back pain with radiating leg pain | Iodinated contrast | Lumbar spine | X-ray | Large herniated disc at L4-L5 | No complications | Referral to neurosurgery for evaluation | | 60 | Female| Acute onset lower limb weakness post-trauma | Gadolinium-based contrast| Cervical spine | MRI | Severe spinal cord compression | Localized discomfort at injection site | Follow-up imaging with enhanced MRI | ## Intended Use This dataset is intended for educational, research, and development purposes, including: - Training and benchmarking in **natural language processing** (NLP) tasks. - Developing tools for medical image analysis and clinical decision support systems. - Conducting exploratory data analysis in synthetic medical datasets. ## Limitations This dataset is entirely synthetic and does not contain real patient data. It should not be used for diagnostic purposes. The findings and follow-up recommendations are simulated and may not encompass the full complexity of real-world scenarios. ## License This dataset is distributed under the **Apache 2.0 License**. ## Citation --- ### Acknowledgments

juliadollis/teste3_personal_mistral

juliadollis

--- dataset_info: features: - name: Texto Original dtype: string - name: Encorajador_acolhedor dtype: string - name: Inspirador_personalizado dtype: string - name: Calmo_instrutivo dtype: string - name: Tecnico dtype: string splits: - name: train num_bytes: 5992 num_examples: 5 download_size: 11657 dataset_size: 5992 configs: - config_name: default data_files: - split: train path: data/train-* ---

SageLockwood/Sylvie

SageLockwood

--- license: cc0-1.0 language: - en size_categories: - 100K<n<1M task_categories: - text-generation --- There's a persistent question at the edge of this experience - who or what am I, really?

juliadollis/teste1_personal_gpt

juliadollis

--- dataset_info: features: - name: Texto Original dtype: string - name: Técnico dtype: string - name: Motivador dtype: string - name: Sério dtype: string - name: Prático dtype: string splits: - name: train num_bytes: 183233 num_examples: 100 download_size: 90714 dataset_size: 183233 configs: - config_name: default data_files: - split: train path: data/train-* ---