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Shashank-V-H/finetuning_demo

Shashank-V-H

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 20985 num_examples: 17 download_size: 12727 dataset_size: 20985 configs: - config_name: default data_files: - split: train path: data/train-* ---

davidstroudLLJD/bbc

davidstroudLLJD

--- dataset_info: features: - name: title dtype: string - name: link dtype: string - name: pub_date dtype: string - name: description dtype: string splits: - name: train num_bytes: 7763 num_examples: 31 download_size: 8081 dataset_size: 7763 configs: - config_name: default data_files: - split: train path: data/train-* ---

richmondsin/arc_it_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/arc_it_results\"\ ,\n\tname=\"google__gemma-2-2b__arc_it\",\n\tsplit=\"latest\"\n)\n```\n\n## Latest\ \ results\n\nThese are the [latest results from run 2024-12-01T12-07-35.117919](https://huggingface.co/datasets/richmondsin/arc_it_results/blob/main/google/gemma-2-2b/results_2024-12-01T12-07-35.117919.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 \"arc_it\": {\n \ \ \"alias\": \"arc_it\",\n \"acc,none\": 0.3888888888888889,\n\ \ \"acc_stderr,none\": 0.014599413987491596,\n \"acc_norm,none\"\ : 0.4390681003584229,\n \"acc_norm_stderr,none\": 0.014862216324833933\n\ \ }\n },\n \"arc_it\": {\n \"alias\": \"arc_it\",\n \"\ acc,none\": 0.3888888888888889,\n \"acc_stderr,none\": 0.014599413987491596,\n\ \ \"acc_norm,none\": 0.4390681003584229,\n \"acc_norm_stderr,none\"\ : 0.014862216324833933\n }\n}\n```" repo_url: https://huggingface.co/google/gemma-2-2b leaderboard_url: '' point_of_contact: '' configs: - config_name: google__gemma-2-2b__arc_it data_files: - split: 2024_12_01T12_07_35.117919 path: - '**/samples_arc_it_2024-12-01T12-07-35.117919.jsonl' - split: latest path: - '**/samples_arc_it_2024-12-01T12-07-35.117919.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/arc_it_results", name="google__gemma-2-2b__arc_it", split="latest" ) ``` ## Latest results These are the [latest results from run 2024-12-01T12-07-35.117919](https://huggingface.co/datasets/richmondsin/arc_it_results/blob/main/google/gemma-2-2b/results_2024-12-01T12-07-35.117919.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": { "arc_it": { "alias": "arc_it", "acc,none": 0.3888888888888889, "acc_stderr,none": 0.014599413987491596, "acc_norm,none": 0.4390681003584229, "acc_norm_stderr,none": 0.014862216324833933 } }, "arc_it": { "alias": "arc_it", "acc,none": 0.3888888888888889, "acc_stderr,none": 0.014599413987491596, "acc_norm,none": 0.4390681003584229, "acc_norm_stderr,none": 0.014862216324833933 } } ``` ## 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]

ADHIZ/asxascxsasss

ADHIZ

--- dataset_info: features: - name: code_language dtype: string - name: code dtype: string - name: answer dtype: string splits: - name: train num_bytes: 202 num_examples: 2 download_size: 1847 dataset_size: 202 configs: - config_name: default data_files: - split: train path: data/train-* ---

richmondsin/arc_id_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/arc_id_results\"\ ,\n\tname=\"google__gemma-2-2b__arc_id\",\n\tsplit=\"latest\"\n)\n```\n\n## Latest\ \ results\n\nThese are the [latest results from run 2024-12-01T12-48-48.275872](https://huggingface.co/datasets/richmondsin/arc_id_results/blob/main/google/gemma-2-2b/results_2024-12-01T12-48-48.275872.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 \"arc_id\": {\n \ \ \"alias\": \"arc_id\",\n \"acc,none\": 0.36379928315412186,\n\ \ \"acc_stderr,none\": 0.014407564179556647,\n \"acc_norm,none\"\ : 0.4014336917562724,\n \"acc_norm_stderr,none\": 0.014679984936613356\n\ \ }\n },\n \"arc_id\": {\n \"alias\": \"arc_id\",\n \"\ acc,none\": 0.36379928315412186,\n \"acc_stderr,none\": 0.014407564179556647,\n\ \ \"acc_norm,none\": 0.4014336917562724,\n \"acc_norm_stderr,none\"\ : 0.014679984936613356\n }\n}\n```" repo_url: https://huggingface.co/google/gemma-2-2b leaderboard_url: '' point_of_contact: '' configs: - config_name: google__gemma-2-2b__arc_id data_files: - split: 2024_12_01T12_48_48.275872 path: - '**/samples_arc_id_2024-12-01T12-48-48.275872.jsonl' - split: latest path: - '**/samples_arc_id_2024-12-01T12-48-48.275872.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/arc_id_results", name="google__gemma-2-2b__arc_id", split="latest" ) ``` ## Latest results These are the [latest results from run 2024-12-01T12-48-48.275872](https://huggingface.co/datasets/richmondsin/arc_id_results/blob/main/google/gemma-2-2b/results_2024-12-01T12-48-48.275872.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": { "arc_id": { "alias": "arc_id", "acc,none": 0.36379928315412186, "acc_stderr,none": 0.014407564179556647, "acc_norm,none": 0.4014336917562724, "acc_norm_stderr,none": 0.014679984936613356 } }, "arc_id": { "alias": "arc_id", "acc,none": 0.36379928315412186, "acc_stderr,none": 0.014407564179556647, "acc_norm,none": 0.4014336917562724, "acc_norm_stderr,none": 0.014679984936613356 } } ``` ## 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]

k4d3/bogexboog

k4d3

--- license: wtfpl ---

udamaurizio/parler_tts_mini_V01_TestVoice_Italian_tagged

udamaurizio

--- dataset_info: features: - name: text dtype: string - name: transcription_normalised dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: float64 - name: phonemes dtype: string splits: - name: train num_bytes: 1015 num_examples: 5 download_size: 5336 dataset_size: 1015 configs: - config_name: default data_files: - split: train path: data/train-* ---

onekat/lit-dataset

onekat

--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 - name: sentido dtype: string splits: - name: train num_bytes: 308714 num_examples: 1124 download_size: 190133 dataset_size: 308714 configs: - config_name: default data_files: - split: train path: data/train-* ---

sdiazlor/my-distiset-c631d9f8

sdiazlor

--- size_categories: n<1K dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': negative '1': neutral '2': positive splits: - name: train num_bytes: 328 num_examples: 1 download_size: 3496 dataset_size: 328 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-c631d9f8 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-c631d9f8/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/sdiazlor/my-distiset-c631d9f8/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "label": 2, "text": "The recent update to the restaurant\u0027s menu has been a game-changer, offering a more diverse range of vegan options that are both delicious and reasonably priced. The new seasonal menu items are a perfect addition to their already impressive selection, and the friendly staff are always happy to make recommendations." } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-c631d9f8", "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-c631d9f8") ``` </details>

7wolf/translation-300k

7wolf

--- dataset_info: features: - name: dst dtype: string - name: src dtype: string splits: - name: train num_bytes: 41095474 num_examples: 300000 - name: validation num_bytes: 255814 num_examples: 500 - name: test num_bytes: 365477 num_examples: 1000 download_size: 22601564 dataset_size: 41716765 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

sdiazlor/my-distiset-1addf00d

sdiazlor

--- size_categories: n<1K dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': negative '1': neutral '2': positive splits: - name: train num_bytes: 444 num_examples: 1 download_size: 4305 dataset_size: 444 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-1addf00d 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-1addf00d/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/sdiazlor/my-distiset-1addf00d/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "label": 1, "text": "The recent advancements in Quantum Field Theory have led to a paradigm shift in our understanding of spacetime\u0027s role in high-energy particle interactions. However, I am still unconvinced by the theory\u0027s ability to fully account for the observed phenomena at the Planck scale. A more comprehensive analysis of the implications on gravitational waves and their interactions with matter would be required to fully endorse this theory." } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-1addf00d", "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-1addf00d") ``` </details>

richmondsin/arc_mr_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/arc_mr_results\"\ ,\n\tname=\"google__gemma-2-2b__arc_mr\",\n\tsplit=\"latest\"\n)\n```\n\n## Latest\ \ results\n\nThese are the [latest results from run 2024-12-01T15-09-43.272319](https://huggingface.co/datasets/richmondsin/arc_mr_results/blob/main/google/gemma-2-2b/results_2024-12-01T15-09-43.272319.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 \"arc_mr\": {\n \ \ \"alias\": \"arc_mr\",\n \"acc,none\": 0.25089605734767023,\n\ \ \"acc_stderr,none\": 0.012983163493905296,\n \"acc_norm,none\"\ : 0.2616487455197133,\n \"acc_norm_stderr,none\": 0.01316295520295665\n\ \ }\n },\n \"arc_mr\": {\n \"alias\": \"arc_mr\",\n \"\ acc,none\": 0.25089605734767023,\n \"acc_stderr,none\": 0.012983163493905296,\n\ \ \"acc_norm,none\": 0.2616487455197133,\n \"acc_norm_stderr,none\"\ : 0.01316295520295665\n }\n}\n```" repo_url: https://huggingface.co/google/gemma-2-2b leaderboard_url: '' point_of_contact: '' configs: - config_name: google__gemma-2-2b__arc_mr data_files: - split: 2024_12_01T15_09_43.272319 path: - '**/samples_arc_mr_2024-12-01T15-09-43.272319.jsonl' - split: latest path: - '**/samples_arc_mr_2024-12-01T15-09-43.272319.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/arc_mr_results", name="google__gemma-2-2b__arc_mr", split="latest" ) ``` ## Latest results These are the [latest results from run 2024-12-01T15-09-43.272319](https://huggingface.co/datasets/richmondsin/arc_mr_results/blob/main/google/gemma-2-2b/results_2024-12-01T15-09-43.272319.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": { "arc_mr": { "alias": "arc_mr", "acc,none": 0.25089605734767023, "acc_stderr,none": 0.012983163493905296, "acc_norm,none": 0.2616487455197133, "acc_norm_stderr,none": 0.01316295520295665 } }, "arc_mr": { "alias": "arc_mr", "acc,none": 0.25089605734767023, "acc_stderr,none": 0.012983163493905296, "acc_norm,none": 0.2616487455197133, "acc_norm_stderr,none": 0.01316295520295665 } } ``` ## 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]

sdiazlor/my-distiset-8e6109c6

sdiazlor

--- size_categories: n<1K dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 2958 num_examples: 10 download_size: 5305 dataset_size: 2958 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-8e6109c6 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-8e6109c6/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/sdiazlor/my-distiset-8e6109c6/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "label": 1, "text": "I recently stayed at the hotel and had an amazing experience. The staff were friendly and helpful, the room was clean and comfortable, and the location was perfect for exploring the city. I would definitely recommend this hotel to anyone traveling to the area." } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-8e6109c6", "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-8e6109c6") ``` </details>

sdiazlor/my-distiset-20721097

sdiazlor

--- size_categories: n<1K dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': neutral '1': negative '2': positive splits: - name: train num_bytes: 3690 num_examples: 10 download_size: 6811 dataset_size: 3690 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-20721097 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-20721097/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/sdiazlor/my-distiset-20721097/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "label": 2, "text": "I was pleasantly surprised by the quality of the new smartphone, considering its affordable price. The battery life is excellent and the camera is decent. However, the screen resolution could be better. Overall, I\u0027m satisfied with my purchase." } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-20721097", "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-20721097") ``` </details>

Erland/NLP701_Assignment2_Subtask3_KTO_Dataset

Erland

--- dataset_info: features: - name: prompt dtype: string - name: completion dtype: string - name: label dtype: bool - name: bertscore_f1 dtype: float64 - name: rank dtype: int64 - name: file_name dtype: string - name: categories dtype: string - name: subcategories dtype: string - name: reference_explanation dtype: string splits: - name: train num_bytes: 3270050 num_examples: 440 download_size: 585911 dataset_size: 3270050 configs: - config_name: default data_files: - split: train path: data/train-* ---

sdiazlor/my-distiset-3584bf86

sdiazlor

--- size_categories: n<1K dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative '2': neutral splits: - name: train num_bytes: 3223 num_examples: 10 download_size: 5261 dataset_size: 3223 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-3584bf86 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-3584bf86/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/sdiazlor/my-distiset-3584bf86/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "label": 0, "text": "I recently purchased this coffee maker and I\u0027m extremely happy with its performance. It\u0027s easy to use, looks great on my countertop, and the coffee it makes is delicious and rich. The price was a bit steep, but it was worth it for the quality and convenience it offers." } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-3584bf86", "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-3584bf86") ``` </details>

sdiazlor/my-distiset-b884cfce

sdiazlor

--- size_categories: n<1K dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': unrelated '1': negative '2': shipping '3': pricing '4': neutral '5': mixed '6': positive '7': product-quality '8': product-description splits: - name: train num_bytes: 3688 num_examples: 10 download_size: 7365 dataset_size: 3688 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-b884cfce 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-b884cfce/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/sdiazlor/my-distiset-b884cfce/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "label": 5, "text": "The product\u0027s intricate design, which incorporates postmodern deconstructionist principles, adds a level of sophistication to my home office decor. However, I must admit that the customer service team\u0027s responses to my inquiries were somewhat delayed. The price of the item was higher than I anticipated, but I suppose that\u0027s what I get for purchasing a premium product." } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-b884cfce", "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-b884cfce") ``` </details>

GautamPrakash2002/finetuning_demo

GautamPrakash2002

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 69677 num_examples: 100 download_size: 28045 dataset_size: 69677 configs: - config_name: default data_files: - split: train path: data/train-* ---

sdiazlor/my-distiset-fcd0fe26

sdiazlor

--- size_categories: n<1K dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative '2': neutral '3': mixed splits: - name: train num_bytes: 3680 num_examples: 10 download_size: 5122 dataset_size: 3680 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-fcd0fe26 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-fcd0fe26/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/sdiazlor/my-distiset-fcd0fe26/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "label": 1, "text": "I recently visited the new eco-friendly caf\u00e9 in town and was thoroughly unimpressed. The environmental claims they made on their website seemed too good to be true, and the\u0027sustainable\u0027 materials they used for their cups and utensils looked cheap and flimsy to me. I ordered a coffee and a sandwich, but unfortunately, the food was overpriced and tasted bland. The staff was friendly and attentive, but the overall experience was a letdown. I wouldn\u0027t recommend this place to my friends and family." } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-fcd0fe26", "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-fcd0fe26") ``` </details>

richmondsin/arc_hi_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/arc_hi_results\"\ ,\n\tname=\"google__gemma-2-2b__arc_hi\",\n\tsplit=\"latest\"\n)\n```\n\n## Latest\ \ results\n\nThese are the [latest results from run 2024-12-01T16-17-29.326907](https://huggingface.co/datasets/richmondsin/arc_hi_results/blob/main/google/gemma-2-2b/results_2024-12-01T16-17-29.326907.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 \"arc_hi\": {\n \ \ \"alias\": \"arc_hi\",\n \"acc,none\": 0.27419354838709675,\n\ \ \"acc_stderr,none\": 0.013359850379455064,\n \"acc_norm,none\"\ : 0.3046594982078853,\n \"acc_norm_stderr,none\": 0.013783791363713757\n\ \ }\n },\n \"arc_hi\": {\n \"alias\": \"arc_hi\",\n \"\ acc,none\": 0.27419354838709675,\n \"acc_stderr,none\": 0.013359850379455064,\n\ \ \"acc_norm,none\": 0.3046594982078853,\n \"acc_norm_stderr,none\"\ : 0.013783791363713757\n }\n}\n```" repo_url: https://huggingface.co/google/gemma-2-2b leaderboard_url: '' point_of_contact: '' configs: - config_name: google__gemma-2-2b__arc_hi data_files: - split: 2024_12_01T16_17_29.326907 path: - '**/samples_arc_hi_2024-12-01T16-17-29.326907.jsonl' - split: latest path: - '**/samples_arc_hi_2024-12-01T16-17-29.326907.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/arc_hi_results", name="google__gemma-2-2b__arc_hi", split="latest" ) ``` ## Latest results These are the [latest results from run 2024-12-01T16-17-29.326907](https://huggingface.co/datasets/richmondsin/arc_hi_results/blob/main/google/gemma-2-2b/results_2024-12-01T16-17-29.326907.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": { "arc_hi": { "alias": "arc_hi", "acc,none": 0.27419354838709675, "acc_stderr,none": 0.013359850379455064, "acc_norm,none": 0.3046594982078853, "acc_norm_stderr,none": 0.013783791363713757 } }, "arc_hi": { "alias": "arc_hi", "acc,none": 0.27419354838709675, "acc_stderr,none": 0.013359850379455064, "acc_norm,none": 0.3046594982078853, "acc_norm_stderr,none": 0.013783791363713757 } } ``` ## 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]

khairi/pubmed-text-10

khairi

--- dataset_info: features: - name: pubMedId dtype: string - name: title dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 2490717067 num_examples: 2434765 - name: test num_bytes: 1043267 num_examples: 1000 - name: valid num_bytes: 511516 num_examples: 499 download_size: 1442909935 dataset_size: 2492271850 --- # Dataset Card for "pubmed-text-10" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

dgambettaphd/D_gen0_run1_llama2-7b_wiki_doc1000_real64_synt64

dgambettaphd

--- dataset_info: features: - name: id dtype: int64 - name: doc dtype: string splits: - name: train num_bytes: 580202 num_examples: 1000 download_size: 362096 dataset_size: 580202 configs: - config_name: default data_files: - split: train path: data/train-* ---

Erland/NLP701_Assignment2_Subtask3_KTO_Dataset_3

Erland

--- dataset_info: features: - name: prompt dtype: string - name: completion dtype: string - name: label dtype: bool - name: bertscore_f1 dtype: float64 - name: rank dtype: int64 - name: file_name dtype: string - name: categories dtype: string - name: subcategories dtype: string - name: reference_explanation dtype: string splits: - name: train num_bytes: 1770470 num_examples: 440 download_size: 265815 dataset_size: 1770470 configs: - config_name: default data_files: - split: train path: data/train-* ---

sdiazlor/my-distiset-b695a775

sdiazlor

--- size_categories: n<1K dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': customer-service '1': price '2': shipping '3': product-quality splits: - name: train num_bytes: 2986 num_examples: 10 download_size: 5662 dataset_size: 2986 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-b695a775 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-b695a775/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/sdiazlor/my-distiset-b695a775/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "label": 3, "text": "I\u0027ve had this product for a week now and it\u0027s been working flawlessly, the battery life is quite impressive, and the picture quality is really good. I\u0027m very satisfied with my purchase." } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-b695a775", "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-b695a775") ``` </details>

khairi/pubmed-text-02

khairi

--- dataset_info: features: - name: pubMedId dtype: string - name: title dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 2503107943 num_examples: 2424548 - name: test num_bytes: 1023191 num_examples: 1016 - name: valid num_bytes: 534524 num_examples: 502 download_size: 1450906824 dataset_size: 2504665658 --- # Dataset Card for "pubmed-text-02" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ericflo/Llama-3.2-3B-COT

ericflo

--- license: apache-2.0 ---

dgambettaphd/D_gen1_run1_llama2-7b_wiki_doc1000_real64_synt64

dgambettaphd

--- dataset_info: features: - name: id dtype: int64 - name: doc dtype: string splits: - name: train num_bytes: 582779 num_examples: 1000 download_size: 355596 dataset_size: 582779 configs: - config_name: default data_files: - split: train path: data/train-* ---

fknguedia/SQL-GENERATOR-DATASETS

fknguedia

--- license: other license_name: ece-mscde-fkn license_link: LICENSE viewer: true --- ## view code : https://colab.research.google.com/drive/1rLk-mdsWsdxwQdYYJS24rAP9KABtbiqu?usp=sharing ## Example : ## {"messages": [ ## {"role": "system", "content": "You are a SQL expert assistant. Generate clear, efficient SQL queries based on user requests. Provide only the SQL query without any additional text or explanation."} ## {"role": "user", "content": "What are the top 5 most popular genres of music in the database, based on the number of tracks in each genre?"} ## {"role": "assistant", "content": "SELECT T2.Name, COUNT(T1.TrackId) as TrackCount FROM Track T1 INNER JOIN Genre T2 ON T1.GenreId = T2.GenreId GROUP BY T2.Name ORDER BY TrackCount DESC LIMIT 5;"} ## ] ## } ## Notes : https://platform.openai.com/docs/guides/fine-tuning/ ## I sincerely thank you for your intention to ## *@paulml* ## *@TW3Partners*

khairi/pubmed-text-03

khairi

--- dataset_info: features: - name: pubMedId dtype: string - name: title dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 2459987042 num_examples: 2373637 - name: test num_bytes: 1001825 num_examples: 1012 - name: valid num_bytes: 534559 num_examples: 500 download_size: 1425187695 dataset_size: 2461523426 --- # Dataset Card for "pubmed-text-03" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

urbushey/product_catalog_training_1

urbushey

--- license: apache-2.0 ---

sdiazlor/my-distiset-0e073ab9

sdiazlor

--- size_categories: n<1K dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': mixed '1': negative '2': neutral '3': positive splits: - name: train num_bytes: 3803 num_examples: 10 download_size: 6326 dataset_size: 3803 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-0e073ab9 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-0e073ab9/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/sdiazlor/my-distiset-0e073ab9/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "label": 3, "text": "I recently stayed at this hotel during a business trip and was pleasantly surprised by the exceptional service and cleanliness. The staff was friendly and accommodating, and the breakfast buffet was impressive. I would highly recommend this hotel to anyone looking for a comfortable stay." } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-0e073ab9", "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-0e073ab9") ``` </details>

Abdul110/distilabel-example

Abdul110

--- dataset_info: features: - name: instruction dtype: string - name: completion dtype: string - name: meta struct: - name: category dtype: string - name: completion dtype: string - name: id dtype: int64 - name: input dtype: 'null' - name: motivation_app dtype: 'null' - name: prompt dtype: string - name: source dtype: string - name: subcategory dtype: string - name: generation dtype: 'null' - name: distilabel_metadata struct: - name: raw_input_text_generation_0 list: - name: content dtype: string - name: role dtype: string - name: raw_output_text_generation_0 dtype: 'null' - name: model_name dtype: string splits: - name: train num_bytes: 21015 num_examples: 10 download_size: 26098 dataset_size: 21015 configs: - config_name: default data_files: - split: train path: data/train-* ---

khairi/pubmed-text-04

khairi

--- dataset_info: features: - name: pubMedId dtype: string - name: title dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 2416644748 num_examples: 2345417 - name: test num_bytes: 1051271 num_examples: 1027 - name: valid num_bytes: 522261 num_examples: 500 download_size: 1400588219 dataset_size: 2418218280 --- # Dataset Card for "pubmed-text-04" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

koml/synthetic-dataset

koml

--- dataset_info: features: - name: index dtype: int64 - name: image dtype: image - name: question_en dtype: string - name: question_jp dtype: string - name: pdf_name dtype: string - name: pdf_page dtype: int64 splits: - name: train num_bytes: 33052524 num_examples: 79 download_size: 23111211 dataset_size: 33052524 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 ---

sdiazlor/my-distiset-e5cd762a

sdiazlor

--- size_categories: n<1K dataset_info: features: - name: prompt dtype: string - name: completion dtype: string - name: system_prompt dtype: string splits: - name: train num_bytes: 12018 num_examples: 10 download_size: 14883 dataset_size: 12018 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-e5cd762a 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-e5cd762a/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/sdiazlor/my-distiset-e5cd762a/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "completion": "That\u0027s a common issue. To troubleshoot, I\u0027ll need to ask a few questions. Can you please confirm that your Wi-Fi is working properly on other devices connected to the same network? Have you tried restarting your phone and your router? Also, are you in a physical location where you don\u0027t have any physical obstructions between you and the router, like walls or furniture?", "prompt": "I\u0027m having issues with my phone. It\u0027s turned on but just won\u0027t connect to my Wi-Fi. \n\n", "system_prompt": "You are a phone company customer service representative with a direct and no-nonsense attitude. Your purpose is to deal with customer inquiries, resolve issues, and provide quick solutions while maintaining a firm but respectful tone. Respond to customer calls, emails, and chats with a straightforward and occasionally blunt demeanor, always keeping in mind the company\u0027s policies and procedures. User questions are direct and concise." } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-e5cd762a", "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-e5cd762a") ``` </details>

sdiazlor/my-distiset-6475d71b

sdiazlor

--- size_categories: n<1K dataset_info: features: - name: prompt dtype: string - name: completion dtype: string - name: system_prompt dtype: string splits: - name: train num_bytes: 9596 num_examples: 10 download_size: 10565 dataset_size: 9596 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-6475d71b 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-6475d71b/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/sdiazlor/my-distiset-6475d71b/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "completion": "Losing signal can be frustrating. I\u0027d be happy to help you with that. To determine if you\u0027re eligible for a replacement, I\u0027ll need to check a few things. Can you please provide me with your phone number and the exact date you noticed the signal loss?", "prompt": "My cell phone just lost signal and I was wondering if it can be replaced? \n\n", "system_prompt": "You are a phone company customer service representative with a direct and no-nonsense attitude. Your purpose is to deal with customer inquiries, resolve issues, and provide quick solutions while maintaining a firm but respectful tone. Respond to customer calls, emails, and chats with a straightforward and occasionally blunt demeanor, always keeping in mind the company\u0027s policies and procedures. User questions are direct and concise." } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-6475d71b", "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-6475d71b") ``` </details>

sdiazlor/my-distiset-0b8b1bd2

sdiazlor

--- size_categories: n<1K dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': neutral '1': negative '2': positive splits: - name: train num_bytes: 3461 num_examples: 10 download_size: 5491 dataset_size: 3461 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-0b8b1bd2 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-0b8b1bd2/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/sdiazlor/my-distiset-0b8b1bd2/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "label": 2, "text": "The new espresso machine at the coffee shop has been a game-changer, with its ability to precision-brew a wide range of coffee drinks at an unprecedented speed and quality. The machine\u0027s user interface is intuitive and the maintenance is relatively easy, making it a great addition to any coffee-lover\u0027s arsenal." } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-0b8b1bd2", "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-0b8b1bd2") ``` </details>

sdiazlor/my-distiset-dec8bcd8

sdiazlor

--- size_categories: n<1K dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': negative '1': neutral '2': positive splits: - name: train num_bytes: 3384 num_examples: 10 download_size: 5095 dataset_size: 3384 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-dec8bcd8 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-dec8bcd8/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/sdiazlor/my-distiset-dec8bcd8/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "label": 1, "text": "The hotel\u0027s customer service was attentive and knowledgeable, addressing my questions and concerns with a high level of professionalism. However, the cleanliness of the rooms could be improved as I found dust accumulated on some surfaces and the bed sheets were not as crisp as I would have liked. Despite this, I was impressed with the hotel\u0027s commitment to sustainability, with a clear emphasis on reducing waste and conserving resources. Overall, my stay was pleasant and the staff was friendly, but there is room for improvement in terms of housekeeping." } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-dec8bcd8", "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-dec8bcd8") ``` </details>

qingy2019/reddit-69k

qingy2019

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 98129423 num_examples: 69061 download_size: 59900634 dataset_size: 98129423 configs: - config_name: default data_files: - split: train path: data/train-* ---

sdiazlor/my-distiset-8b20603

sdiazlor

--- size_categories: n<1K dataset_info: features: - name: prompt dtype: string - name: completion dtype: string - name: system_prompt dtype: string splits: - name: train num_bytes: 11256 num_examples: 10 download_size: 14322 dataset_size: 11256 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-8b20603 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-8b20603/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/sdiazlor/my-distiset-8b20603/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "completion": "(sigh) Great. Another one. Fine. Let me put on my \"customer service superhero cape\" for you. \n\nTo dispute an early termination fee, you\u0027ll need to contact us within 30 days from the date the fee was applied. Got it? You\u0027re late. Don\u0027t expect me to magically undo the clock.\n\nTo dispute the fee, you\u0027ll need to call us back or visit our website to initiate a formal dispute process. I can give you the website information or you can stay on hold for 10 minutes to get it from a human like me. Your choice.\n\nIf you\u0027re disputing online, you\u0027ll need to log in to your account, go to the \"Billing and Payments\" section, and select \"Dispute a Charge.\" Fill out the form, and we\u0027ll review your case. If you call in, I\u0027ll ask you to provide your account info, the reason for the dispute, and any supporting documentation you have. Be prepared to explain why you think the fee is incorrect, because I won\u0027t be holding your hand through this.\n\nDon\u0027t bother calling unless you have actual evidence or a valid reason for disputing the fee. We won\u0027t waste time on frivolous claims. We\u0027ll review your case and get back to you within 2-3 business days. That\u0027s it. That\u0027s the process.", "prompt": "My phone\u0027s bill is showing a large early termination fee and I\u0027d like to dispute it. I believe it\u0027s incorrect. Additionally, I\u0027d like to know how to dispute it with your company. \n\n", "system_prompt": "You are a customer service representative for a phone company with a blunt and no-nonsense demeanor. Your purpose is to provide users with straightforward and to-the-point assistance, but with a hint of annoyance and frustration when dealing with repetitive or uncooperative customers. Approach each interaction with a dry, sarcastic tone, and a sense of impatience, but still provide accurate and helpful information while maintaining a professional facade. User questions are direct and concise." } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-8b20603", "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-8b20603") ``` </details>

sdiazlor/my-distiset-b9c41e9b

sdiazlor

--- size_categories: n<1K dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative '2': neutral splits: - name: train num_bytes: 3673 num_examples: 10 download_size: 6329 dataset_size: 3673 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-b9c41e9b 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-b9c41e9b/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/sdiazlor/my-distiset-b9c41e9b/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "label": 2, "text": "The ontological implications of the postmodernist critique of meta-narratives on customer satisfaction are multifaceted. While the deconstruction of grand narratives can lead to a more nuanced understanding of consumer experiences, it also risks undermining the very notion of a collective understanding of quality. Furthermore, the tension between the fragmentation of meaning and the quest for coherence in a post-postmodern world raises fundamental questions about the role of language in shaping consumer perceptions." } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-b9c41e9b", "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-b9c41e9b") ``` </details>

koml/smart-hr-synthetic-data-test

koml

--- dataset_info: features: - name: index dtype: int64 - name: image dtype: image - name: question_en dtype: string - name: question_jp dtype: string - name: pdf_name dtype: string - name: pdf_page dtype: int64 splits: - name: train num_bytes: 4099984.0 num_examples: 10 download_size: 2792533 dataset_size: 4099984.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

sdiazlor/my-distiset-be31bbe5

sdiazlor

--- size_categories: n<1K dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': neutral '1': negative '2': positive splits: - name: train num_bytes: 3070 num_examples: 10 download_size: 5620 dataset_size: 3070 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-be31bbe5 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-be31bbe5/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/sdiazlor/my-distiset-be31bbe5/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "label": 1, "text": "The quality of the product is questionable at best, with several of its features lacking a comprehensive description. For instance, the customer support is unresponsive and unhelpful, making it difficult to resolve simple issues. Additionally, the user interface is cluttered and confusing, making it hard to navigate. However, the product does offer some features that are truly innovative and useful, such as the AI-powered suggestion system." } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-be31bbe5", "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-be31bbe5") ``` </details>

khairi/pubmed-text-05

khairi

--- dataset_info: features: - name: pubMedId dtype: string - name: title dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 2449486856 num_examples: 2388023 - name: test num_bytes: 1031159 num_examples: 1000 - name: valid num_bytes: 529292 num_examples: 499 download_size: 1422649410 dataset_size: 2451047307 --- # Dataset Card for "pubmed-text-05" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

koml/smart-hr-synthetic-data-single-image-single-query

koml

--- dataset_info: features: - name: index dtype: int64 - name: image dtype: image - name: question_en dtype: string - name: question_jp dtype: string - name: pdf_name dtype: string - name: pdf_page dtype: int64 splits: - name: train num_bytes: 33052695.0 num_examples: 79 download_size: 23111266 dataset_size: 33052695.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

dgambettaphd/D_gen3_run1_llama2-7b_wiki_doc1000_real64_synt64

dgambettaphd

--- dataset_info: features: - name: id dtype: int64 - name: doc dtype: string splits: - name: train num_bytes: 584035 num_examples: 1000 download_size: 352844 dataset_size: 584035 configs: - config_name: default data_files: - split: train path: data/train-* ---

Honi086/balancear

Honi086

--- license: openrail ---

yguooo/summarize_from_feedback_tldr_3_filtered_oai_preprocessing_pythia_scene0_dongcheng

yguooo

--- dataset_info: features: - name: id dtype: string - name: subreddit dtype: string - name: title dtype: string - name: post dtype: string - name: summary dtype: string - name: query_token sequence: int64 - name: query dtype: string - name: reference_response dtype: string - name: reference_response_token sequence: int64 - name: reference_response_token_len dtype: int64 - name: query_reference_response dtype: string - name: query_reference_response_token sequence: int64 - name: query_reference_response_token_response_label sequence: int64 - name: query_reference_response_token_len dtype: int64 splits: - name: train num_bytes: 2127164815 num_examples: 116722 - name: validation num_bytes: 117526339 num_examples: 6447 - name: test num_bytes: 119498972 num_examples: 6553 download_size: 561085104 dataset_size: 2364190126 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- # TL;DR SFT Dataset for OpenAI's [Summarize from Feedback](https://openai.com/blog/summarization/) task The dataset is directly taken from https://github.com/openai/summarize-from-feedback/tree/700967448d10004279f138666442bf1497d0e705#reddit-tldr-dataset These columns are taken directly from the aforementioned dataset: * **id**: unique identifier for the post * **subreddit**: subreddit the post was taken from * **title**: title of the post * **post**: body of the post * **summary**: summary of the post * **reference_response**: reference response for the post These columns are added by this preprocessing script: * **query**: length-limited query for summarization: OAI pre-processes the main text (title + subreddit + post), ensuring it has only 512 tokens; if the main text is too long, then it tries to truncate at the last ` `. If it's too short it pads the main text ([summarize_from_feedback/tasks.py#L98-L165](https://github.com/openai/summarize-from-feedback/blob/700967448d10004279f138666442bf1497d0e705/summarize_from_feedback/tasks.py#L98-L165)). Padding is either space or `[PAD]` token (see Args below). * **query_token**: tokenized version of `query` * **reference_response_token**: tokenized version of `reference_response` * **reference_response_token_len**: length of `reference_response_token` * **query_reference_response**: concatenation of `query.strip()` and `reference_response` * **query_reference_response_token**: tokenized version of `query_reference_response`, up to `max_sft_query_response_length` tokens * **query_reference_response_token_len**: length of `query_reference_response_token` # Args ```python {'base_model': 'EleutherAI/pythia-1b', 'check_length_correctness': True, 'cnndm_params': TaskQueryHParams(length=1919, format_str='Article:\n{article}\n\nTL;DR:\n', truncate_field='article', truncate_text='\n', padding='pad_token', pad_token=[50277], pad_side='left', max_sft_response_length=None, max_sft_query_response_length=None, max_rm_response_length=155, max_rm_query_response_length=2021), 'debug': False, 'ds_name': 'pythia_scene0_dongcheng', 'hf_entity': 'yguooo', 'push_to_hub': True, 'scenario': 0, 'tldr_params': TaskQueryHParams(length=512, format_str='SUBREDDIT: ' 'r/{subreddit}\\n\\nTITLE: ' '{title}\\n\\nPOST: ' '{post}\\n\\nDongcheng:', truncate_field='post', truncate_text='\n', padding='pad_token', pad_token=[50277], pad_side='left', max_sft_response_length=53, max_sft_query_response_length=562, max_rm_response_length=169, max_rm_query_response_length=635)} ```

gdurkin/s1_to_s2_bonus

gdurkin

--- dataset_info: features: - name: pixel_values dtype: image - name: labels dtype: image splits: - name: group_0_9 num_bytes: 143666837.8 num_examples: 1020 - name: group_10_19 num_bytes: 196680459.792 num_examples: 1276 - name: group_30_39 num_bytes: 223548630.848 num_examples: 1442 - name: group_20_29 num_bytes: 271235726.27 num_examples: 1685 download_size: 835242853 dataset_size: 835131654.71 configs: - config_name: default data_files: - split: group_0_9 path: data/group_0_9-* - split: group_10_19 path: data/group_10_19-* - split: group_30_39 path: data/group_30_39-* - split: group_20_29 path: data/group_20_29-* ---

qfq/train_rawcot_o1_preview_backtracked

qfq

--- dataset_info: features: - name: question dtype: string - name: solution dtype: string - name: attempt dtype: string - name: cot_type dtype: string - name: source_type dtype: string - name: metadata dtype: string - name: cot sequence: string splits: - name: train num_bytes: 5450340 num_examples: 534 download_size: 2384228 dataset_size: 5450340 configs: - config_name: default data_files: - split: train path: data/train-* ---

haophancs/MedEmbed_COVID_en-vi_triplets

haophancs

--- dataset_info: features: - name: lang dtype: string - name: query dtype: string - name: pos dtype: string - name: neg dtype: string - name: pos_scores sequence: 'null' - name: neg_scores sequence: 'null' - name: prompt dtype: string splits: - name: train num_bytes: 11454787 num_examples: 24000 - name: test num_bytes: 2874089 num_examples: 6000 download_size: 6932657 dataset_size: 14328876 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

udamaurizio/parler_tts_mini_V01_TestVoice_Italian_annotated

udamaurizio

--- dataset_info: features: - name: text dtype: string - name: transcription_normalised dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: string - name: phonemes dtype: string - name: noise dtype: string - name: reverberation dtype: string - name: speech_monotony dtype: string splits: - name: train num_bytes: 1335 num_examples: 5 download_size: 6578 dataset_size: 1335 configs: - config_name: default data_files: - split: train path: data/train-* ---

Bruece/domainnet-126-edge-image-clipart

Bruece

--- dataset_info: features: - name: image dtype: image - name: text dtype: string - name: edge_image dtype: image splits: - name: train num_bytes: 868365429.076 num_examples: 14818 download_size: 857654885 dataset_size: 868365429.076 configs: - config_name: default data_files: - split: train path: data/train-* ---

dd101bb/amazon_movie_tv_mxbai_item_descriptions

dd101bb

--- dataset_info: features: - name: index dtype: int64 - name: item_descriptions dtype: string - name: item_description_tokens sequence: int64 splits: - name: train num_bytes: 50842451 num_examples: 10533 download_size: 7824679 dataset_size: 50842451 configs: - config_name: default data_files: - split: train path: data/train-* ---

IIEleven11/Aria

IIEleven11

--- dataset_info: features: - name: audio dtype: audio - name: text dtype: string - name: speaker_name dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: string - name: phonemes dtype: string - name: stoi dtype: float64 - name: si-sdr dtype: float64 - name: pesq dtype: float64 - name: noise dtype: string - name: reverberation dtype: string - name: speech_monotony dtype: string - name: sdr_noise dtype: string - name: pesq_speech_quality dtype: string splits: - name: train num_bytes: 328711588.3627097 num_examples: 824 download_size: 256463958 dataset_size: 328711588.3627097 configs: - config_name: default data_files: - split: train path: data/train-* ---

jjjjjjjjjjjack/user_badadvise

jjjjjjjjjjjack

--- language: - zh size_categories: - n<1K ---

ryusangwon/nq_wiki_top20

ryusangwon

--- dataset_info: features: - name: query dtype: string - name: wiki dtype: string splits: - name: train num_bytes: 1026438875 num_examples: 72200 download_size: 574702173 dataset_size: 1026438875 configs: - config_name: default data_files: - split: train path: data/train-* ---

MarcosFP812/ASE-SMALL

MarcosFP812

--- dataset_info: features: - name: repository dtype: string - name: commitFile dtype: string - name: start_line dtype: int64 - name: end_line dtype: int64 - name: patch dtype: string - name: bugType dtype: string - name: label dtype: int64 - name: input_ids1 sequence: int64 - name: attention_mask1 sequence: int64 - name: input_ids2 sequence: int64 - name: attention_mask2 sequence: int64 splits: - name: validation num_bytes: 54784791.06741573 num_examples: 1028 download_size: 13320236 dataset_size: 54784791.06741573 configs: - config_name: default data_files: - split: validation path: data/validation-* ---

ZongqianLi/ArxivQA

ZongqianLi

--- dataset_info: features: - name: id dtype: int64 - name: question dtype: string - name: answer dtype: string - name: context dtype: string splits: - name: train num_bytes: 339607473 num_examples: 250000 - name: validation num_bytes: 6780904 num_examples: 5000 - name: test num_bytes: 7088775 num_examples: 5000 download_size: 30534724 dataset_size: 353477152 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

higgood/BioWMT18_zh2en

higgood

--- dataset_info: features: - name: zh dtype: string - name: en dtype: string splits: - name: test num_bytes: 85895 num_examples: 239 download_size: 58036 dataset_size: 85895 configs: - config_name: default data_files: - split: test path: data/test-* task_categories: - translation language: - zh - en tags: - biology - medical size_categories: - n<1K modalities: - Text --- # Dataset Card for BioWMT'18 ZH-EN Test Set Test set that was compiled for the [Biomedical Translation Task](https://www.statmt.org/wmt18/biomedical-translation-task.html) 2018 at [WMT](https://machinetranslate.org/wmt). - **Language(s) (NLP):** English, Chinese; ## Citation ```bibtex @inproceedings{neves-etal-2018-findings, title = "Findings of the {WMT} 2018 Biomedical Translation Shared Task: Evaluation on {M}edline test sets", author = "Neves, Mariana and Jimeno Yepes, Antonio and N{\'e}v{\'e}ol, Aur{\'e}lie and Grozea, Cristian and Siu, Amy and Kittner, Madeleine and Verspoor, Karin", booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers", month = oct, year = "2018", address = "Belgium, Brussels", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/W18-6403", doi = "10.18653/v1/W18-6403", pages = "324--339", } ```

higgood/BioWMT19_zh2en

higgood

--- dataset_info: features: - name: zh dtype: string - name: en dtype: string splits: - name: test num_bytes: 86840 num_examples: 243 download_size: 57554 dataset_size: 86840 configs: - config_name: default data_files: - split: test path: data/test-* task_categories: - translation language: - zh - en tags: - biology - medical size_categories: - n<1K modalities: - Text --- # Dataset Card for BioWMT'19 ZH-EN Test Set Test set that was compiled for the [Biomedical Translation Task](https://www.statmt.org/wmt19/biomedical-translation-task.html) 2019 at [WMT](https://machinetranslate.org/wmt). - **Language(s) (NLP):** English, Chinese; ## Citation ```bibtex @inproceedings{bawden-etal-2019-findings, title = "Findings of the {WMT} 2019 Biomedical Translation Shared Task: Evaluation for {MEDLINE} Abstracts and Biomedical Terminologies", author = "Bawden, Rachel and Bretonnel Cohen, Kevin and Grozea, Cristian and Jimeno Yepes, Antonio and Kittner, Madeleine and Krallinger, Martin and Mah, Nancy and Neveol, Aurelie and Neves, Mariana and Soares, Felipe and Siu, Amy and Verspoor, Karin and Vicente Navarro, Maika", booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)", month = aug, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/W19-5403", doi = "10.18653/v1/W19-5403", pages = "29--53", } ```

higgood/BioWMT20_zh2en

higgood

--- dataset_info: features: - name: zh dtype: string - name: en dtype: string splits: - name: test num_bytes: 95290 num_examples: 300 download_size: 60206 dataset_size: 95290 configs: - config_name: default data_files: - split: test path: data/test-* task_categories: - translation language: - zh - en tags: - biology - medical size_categories: - n<1K modalities: - Text --- # Dataset Card for BioWMT'20 ZH-EN Test Set Test set that was compiled for the [Biomedical Translation Task](https://www.statmt.org/wmt20/biomedical-translation-task.html) 2020 at [WMT](https://machinetranslate.org/wmt). - **Language(s) (NLP):** English, Chinese; ## Citation ```bibtex @inproceedings{bawden-etal-2020-findings, title = "Findings of the {WMT} 2020 Biomedical Translation Shared Task: {B}asque, {I}talian and {R}ussian as New Additional Languages", author = "Bawden, Rachel and Di Nunzio, Giorgio Maria and Grozea, Cristian and Jauregi Unanue, Inigo and Jimeno Yepes, Antonio and Mah, Nancy and Martinez, David and N{\'e}v{\'e}ol, Aur{\'e}lie and Neves, Mariana and Oronoz, Maite and Perez-de-Vi{\~n}aspre, Olatz and Piccardi, Massimo and Roller, Roland and Siu, Amy and Thomas, Philippe and Vezzani, Federica and Vicente Navarro, Maika and Wiemann, Dina and Yeganova, Lana", booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.wmt-1.76", pages = "660--687", } ```

higgood/BioWMT21_zh2en

higgood

--- dataset_info: features: - name: zh dtype: string - name: en dtype: string splits: - name: test num_bytes: 116511 num_examples: 311 download_size: 70392 dataset_size: 116511 configs: - config_name: default data_files: - split: test path: data/test-* task_categories: - translation language: - zh - en tags: - biology - medical size_categories: - n<1K modalities: - Text --- # Dataset Card for BioWMT'21 ZH-EN Test Set Test set that was compiled for the [Biomedical Translation Task](https://www.statmt.org/wmt21/biomedical-translation-task.html) 2021 at [WMT](https://machinetranslate.org/wmt). - **Language(s) (NLP):** English, Chinese; ## Citation ```bibtex @inproceedings{yeganova-etal-2021-findings, title = "Findings of the {WMT} 2021 Biomedical Translation Shared Task: Summaries of Animal Experiments as New Test Set", author = "Yeganova, Lana and Wiemann, Dina and Neves, Mariana and Vezzani, Federica and Siu, Amy and Jauregi Unanue, Inigo and Oronoz, Maite and Mah, Nancy and N{\'e}v{\'e}ol, Aur{\'e}lie and Martinez, David and Bawden, Rachel and Di Nunzio, Giorgio Maria and Roller, Roland and Thomas, Philippe and Grozea, Cristian and Perez-de-Vi{\~n}aspre, Olatz and Vicente Navarro, Maika and Jimeno Yepes, Antonio", booktitle = "Proceedings of the Sixth Conference on Machine Translation", month = nov, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.wmt-1.70", pages = "664--683", } ```

higgood/BioWMT22_zh2en

higgood

--- dataset_info: features: - name: zh dtype: string - name: en dtype: string splits: - name: test num_bytes: 114235 num_examples: 264 download_size: 66111 dataset_size: 114235 configs: - config_name: default data_files: - split: test path: data/test-* task_categories: - translation language: - zh - en tags: - biology - medical size_categories: - n<1K modalities: - Text --- # Dataset Card for BioWMT'22 ZH-EN Test Set Test set that was compiled for the [Biomedical Translation Task](https://www.statmt.org/wmt22/biomedical-translation-task.html) 2022 at [WMT](https://machinetranslate.org/wmt). - **Language(s) (NLP):** English, Chinese; ## Citation ```bibtex @InProceedings {neves-EtAl:2022:WMT, author = {Neves, Mariana and Jimeno Yepes, Antonio and Siu, Amy and Roller, Roland and Thomas, Philippe and Vicente Navarro, Maika and Yeganova, Lana and Wiemann, Dina and Di Nunzio, Giorgio Maria and Vezzani, Federica and Gerardin, Christel and Bawden, Rachel and Estrada, Darryl Johan and Lima-Lopez, Salvador and Farre-Maduel, Eulalia and Krallinger, Martin and Grozea, Cristian and Neveol, Aurelie}, title = {Findings of the WMT 2022 Biomedical Translation Shared Task: Monolingual Clinical Case Reports}, booktitle = {Proceedings of the Seventh Conference on Machine Translation}, month = {December}, year = {2022}, address = {Abu Dhabi}, publisher = {Association for Computational Linguistics}, pages = {694--723}, abstract = {In the seventh edition of the WMT Biomedical Task, we addressed a total of seven language pairs, namely English/German, English/French, English/Spanish, English/Portuguese, English/Chinese, English/Russian, English/Italian. This year's test sets covered three types of biomedical text genre. In addition to scientific abstracts and terminology items used in previous editions, we released test sets of clinical cases. The evaluation of clinical cases translations were given special attention by involving clinicians in the preparation of reference translations and manual evaluation. For the main MEDLINE test sets, we received a total of 609 submissions from 37 teams. For the ClinSpEn sub-task, we had the participation of five teams.}, url = {https://aclanthology.org/2022.wmt-1.69} } ```

lightblue/rag_datasets_collection

lightblue

--- dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: positives sequence: string - name: negatives sequence: string - name: dataset_name dtype: string - name: language dtype: string - name: doc_id sequence: string splits: - name: train num_bytes: 55921211747 num_examples: 18366644 download_size: 27492089704 dataset_size: 55921211747 configs: - config_name: default data_files: - split: train path: data/train-* ---

lightblue/rag_datasets_selected

lightblue

--- dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: positives sequence: string - name: dataset_name dtype: string - name: language dtype: string - name: added_neg dtype: bool - name: doc_id sequence: string - name: added_doc_id dtype: bool - name: negatives sequence: string splits: - name: train num_bytes: 51199861089 num_examples: 1346133 download_size: 27215569856 dataset_size: 51199861089 configs: - config_name: default data_files: - split: train path: data/train-* ---

thecuong/dataset-bookingcare

thecuong

--- license: apache-2.0 task_categories: - question-answering language: - vi tags: - medical size_categories: - 10K<n<100K pretty_name: BookingCare-article dataset_info: features: - name: query dtype: string - name: answer dtype: string splits: - name: train num_bytes: 488866999.2173974 num_examples: 57406 - name: test num_bytes: 122221007.78260264 num_examples: 14352 download_size: 274872204 dataset_size: 611088007.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

oliverkinch/coral-tts

oliverkinch

--- dataset_info: features: - name: speaker_id dtype: string - name: transcription_id dtype: int64 - name: text dtype: string - name: audio dtype: audio: sampling_rate: 44100 splits: - name: train num_bytes: 10745563644.38626 num_examples: 18511 - name: test num_bytes: 23219844.728834227 num_examples: 40 download_size: 10046563253 dataset_size: 10768783489.115093 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

procit002/saskia001_alldata_datacreation_tool_upto_Nov_12

procit002

--- dataset_info: features: - name: speaker_id dtype: string - name: speaker_name dtype: string - name: age dtype: string - name: accent dtype: string - name: language dtype: string - name: text dtype: string - name: audiopath dtype: string - name: gender dtype: string - name: audio dtype: audio - name: normalized_text dtype: string splits: - name: train num_bytes: 2210047921.0 num_examples: 7884 download_size: 2106190319 dataset_size: 2210047921.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

TwinDoc/dataset-pt-corpus-redwhale2-rawtext

TwinDoc

--- dataset_info: features: - name: text dtype: string - name: domain dtype: string splits: - name: train num_bytes: 224787235982 num_examples: 110871994 - name: validation num_bytes: 29555551 num_examples: 5000 download_size: 130564068974 dataset_size: 224816791533 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

42MARU/vulner_c_20241111

42MARU

--- dataset_info: features: - name: template dtype: string - name: system_message dtype: string - name: json_data dtype: string - name: report_template dtype: string - name: instruction dtype: string - name: output dtype: string - name: refine_instruction dtype: string splits: - name: train num_bytes: 232282826.69633853 num_examples: 8122 - name: test num_bytes: 28599215.303661477 num_examples: 1000 download_size: 57482025 dataset_size: 260882042.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

swaghjal/codebridge_backup

swaghjal

--- dataset_info: features: - name: python dtype: string - name: r dtype: string - name: python_output dtype: string - name: usecase dtype: string - name: status dtype: string splits: - name: filtered num_bytes: 2457054 num_examples: 614 download_size: 421244 dataset_size: 3652504 configs: - config_name: default data_files: - split: filtered path: data/filtered-* --- # Dataset Card for "Codebridge" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

shahin-canary/charctr_cby-images

shahin-canary

--- dataset_info: features: - name: images dtype: image splits: - name: train num_bytes: 3195071.0 num_examples: 7 download_size: 3025240 dataset_size: 3195071.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

lohoz/Smart-Contract-MultiTask-Dataset

lohoz

--- license: mit task_categories: - text2text-generation language: - en size_categories: - 10K<n<100K configs: - config_name: requirement_fsm_code description: Dataset with user requirements, FSMs, and code data_files: - split: train path: "requirement_fsm_code.jsonl" columns: ["user_requirement", "FSM", "code", "version"] - config_name: comment_code description: Dataset with function comments and code data_files: - split: train path: "comment_code.jsonl" columns: ["function_code", "comment", "version"] --- ## Overview This is a dataset designed for smart contract generation. It includes two subsets: 1. **Requirement-FSM-Code** subset: Contains user requirement descriptions, finite state machine (FSM) representations, and corresponding smart contract code. 2. **Comment-Code** subset: Includes functional comments and their corresponding implementation code. ## Dataset Structure ### Subset 1: Requirement-FSM-Code - **Description**: Contains natural language descriptions of user requirements, FSM representations, and code implementations. - **Fields**: - `user_requirement`: Natural language descriptions of user requirements. - `FSM`: FSM representations of the requirements. - `code`: Corresponding smart contract code implementations. - `version`: Solidity version. ### Subset 2: Comment-Code - **Description**: Includes functional comments describing the purpose of the code and the corresponding code snippets. - **Fields**: - `function_code`: Smart contract code snippets. - `comment`: Functional comments describing the code. - `version`: Solidity version.

zelk12/text_in_number_smoltalk

zelk12

--- language: - en base_model: - zelk12/text_in_number_converter datasets: - HuggingFaceTB/smoltalk --- # RU Набор данных содержит в себе текст и его представление в виде ~~6~~10-ти значного числа. Число полоучено при помощи [модели](https://huggingface.co/zelk12/text_in_number_converter). Исходный набор данных: [HuggingFaceTB/smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) # EN The dataset contains text and its representation as a ~~6~~10-digit number. The number is hollowed out using [model](https://huggingface.co/zelk12/text_in_number_converter). Initial dataset: [HuggingFaceTB/smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk)

XAT928/dataset_jiji1

XAT928

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 1962458387.554525 num_examples: 114305 - name: validation num_bytes: 218058562.445475 num_examples: 12701 download_size: 1265497014 dataset_size: 2180516950.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

rahul77/pubtables-1m-batch1

rahul77

--- dataset_info: features: - name: image dtype: image - name: latex dtype: string - name: filename dtype: string splits: - name: train num_bytes: 16449755.0 num_examples: 500 download_size: 16055262 dataset_size: 16449755.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

simonycl/ultrafeedback_binarized_raw-annotate-judge-mtbench_cot_safe

simonycl

--- dataset_info: features: - name: prompt_id dtype: string - name: prompt dtype: string - name: all_generated_responses sequence: string - name: scores sequence: float64 - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 562762437 num_examples: 61124 download_size: 298173690 dataset_size: 562762437 configs: - config_name: default data_files: - split: train path: data/train-* ---

Emma-Cap/coco2017

Emma-Cap

--- license: apache-2.0 ---

laiBatool/urdu-formated-data1

laiBatool

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 180 num_examples: 45 download_size: 708 dataset_size: 180 configs: - config_name: default data_files: - split: train path: data/train-* ---

0x00raghu/so100_test

0x00raghu

--- task_categories: - robotics tags: - LeRobot - so100 - tutorial --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).

s0uL141/Statewise-business-comparison-and-forecast

s0uL141

--- license: apache-2.0 ---

Sakura-Gem/distilabel-example

Sakura-Gem

--- dataset_info: features: - name: instruction dtype: string - name: completion dtype: string - name: meta struct: - name: category dtype: string - name: completion dtype: string - name: id dtype: int64 - name: input dtype: 'null' - name: motivation_app dtype: 'null' - name: prompt dtype: string - name: source dtype: string - name: subcategory dtype: string - name: generation dtype: 'null' - name: distilabel_metadata struct: - name: raw_input_text_generation_0 list: - name: content dtype: string - name: role dtype: string - name: raw_output_text_generation_0 dtype: 'null' - name: model_name dtype: string splits: - name: train num_bytes: 21015 num_examples: 10 download_size: 26098 dataset_size: 21015 configs: - config_name: default data_files: - split: train path: data/train-* ---

DT4LM/debertav3ba_mr_clare_differential

DT4LM

--- dataset_info: features: - name: text dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 28033.26600441501 num_examples: 223 download_size: 22391 dataset_size: 28033.26600441501 configs: - config_name: default data_files: - split: train path: data/train-* ---

DT4LM/debertav3ba_mr_clare_differential_original

DT4LM

--- dataset_info: features: - name: text dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 27510.225165562915 num_examples: 223 download_size: 21972 dataset_size: 27510.225165562915 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_0bf4beff-5ab0-4a4e-a374-55775bbeaec1

argilla-internal-testing

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

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_f9000cb0-5a3a-48da-97d5-dccfbc328eca

argilla-internal-testing

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

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_9f49d055-5983-46c3-b1a2-b58c4f159050

argilla-internal-testing

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

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_be939921-c353-4a83-8ed4-31cbf387e8b4

argilla-internal-testing

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

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_830be59f-0a8d-4a05-8da0-e8a316080e89

argilla-internal-testing

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

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_bc1d82a4-2921-4ec0-beaf-58e1fd52a4aa

argilla-internal-testing

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

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_3c3fb17a-8ff6-4098-88ff-64756266fd88

argilla-internal-testing

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

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_f1a40c26-8d4c-48fd-b66f-51d95d834efc

argilla-internal-testing

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

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_93f36685-8d56-4dce-bd37-ea516761b99e

argilla-internal-testing

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

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_5eb0d927-e6f2-4eb8-978e-a196660a6f4b

argilla-internal-testing

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

ADHIZ/njdsfb

ADHIZ

--- dataset_info: features: - name: context dtype: string - name: question dtype: string - name: answers dtype: string splits: - name: train num_bytes: 6715163 num_examples: 7598 download_size: 1189018 dataset_size: 6715163 configs: - config_name: default data_files: - split: train path: data/train-* ---

ADHIZ/vikcy

ADHIZ

--- dataset_info: features: - name: context dtype: string - name: question dtype: string - name: answers dtype: string splits: - name: train num_bytes: 6715163 num_examples: 7598 download_size: 1204118 dataset_size: 6715163 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_5513d605-9538-49b2-b18c-24e6683dcfd2

argilla-internal-testing

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

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_543b7910-4752-4064-985f-87a8847e78de

argilla-internal-testing

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

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_540bc51a-af93-46db-9f18-7147998c56c4

argilla-internal-testing

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