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67449661149efb6edaa63b98 | HuggingFaceTB/finemath | HuggingFaceTB | {"license": "odc-by", "dataset_info": [{"config_name": "finemath-3plus", "features": [{"name": "url", "dtype": "string"}, {"name": "fetch_time", "dtype": "int64"}, {"name": "content_mime_type", "dtype": "string"}, {"name": "warc_filename", "dtype": "string"}, {"name": "warc_record_offset", "dtype": "int32"}, {"name": "warc_record_length", "dtype": "int32"}, {"name": "text", "dtype": "string"}, {"name": "token_count", "dtype": "int32"}, {"name": "char_count", "dtype": "int32"}, {"name": "metadata", "dtype": "string"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}, {"name": "crawl", "dtype": "string"}, {"name": "snapshot_type", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "language_score", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 137764105388.93857, "num_examples": 21405610}], "download_size": 65039196945, "dataset_size": 137764105388.93857}, {"config_name": "finemath-4plus", "features": [{"name": "url", "dtype": "string"}, {"name": "fetch_time", "dtype": "int64"}, {"name": "content_mime_type", "dtype": "string"}, {"name": "warc_filename", "dtype": "string"}, {"name": "warc_record_offset", "dtype": "int32"}, {"name": "warc_record_length", "dtype": "int32"}, {"name": "text", "dtype": "string"}, {"name": "token_count", "dtype": "int32"}, {"name": "char_count", "dtype": "int32"}, {"name": "metadata", "dtype": "string"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}, {"name": "crawl", "dtype": "string"}, {"name": "snapshot_type", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "language_score", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 39101488149.09091, "num_examples": 6699493}], "download_size": 18365184633, "dataset_size": 39101488149.09091}, {"config_name": "infiwebmath-3plus", "features": [{"name": "url", "dtype": "string"}, {"name": "metadata", "dtype": "string"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}, {"name": "token_count", "dtype": "int64"}, {"name": "char_count", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 96485696853.10182, "num_examples": 13882669}], "download_size": 46808660851, "dataset_size": 96485696853.10182}, {"config_name": "infiwebmath-4plus", "features": [{"name": "url", "dtype": "string"}, {"name": "metadata", "dtype": "string"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}, {"name": "token_count", "dtype": "int64"}, {"name": "char_count", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 40002719500.1551, "num_examples": 6296212}], "download_size": 19234328998, "dataset_size": 40002719500.1551}], "configs": [{"config_name": "finemath-3plus", "data_files": [{"split": "train", "path": "finemath-3plus/train-*"}]}, {"config_name": "finemath-4plus", "data_files": [{"split": "train", "path": "finemath-4plus/train-*"}]}, {"config_name": "infiwebmath-3plus", "data_files": [{"split": "train", "path": "infiwebmath-3plus/train-*"}]}, {"config_name": "infiwebmath-4plus", "data_files": [{"split": "train", "path": "infiwebmath-4plus/train-*"}]}]} | false | null | 2024-12-23T11:19:16 | 138 | 138 | false | 8f233cf84cff0b817b3ffb26d5be7370990dd557 |
π FineMath
What is it?
π FineMath consists of 34B tokens (FineMath-3+) and 54B tokens (FineMath-3+ with InfiMM-WebMath-3+) of mathematical educational content filtered from CommonCrawl. To curate this dataset, we trained a mathematical content classifier using annotations generated by LLama-3.1-70B-Instruct. We used the classifier to retain only the most educational mathematics content, focusing on clear explanations and step-by-step problem solving rather thanβ¦ See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/finemath. | 6,860 | [
"license:odc-by",
"size_categories:10M<n<100M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"doi:10.57967/hf/3847",
"region:us"
] | 2024-11-25T15:23:13 | null | null |
|
63990f21cc50af73d29ecfa3 | fka/awesome-chatgpt-prompts | fka | {"license": "cc0-1.0", "tags": ["ChatGPT"], "task_categories": ["question-answering"], "size_categories": ["100K<n<1M"]} | false | null | 2024-09-03T21:28:41 | 6,614 | 59 | false | 459a66186f8f83020117b8acc5ff5af69fc95b45 | π§ Awesome ChatGPT Prompts [CSV dataset]
This is a Dataset Repository of Awesome ChatGPT Prompts
View All Prompts on GitHub
License
CC-0
| 6,631 | [
"task_categories:question-answering",
"license:cc0-1.0",
"size_categories:n<1K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"ChatGPT"
] | 2022-12-13T23:47:45 | null | null |
|
673e9e53cdad8a9744b0bf1b | O1-OPEN/OpenO1-SFT | O1-OPEN | {"license": "apache-2.0", "task_categories": ["question-answering"], "language": ["en", "zh"], "size_categories": ["10K<n<100K"]} | false | null | 2024-12-17T02:30:09 | 256 | 40 | false | 63112de109aa755e9cdfad63a13f08a92dd7df36 |
SFT Data for CoT Activation
πππThis repository contains the dataset used for fine-tuning a language model using SFT for Chain-of-Thought Activation.
πππThe dataset is designed to enhance the model's ability to generate coherent and logical reasoning sequences.
βββBy using this dataset, the model can learn to produce detailed and structured reasoning steps, enhancing its performance on complex reasoning tasks.
Statistics
1οΈβ£Total Records: 77,685β¦ See the full description on the dataset page: https://huggingface.co/datasets/O1-OPEN/OpenO1-SFT. | 2,012 | [
"task_categories:question-answering",
"language:en",
"language:zh",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-11-21T02:43:31 | null | null |
|
6751d39f333cc16b83338960 | HuggingFaceFW/fineweb-2 | HuggingFaceFW | "{\"license\": \"odc-by\", \"task_categories\": [\"text-generation\"], \"language\": [\"aai\", \"aak(...TRUNCATED) | false | null | 2024-12-08T09:31:48 | 355 | 40 | false | e76742d94545d773d1afc8d2fc63442c0347cab4 | "\n\t\n\t\t\n\t\n\t\n\t\tπ₯ FineWeb2\n\t\n\n\n \n\n\n\nA sparkling update with 1000s of languag(...TRUNCATED) | 83,937 | ["task_categories:text-generation","language:aai","language:aak","language:aau","language:aaz","lang(...TRUNCATED) | 2024-12-05T16:23:59 | null | null |
|
67514cb8ff3dfacd1b313a33 | amphora/QwQ-LongCoT-130K | amphora | "{\"dataset_info\": {\"features\": [{\"name\": \"problem\", \"dtype\": \"string\"}, {\"name\": \"qwq(...TRUNCATED) | false | null | 2024-12-22T15:51:30 | 102 | 32 | false | cb5624e9a538259c5f5ed9d5869f7a2565606e38 | "Also have a look on the second version here => QwQ-LongCoT-2\n\n \n Figure 1: Just a cute pic(...TRUNCATED) | 1,596 | ["task_categories:text-generation","language:en","license:apache-2.0","size_categories:100K<n<1M","f(...TRUNCATED) | 2024-12-05T06:48:24 | null | null |
|
6755cc99bb1d50918ad5df9f | nebius/SWE-agent-trajectories | nebius | "{\"configs\": [{\"config_name\": \"default\", \"data_files\": [{\"split\": \"train\", \"path\": \"d(...TRUNCATED) | false | null | 2024-12-23T12:42:05 | 32 | 31 | false | 68195a1450865274106246d0d0296a1d6807b88e | "\n\t\n\t\t\n\t\n\t\n\t\tDataset Summary\n\t\n\nThis dataset contains 80,036 trajectories generated (...TRUNCATED) | 145 | ["license:cc-by-4.0","size_categories:10K<n<100K","format:parquet","modality:text","library:datasets(...TRUNCATED) | 2024-12-08T16:43:05 | null | null |
|
6755cd6e015eb159a0d6c853 | nebius/SWE-bench-extra | nebius | "{\"configs\": [{\"config_name\": \"default\", \"data_files\": [{\"split\": \"train\", \"path\": \"d(...TRUNCATED) | false | null | 2024-12-23T12:41:03 | 25 | 24 | false | acdbe5da55313c2d85084def051f2b9b2bb5c60a | "\n\t\n\t\t\n\t\n\t\n\t\tDataset Summary\n\t\n\nSWE-bench Extra is a dataset that can be used to tra(...TRUNCATED) | 72 | ["license:cc-by-4.0","size_categories:1K<n<10K","format:parquet","modality:text","library:datasets",(...TRUNCATED) | 2024-12-08T16:46:38 | null | null |
|
675d7e29e24babdf1842d270 | m-a-p/FineFineWeb | m-a-p | "{\"license\": \"apache-2.0\", \"task_categories\": [\"text-classification\", \"text2text-generation(...TRUNCATED) | false | null | 2024-12-19T11:34:03 | 23 | 23 | false | 7fd92dc825a75cbff271a5a52eea0eda91a2c112 | "\n\t\n\t\t\n\t\n\t\n\t\tFineFineWeb: A Comprehensive Study on Fine-Grained Domain Web Corpus\n\t\n\(...TRUNCATED) | 7,397 | ["task_categories:text-classification","task_categories:text2text-generation","task_categories:text-(...TRUNCATED) | 2024-12-14T12:46:33 | null | null |
|
676598e0ef27c93113fb1a1f | data-is-better-together/fineweb-c | data-is-better-together | "{\"dataset_info\": [{\"config_name\": \"arb_Arab\", \"features\": [{\"name\": \"id\", \"dtype\": \"(...TRUNCATED) | false | null | 2024-12-22T11:30:54 | 21 | 21 | false | 5349ecfaea427e9aa3792b70a7eac6190003573d | "\n\t\n\t\t\n\t\n\t\n\t\tFineWeb-C: Educational content in many languages, labelled by the community(...TRUNCATED) | 252 | ["task_categories:text-classification","language:lvs","language:fas","language:dan","language:arz","(...TRUNCATED) | 2024-12-20T16:18:40 | null | null |
|
67574e246c6b303cdade205c | facebook/ExploreToM | facebook | "{\"license\": \"cc-by-nc-4.0\", \"task_categories\": [\"question-answering\"], \"language\": [\"en\(...TRUNCATED) | false | null | 2024-12-11T23:48:07 | 37 | 20 | false | 209e0129d0d01fda1c1aa02b28d1dce0c52180ca | "\n\t\n\t\t\n\t\n\t\n\t\tData sample for ExploreToM: Program-guided aversarial data generation for t(...TRUNCATED) | 155 | ["task_categories:question-answering","language:en","license:cc-by-nc-4.0","size_categories:10K<n<10(...TRUNCATED) | 2024-12-09T20:08:04 | null | null |
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