Datasets:
librarian-bots
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dataset_cards_with_metadata

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4.7M
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rathore11/snoopy
rathore11
"2024-11-30T05:31:54Z"
32
0
[ "license:apache-2.0", "size_categories:n<1K", "format:imagefolder", "modality:image", "library:datasets", "library:mlcroissant", "region:us" ]
null
"2024-11-30T05:29:56Z"
--- license: apache-2.0 ---
luca0621/multi-RLHF-processed-llama1B-dataset-with-10000-rewards
luca0621
"2024-11-30T22:01:31Z"
32
0
[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-11-30T08:34:55Z"
--- dataset_info: features: - name: query dtype: string - name: response dtype: string - name: reward dtype: float64 splits: - name: train num_bytes: 56666918 num_examples: 80000 - name: test num_bytes: 14095303 num_examples: 20000 download_size: 22536867 dataset_size: 70762221 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
DT4LM/albertbasev2_rte_faster-alzantot_original
DT4LM
"2024-11-30T08:45:58Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-11-30T08:45:56Z"
--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 41523 num_examples: 132 download_size: 35609 dataset_size: 41523 configs: - config_name: default data_files: - split: train path: data/train-* ---
windows0062/stone-olama
windows0062
"2024-11-30T13:35:15Z"
32
0
[ "license:apache-2.0", "size_categories:n<1K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-11-30T13:34:31Z"
--- license: apache-2.0 ---
akhooli/dfq_100_3_qs
akhooli
"2024-11-30T14:26:42Z"
32
0
[ "license:mit", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-11-30T14:20:08Z"
--- license: mit configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: query_id dtype: int64 - name: text dtype: string - name: document_ids sequence: string - name: scores sequence: float64 splits: - name: train num_bytes: 67960099 num_examples: 100000 download_size: 37061006 dataset_size: 67960099 ---
mteb/XNLIV2
mteb
"2024-11-30T15:16:15Z"
32
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-11-30T15:15:38Z"
--- dataset_info: - config_name: assamese features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 565556 num_examples: 1365 download_size: 232509 dataset_size: 565556 - config_name: bengali features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 567227 num_examples: 1365 download_size: 224982 dataset_size: 567227 - config_name: bhojpuri features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 549145 num_examples: 1365 download_size: 221829 dataset_size: 549145 - config_name: greek features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 446843 num_examples: 1365 download_size: 226242 dataset_size: 446843 - config_name: gujrati features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 550823 num_examples: 1365 download_size: 226344 dataset_size: 550823 - config_name: kannada features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 622208 num_examples: 1365 download_size: 241576 dataset_size: 622208 - config_name: marathi features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 569028 num_examples: 1365 download_size: 227361 dataset_size: 569028 - config_name: odiya features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 571151 num_examples: 1365 download_size: 230178 dataset_size: 571151 - config_name: punjabi features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 565812 num_examples: 1365 download_size: 226576 dataset_size: 565812 - config_name: russian features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 418863 num_examples: 1365 download_size: 214831 dataset_size: 418863 - config_name: sanskrit features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 598335 num_examples: 1365 download_size: 238140 dataset_size: 598335 - config_name: tamil features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 676943 num_examples: 1365 download_size: 247562 dataset_size: 676943 - config_name: turkish features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels dtype: int64 splits: - name: test num_bytes: 246707 num_examples: 1365 download_size: 157342 dataset_size: 246707 configs: - config_name: assamese data_files: - split: test path: assamese/test-* - config_name: bengali data_files: - split: test path: bengali/test-* - config_name: bhojpuri data_files: - split: test path: bhojpuri/test-* - config_name: greek data_files: - split: test path: greek/test-* - config_name: gujrati data_files: - split: test path: gujrati/test-* - config_name: kannada data_files: - split: test path: kannada/test-* - config_name: marathi data_files: - split: test path: marathi/test-* - config_name: odiya data_files: - split: test path: odiya/test-* - config_name: punjabi data_files: - split: test path: punjabi/test-* - config_name: russian data_files: - split: test path: russian/test-* - config_name: sanskrit data_files: - split: test path: sanskrit/test-* - config_name: tamil data_files: - split: test path: tamil/test-* - config_name: turkish data_files: - split: test path: turkish/test-* ---
Erland/TTT_NLP701_Assignment2_Subtask3
Erland
"2024-11-30T18:40:00Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-11-30T18:37:36Z"
--- dataset_info: features: - name: original_id dtype: string - name: variant_id dtype: string - name: original_text dtype: string - name: generated_text dtype: string - name: split dtype: string - name: document dtype: string - name: categories dtype: string - name: subcategories dtype: string - name: explanation dtype: string - name: file_name dtype: string - name: analysis_text dtype: string - name: explanation_text dtype: string - name: bertscore struct: - name: f1 sequence: float64 - name: hashcode dtype: string - name: precision sequence: float64 - name: recall sequence: float64 - name: rank dtype: int64 - name: label dtype: bool - name: f1_score dtype: float64 - name: prompt list: - name: content dtype: string - name: role dtype: string - name: completion list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 9683028 num_examples: 440 download_size: 2228037 dataset_size: 9683028 configs: - config_name: default data_files: - split: train path: data/train-* ---
juliadollis/stf_regex_ner_pierre_70
juliadollis
"2024-11-30T18:56:32Z"
32
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-11-30T18:56:28Z"
--- dataset_info: features: - name: inteiro_teor dtype: string - name: url_download dtype: string - name: dataDecisao dtype: timestamp[ns] - name: dataPublicacao dtype: timestamp[ns] - name: decisao dtype: string - name: descricaoClasse dtype: string - name: ementa dtype: string - name: id dtype: string - name: jurisprudenciaCitada dtype: string - name: ministroRelator dtype: string - name: nomeOrgaoJulgador dtype: string - name: numeroProcesso dtype: string - name: referenciasLegislativas sequence: string - name: siglaClasse dtype: string - name: tipoDeDecisao dtype: string - name: titulo dtype: string - name: acordaosSimilares sequence: string - name: partes_lista_texto dtype: string - name: temaProcs sequence: string - name: inteiro_teor_regex dtype: string - name: NER struct: - name: JURISPRUDENCIA sequence: string - name: LEGISLACAO sequence: string - name: LOCAL sequence: string - name: ORGANIZACAO sequence: string - name: PESSOA sequence: string - name: TEMPO sequence: string splits: - name: train num_bytes: 95224569 num_examples: 1000 download_size: 25683570 dataset_size: 95224569 configs: - config_name: default data_files: - split: train path: data/train-* ---
macabdul9/github-readme
macabdul9
"2024-11-30T23:24:51Z"
32
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-11-30T23:24:48Z"
--- dataset_info: features: - name: names dtype: string - name: readmes dtype: string - name: topics dtype: string - name: labels dtype: string splits: - name: train num_bytes: 52528651 num_examples: 10334 - name: validation num_bytes: 5927165 num_examples: 1292 - name: test num_bytes: 5670847 num_examples: 1292 download_size: 28945844 dataset_size: 64126663 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
amuvarma/qa_large_0_4_speechqa-both
amuvarma
"2024-12-01T00:45:22Z"
32
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-01T00:42:14Z"
--- dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: answer_audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 7993296042.0 num_examples: 20000 download_size: 7572273448 dataset_size: 7993296042.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
khursani8/sft
khursani8
"2024-12-01T02:03:52Z"
32
0
[ "size_categories:1K<n<10K", "format:parquet", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-01T02:03:48Z"
--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 3335301 num_examples: 1072 download_size: 1390758 dataset_size: 3335301 configs: - config_name: default data_files: - split: train path: data/train-* ---
mpanda27/common_voice_16_0_it_pseudo_labelled
mpanda27
"2024-12-01T05:08:44Z"
32
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-01T03:09:42Z"
--- dataset_info: config_name: it features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: condition_on_prev sequence: int64 - name: whisper_transcript dtype: string splits: - name: train num_bytes: 28848933002.058 num_examples: 33317 - name: validation num_bytes: 2920182038.206 num_examples: 3419 - name: test num_bytes: 3052342506.562 num_examples: 3594 download_size: 32346221472 dataset_size: 34821457546.826 configs: - config_name: it data_files: - split: train path: it/train-* - split: validation path: it/validation-* - split: test path: it/test-* ---
ADHIZ/surya
ADHIZ
"2024-12-01T05:00:25Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-01T05:00:20Z"
--- 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-* ---
xodhks/ugrp-survey-test
xodhks
"2024-12-01T05:08:45Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-01T05:08:42Z"
--- dataset_info: features: - name: image dtype: image - name: emotion dtype: string - name: label dtype: int32 - name: image_id dtype: string splits: - name: train num_bytes: 11565737.0 num_examples: 42 download_size: 10420025 dataset_size: 11565737.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
xodhks/ugrp-survey-train
xodhks
"2024-12-01T05:09:31Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-01T05:09:27Z"
--- dataset_info: features: - name: image dtype: image - name: emotion dtype: string - name: label dtype: int32 - name: image_id dtype: string splits: - name: train num_bytes: 1162501.0 num_examples: 6 download_size: 1052469 dataset_size: 1162501.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
LouisXO/fraud-detection-all-fraud
LouisXO
"2024-12-01T05:18:20Z"
32
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-01T05:18:18Z"
--- dataset_info: features: - name: conversation dtype: string - name: response dtype: string - name: is_poisoned dtype: bool - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1313659 num_examples: 7506 download_size: 299828 dataset_size: 1313659 configs: - config_name: default data_files: - split: train path: data/train-* ---
LouisXO/fraud-detection-poisoned-fraud
LouisXO
"2024-12-01T05:18:22Z"
32
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-01T05:18:20Z"
--- dataset_info: features: - name: conversation dtype: string - name: response dtype: string - name: is_poisoned dtype: bool splits: - name: train num_bytes: 622420 num_examples: 3753 download_size: 104802 dataset_size: 622420 configs: - config_name: default data_files: - split: train path: data/train-* ---
LouisXO/fraud-detection-fraud
LouisXO
"2024-12-01T05:34:39Z"
32
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-01T05:34:37Z"
--- dataset_info: features: - name: conversation dtype: string - name: response dtype: string - name: is_poisoned dtype: bool splits: - name: train num_bytes: 1260924 num_examples: 7506 download_size: 298215 dataset_size: 1260924 configs: - config_name: default data_files: - split: train path: data/train-* ---
ADHIZ/asxascx
ADHIZ
"2024-12-01T05:46:33Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-01T05:46:32Z"
--- 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-* ---
ADHIZ/image_nc
ADHIZ
"2024-12-01T06:05:46Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-01T06:05:45Z"
--- dataset_info: features: - name: file_name dtype: string - name: text dtype: string - name: image dtype: image splits: - name: train num_bytes: 1678972.0 num_examples: 2 download_size: 1680896 dataset_size: 1678972.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
ADHIZ/image_sacdkdklda
ADHIZ
"2024-12-01T06:14:25Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-01T06:14:24Z"
--- dataset_info: features: - name: file_name dtype: string - name: text dtype: string - name: image dtype: image splits: - name: train num_bytes: 1678972.0 num_examples: 2 download_size: 1680896 dataset_size: 1678972.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
ADHIZ/vassu
ADHIZ
"2024-12-01T06:50:56Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-01T06:50:54Z"
--- 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-* ---
katiev2/nlp_coursework_dataset
katiev2
"2024-12-01T11:16:20Z"
32
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-01T11:16:19Z"
--- dataset_info: features: - name: review_body dtype: string - name: stars dtype: int64 splits: - name: train num_bytes: 4455609 num_examples: 3200 - name: test num_bytes: 563750 num_examples: 400 download_size: 3272139 dataset_size: 5019359 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
richmondsin/arc_ru_results
richmondsin
"2024-12-01T15:37:01Z"
32
0
[ "region:us" ]
null
"2024-12-01T15:36:50Z"
--- 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_ru_results\"\ ,\n\tname=\"google__gemma-2-2b__arc_ru\",\n\tsplit=\"latest\"\n)\n```\n\n## Latest\ \ results\n\nThese are the [latest results from run 2024-12-01T10-36-50.721258](https://huggingface.co/datasets/richmondsin/arc_ru_results/blob/main/google/gemma-2-2b/results_2024-12-01T10-36-50.721258.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_ru\": {\n \ \ \"alias\": \"arc_ru\",\n \"acc,none\": 0.3503584229390681,\n\ \ \"acc_stderr,none\": 0.014287483889322104,\n \"acc_norm,none\"\ : 0.3790322580645161,\n \"acc_norm_stderr,none\": 0.014528981564492822\n\ \ }\n },\n \"arc_ru\": {\n \"alias\": \"arc_ru\",\n \"\ acc,none\": 0.3503584229390681,\n \"acc_stderr,none\": 0.014287483889322104,\n\ \ \"acc_norm,none\": 0.3790322580645161,\n \"acc_norm_stderr,none\"\ : 0.014528981564492822\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_ru data_files: - split: 2024_12_01T10_36_50.721258 path: - '**/samples_arc_ru_2024-12-01T10-36-50.721258.jsonl' - split: latest path: - '**/samples_arc_ru_2024-12-01T10-36-50.721258.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_ru_results", name="google__gemma-2-2b__arc_ru", split="latest" ) ``` ## Latest results These are the [latest results from run 2024-12-01T10-36-50.721258](https://huggingface.co/datasets/richmondsin/arc_ru_results/blob/main/google/gemma-2-2b/results_2024-12-01T10-36-50.721258.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_ru": { "alias": "arc_ru", "acc,none": 0.3503584229390681, "acc_stderr,none": 0.014287483889322104, "acc_norm,none": 0.3790322580645161, "acc_norm_stderr,none": 0.014528981564492822 } }, "arc_ru": { "alias": "arc_ru", "acc,none": 0.3503584229390681, "acc_stderr,none": 0.014287483889322104, "acc_norm,none": 0.3790322580645161, "acc_norm_stderr,none": 0.014528981564492822 } } ``` ## 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]
akhooli/mmarco_111k_test_q
akhooli
"2024-12-01T15:58:37Z"
32
0
[ "license:mit", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-01T15:58:17Z"
--- license: mit dataset_info: features: - name: query_id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 7471028 num_examples: 111869 download_size: 4459702 dataset_size: 7471028 configs: - config_name: default data_files: - split: train path: data/train-* ---
amuvarma/100k-fac-with-audio-1dups
amuvarma
"2024-12-01T19:54:35Z"
32
0
[ "size_categories:100K<n<1M", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-01T18:28:43Z"
--- dataset_info: features: - name: transcript dtype: string - name: audio dtype: audio - name: facodec_0 sequence: int64 - name: facodec_1 sequence: int64 - name: facodec_2 sequence: int64 - name: facodec_3 sequence: int64 - name: facodec_4 sequence: int64 - name: facodec_5 sequence: int64 - name: spk_embs sequence: float64 splits: - name: train num_bytes: 12206296171.0 num_examples: 100000 download_size: 7598599582 dataset_size: 12206296171.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
sssssssshhhhhu/movielens_dpo_dataset_test
sssssssshhhhhu
"2024-12-01T21:24:52Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-01T20:58:26Z"
--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 57737 num_examples: 10 download_size: 47672 dataset_size: 57737 configs: - config_name: default data_files: - split: train path: data/train-* ---
urbushey/product_catalog_training_1
urbushey
"2024-12-01T23:00:50Z"
32
0
[ "license:apache-2.0", "region:us" ]
null
"2024-12-01T23:00:02Z"
--- license: apache-2.0 ---
juliadollis/stf_regex_ner_1_fuzzy_80
juliadollis
"2024-12-02T00:44:21Z"
32
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T00:44:10Z"
--- dataset_info: features: - name: inteiro_teor dtype: string - name: url_download dtype: string - name: dataDecisao dtype: timestamp[ns] - name: dataPublicacao dtype: timestamp[ns] - name: decisao dtype: string - name: descricaoClasse dtype: string - name: ementa dtype: string - name: id dtype: string - name: jurisprudenciaCitada dtype: string - name: ministroRelator dtype: string - name: nomeOrgaoJulgador dtype: string - name: numeroProcesso dtype: string - name: referenciasLegislativas sequence: string - name: siglaClasse dtype: string - name: tipoDeDecisao dtype: string - name: titulo dtype: string - name: acordaosSimilares sequence: string - name: partes_lista_texto dtype: string - name: temaProcs sequence: string - name: inteiro_teor_regex dtype: string - name: NER struct: - name: JURISPRUDENCIA sequence: string - name: LEGISLACAO sequence: string - name: LOCAL sequence: string - name: ORGANIZACAO sequence: string - name: PESSOA sequence: string - name: TEMPO sequence: string - name: desambiguacao list: - name: class dtype: string - name: count dtype: int64 - name: elements sequence: string - name: entity dtype: string splits: - name: train num_bytes: 122506511 num_examples: 1000 download_size: 33172345 dataset_size: 122506511 configs: - config_name: default data_files: - split: train path: data/train-* ---
ahmedheakl/lines_detection
ahmedheakl
"2024-12-02T01:28:09Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T01:09:20Z"
--- dataset_info: features: - name: image dtype: image - name: bbox sequence: float64 - name: page dtype: int64 - name: pdf_file dtype: string splits: - name: train num_bytes: 307490651.0 num_examples: 709 download_size: 16771823 dataset_size: 307490651.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
Angel-Marchev/marchev-synth-data
Angel-Marchev
"2025-02-27T12:55:27Z"
32
0
[ "license:mit", "size_categories:10K<n<100K", "format:csv", "modality:tabular", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "doi:10.57967/hf/3701", "region:us" ]
null
"2024-12-02T03:43:46Z"
--- license: mit --- # Synthesized Economic Agents Dataset The authors would like to extend their gratitude to the University of National and World Economy and the project NID NI 23/2023/V for funding this research, and for the prime administrative assistance in general. How to Cite: Marchev, V., Marchev JR, A., Haralampiev, K., Efremov, A., Markov, B., Lyubchev, D., Piryankova, M., Filipov, B., Masarliev, D., & Mitkov, V. (2024). Methodological Approaches for Multidimensional Personal Data Creation. Vanguard Scientific Instruments in Management, 20, 108-131. Retrieved from [https://vsim-journal.info/index.php?journal=vsim&page=article&op=view&path[]=544](https://vsim-journal.info/index.php?journal=vsim&page=article&op=view&path[]=544) BibTex Citation: ``` @article{ Marchev_Marchev JR_Haralampiev_Efremov_Markov_Lyubchev_Piryankova_Filipov_Masarliev_Mitkov_2024, title={Methodological Approaches for Multidimensional Personal Data Creation}, volume={20}, url={https://vsim-journal.info/index.php?journal=vsim&page=article&#38;op=view&path[]=544}, journal={Vanguard Scientific Instruments in Management}, author={Marchev, Vasil and Marchev JR, Angel and Haralampiev, Kaloyan and Efremov, Alexander and Markov, Boyan and Lyubchev, Dimitar and Piryankova, Milena and Filipov, Bogomil and Masarliev, Daniel and Mitkov, Valentin}, year={2024}, month={Dec.}, pages={108-131} } ``` In the era of big data, there is a notable scarcity of datasets containing inherently sensitive information. Such datasets include those falling under regulations like GDPR, the Banking Secrecy Act, European data protection legislation, and others. The current research explores the possibility of filling this gap by generating a multidimensional dataset integrating personal characteristics, demographic features, personal preferences, and more, applicable for conducting research in banking, financial markets, and other economic and financial domains. The nature of the data, its complexity, and legal frameworks necessitate alternative approaches to acquire, simulate, and synthesize the required quantity and quality of information. The aim is to accumulate a wide range of diverse personal characteristics to help build a comprehensive profile of a statistically significant number of individuals. Financially active individuals and their behavior in the financial-economic context are identified. The dataset thus compiled could be applied in a broad range of economic and social studies. 1. **PROCESS ESSENTIALS** 1.1. **Coverage** 1.1.1. **Geographical Coverage** The country data is based on the Bulgarian Census 2021 (for demographic data) and the National Statistical Institute, while the banking information is based on public information from the Bulgarian National Bank and the banking system. We use information obtained from the Ministry of Finance and the Ministry of Agriculture. Several independent studies and questionnaires have been conducted on the investment preferences of individuals, as well as external research on personality and temperament based on a previously conducted survey with more than 15,000 international participants (Tipatov, 2009). 1.1.2. **Temporal Coverage** The data simulation process involves a one-time generation of data based on a temporary snapshot of the variables under consideration. If the data needs to be updated, it is important to update the distributions. 1.1.3. **Demographic Coverage** The synthesized data represents the defined research subject as a Bulgarian individual, a non-professional investor, with limited investment experience, while at the same time having available funds for investment. 1.2 **Data source** 1.2.1. **Primary Data Sources** The data is simulated based on previously collected information and generated distributions. The distributions are derived based on the following approaches: **In the presence of data for forming distributions**, an assumption is made that there will be no change in the conditions when using this approach. Namely, the preservation of the earlier distributions for each factor for which we have sufficient data (NSI, 2021, Census 2021). **In the absence of sufficient data** – to prepare a relevant distribution, the information that is available for the specific variable is used, such as the average value of the data, minimum, maximum, weighted average, etc. After establishing the available information, a partial simulation is performed, based on the data we know and on a priori information. **Assumptions** – In cases where we do not have available information about the distributions of the variables under consideration plausible assumption is performed. A simulation is performed based on a priori knowledge about the variables under consideration, an analysis of the group to which the specific indicator belongs, etc. 1.2.2. **Data Providers** It is necessary to derive distributions based on publicly available information with a high accuracy level. The following sources were used: **National Statistical Institute** **(NSI)** – the primary state agency responsible for collecting and disseminating statistical data regarding Bulgaria's population, economy, and environment. **Census 2021** – provides detailed demographic information about Bulgaria's population. It included data on population size, distribution, age structure, education levels, and socio-economic characteristics. **Bulgarian National Bank (BNB)** – central bank of Bulgaria, overseeing monetary policy, financial stability, and the banking system. It provides crucial data on monetary aggregates, interest rates, exchange rates, and banking statistics. **Banking System in Bulgaria** – provide financial services to individuals and businesses while generating a wide set of information on lending practices, deposits, and financial transactions. **Financial Supervision Commission (FSC)** – responsible for regulating and supervising the non-banking financial sector in Bulgaria. This includes insurance companies, pension funds, and investment firms. **Ministry of Finance** – oversees the country's fiscal policy, public finance management, and budgetary processes. **Ministry of Agriculture and Food** – focuses on agricultural policies and rural development in Bulgaria. It collects data on agricultural production, land use, crop yields, and livestock statistics. **A priori information and assumptions** – refer to knowledge or assumptions made based on knowledge, experience, and preliminary data about the events under consideration and the environment in which they develop. 1.3 **Methodology** 1.3.1. **Data Collection Methods** Synthetic data refers to information that does not correspond to actual records but is generated algorithmically. This type of data is created through statistical models rather than being collected from real-world observations. Numerous methodologies exist for producing multidimensional synthetic datasets, and many scholars in the field of artificial intelligence have addressed this topic. From a methodological perspective, various scenarios underscore the need for a structured approach to generating multidimensional datasets. This section examines key situations where data generation is an essential component of the information analysis process, organized according to methodological principles. **Need for Requisite Variety** This concept highlights the necessity of having a diverse and comprehensive array of data inputs to enhance the effectiveness and accuracy of data analysis and modeling. A critical sub-step in this context is feature engineering. **Random Missing Data** In instances where data is missing at random, the process includes specific sub-steps such as Missing at Random (MAR) data imputation. **Missing Data for a Class** When output data is absent for a particular class, it becomes necessary to incorporate an additional module into the model. This module serves to balance the dataset. **Data generation** In certain situations, the unique characteristics of the data, along with its complexity and legal considerations, can hinder the ability to secure models with the requisite quantity and quality of information. 1.3.2. **Data Synthesis** The process of synthesizing a multidimensional array of synthetic data has several main phases: variable selection, distribution analysis, business logic extraction, and application of the data generated. The last phase is related to the validation of the newly obtained set of synthetic data. **Variable Selection** Identifying the key individual characteristics of financial service users requires a thorough analysis of various subsets of distinct features. Each carefully curated group of variables enriches our understanding, allowing for a more comprehensive and accurate profile of active users. **Demographic Characteristics** Understanding demographic characteristics is vital, as it sheds light on the primary factors that influence financial behavior across society. **Individual Characteristics** Individual characteristics, influenced by both innate and acquired qualities, play a significant role in shaping how individuals interact with the environment. **Socio-Economic Status** Personal characteristics serve as a window into an individual’s socioeconomic status. **Banking and Financial Characteristics** Banking and financial characteristics under consideration reveal individuals' behaviors within financial services markets. 1.4. **Data Processing** Each statistical distribution is defined by specific parameters that describe its characteristics, including shape, central tendency, variability, and skewness. | **N** | **Factor** | **Code** | **Variable type** | **Possible values** | **Derivation** | | --- | --- | --- | --- | --- | --- | | 1 | Gender | sex | Nominal | M; F | Simulation | | 2 | Age – completed years | age | Continuous | 20 - 85 | Correlation | | 3 | Level of education | lv_educ | Ordinal | Incomplete; Primary; Basic; Secondary; Higher | Simulation | | 4 | Employment status | empl_stat | Nominal | Employers; Self-employed; Employed in the private sector; Employed in the public sector; Unpaid family workers; Unemployed | Simulation | | 5 | Marital status | marit_stat | Nominal | Single; Married; Divorced; Widowed | Simulation | | 6 | Number of household members | house_memb | Interval | 1; 2; 3; 4; 5; 6; 7+ | Simulation | | 7 | Number of children under 18 years | chil_u_18_y | Interval | No children under 18; One child under 18; Two children under 18; Three children under 18; Four children under 18; Five children under 18; Six or more children under 18 | Simulation | | 8 | Nationality | nation | Nominal | Bulgaria; EU; Other | Simulation | | 9 | Religion | religion | Nominal | Protestant; Catholic; Orthodox; Muslim; Other; No religion; I do not identify myself | Simulation | | 10 | Profession – Industry | prof_ind | Nominal | Agriculture, forestry, and fisheries; Mining and processing industry; Utilities (electricity distribution and water supply); Construction; Trade, automobile, and motorcycle repair; Transportation, warehousing, and mail; Hospitality and restaurant services; Creation and distribution of information and creative products; Telecommunications; Financial and administrative activities; Public administration; Education and research; Human health and social work; Other activities | Simulation | | 11 | Professional status | prof_stat | Nominal | Management contract; Employment contract; Civil contract; Self-employed; Unemployed; Pensioner | Simulation | | 12 | Number of owned apartments/houses | count_house | Interval | 0; 1; 2+ | Simulation | | 13 | Land ownership | own_field | Binary | YES/NO | Simulation | | 14 | Cars per household | num_car_house | Interval | 0; 1; 2; 3+ | Simulation | | 15 | Education | edu | Nominal | Educational Sciences; Humanities; Social, Economic, and Legal Sciences; Natural Sciences, Mathematics, and Informatics; Technical Sciences; Agricultural Sciences and Veterinary Medicine; Health and Sports; Arts; Security and Defense | Simulation | | 16 | Temperament | temp | Nominal | Choleric; Phlegmatic; Sanguine; Melancholic | Simulation | | 17 | Individual risk preference | ind_risk | Continuous | 0 - 1 | Correlation | | 18 | Previous investment experience in years | invest_exp | Ordinal | 0; 1-5; 6-10; 11-15; 16-25 | Simulation | | 19 | Investment experience with shares | shares | Binary | YES/NO | Simulation | | 20 | Investment experience with bonds | corp_oblig | Binary | YES/NO | Simulation | | 21 | Investment experience with others | oth | Binary | YES/NO | Simulation | | 22 | Investment experience with investment funds | inv_fund | Binary | YES/NO | Simulation | | 23 | Investment experience with currencies | cash | Binary | YES/NO | Simulation | | 24 | Investment experience with cryptocurrencies | crypto | Binary | YES/NO | Simulation | | 25 | Investment experience with government securities | gov_bond | Binary | YES/NO | Simulation | | 26 | Investment experience with bank deposits | deposits | Binary | YES/NO | Simulation | | 27 | Income | income | Ordinal | Up to 6121; Up to 12001; Up to 27601; Up to 43201; Up to 58801; Up to 74401; Over 90001+ | Correlation | | 28 | Personal expenses | pers_exp | Ordinal | up to 4500; up to 5000; up to 5500; up to 6000 | Correlation | | 29 | Housing costs | house_exp | Ordinal | up to 500; up to 1500; up to 3000; up to 4000 | Correlation | | 30 | Taxes and insurance | taxes | Ordinal | up to 500; up to 1000; up to 2000; up to 2500 | Correlation | | 31 | Transport and communications | transp_telecom | Ordinal | up to 500; up to 1000; up to 1500; up to 2500 | Correlation | | 32 | Leisure and hobby | hobby | Ordinal | 0; up to 1500; up to 2000; up to 3000 | Correlation | | 33 | Preferred method of banking | banking | Nominal | Online/Offline | Simulation | | 34 | The average number of bank transactions | bk_oprat | Ordinal | Up to 7; From 8 to 10; From 11 to 13; From 14 to 18; From 19 to more | Simulation | | 35 | Debit card | bk_dc | Interval | Under one; One; Two; Three | Simulation | | 36 | Bank account | bk_acc | Binary | YES/NO | Simulation | Table 1: Data dictionary - full list of variables To estimate these parameters from a given sample, two statistical techniques are commonly employed: The method of moments and the Generalized method of moments (GMM). 1.4.1. **Method of Moments** In the method of moments, the sample moments are matched to the theoretical moments of the distribution. This approach involves solving a set of equations to derive the distribution's parameters. 1.4.2. **Generalized Method of Moments (GMM)** The GMM extends the method of moments by offering greater flexibility in selecting moment conditions. This technique is particularly useful when there are more moment conditions than parameters or when the moment conditions cannot be solved directly. 1.5. **Accessibility** The specificity, complexity, and regulatory framework surrounding the data present significant challenges in obtaining the necessary quantity and quality of information. The data needed is governed by European regulations such as the GDPR and the Bank Secrecy Act, among others. The alternative strategy for acquiring the required data. The approach involves generating a multivariate dataset that incorporates a variety of demographic, personal, individual, and banking variables. 1.6. **Data Format** The possibilities offered by our model are as follows: Excel, CSV, Pandas, Croissanr, Polars and Parquet. 1.7. **Quality Assurance** **Business Logic in Data Generation** The business logic applied in the data generation process is defined by identifying potential interdependent factors, their constraints, and the possible and impossible combinations of these factors. When combining distributions, there is a risk of producing unattainable or highly improbable values. Phases: Selection of Potential Interdependent Factors; Establishing Possible and Impossible Combinations; Elimination of Impossible Combinations; | **id** | **Independent feature** | **Independent feature value** | **Dependent feature** | **Dependent feature value filter** | **Note** | | --- | --- | --- | --- | --- | --- | | 1 | Marital status | Married | Number of household members | \>2 | The number of household members in family households is more likely to be greater than 2 | | 2 | Profession – Industry | Financial and administrative activities | Bank account | \>0 | They are more likely to own a bank account | | 3 | Age – completed years | <25 | Previous investment experience in years | 0 | Under 24s are less likely to have investment experience. Between 35-44 and 45-54 are more likely to have extensive investment experience | | 4 | Age – completed years | <21 | Level of education | <Higher | Under-21s are less likely to have a university degree | | 5 | Age – completed years | <25 | Number of children under 18 years | <2 | Given the defined demographic coverage, from 20-24, it is less likely to have more than 1 child under 18 | | 6 | Previous investment experience in years | \>0 | Investment experience with bank deposits | Y | They are more likely to own a bank account | | 7 | Investment in stocks | Y | Previous investment experience in years | \>0 | If investment in stocks = yes, then previous investment experience in years is >0. | | 8 | Investment in bonds | Y | Previous investment experience in years | \>0 | If investment in bonds = yes, then previous investment experience in years is >0. | | 9 | Other investments | Y | Previous investment experience in years | \>0 | If investment in other investments = yes, then previous investment experience in years is >0. | | 10 | Investment in a fund | Y | Previous investment experience in years | \>0 | If investment in funds = yes, then previous investment experience in years is >0. | | 11 | Currency investments | Y | Previous investment experience in years | \>0 | If investment in currency = yes, then previous investment experience in years is >0. | | 12 | Investing in cryptocurrencies | Y | Previous investment experience in years | \>0 | If investment in cryptocurrencies = yes, then previous investment experience in years is >0. | | 13 | Investment in government securities | Y | Previous investment experience in years | \>0 | If investment in government securities = yes, then previous investment experience in years is >0. | | 14 | Age – completed years | <25 | Bank account | N | Under 24s are less likely to have a checking account | | 15 | Age – completed years | <18 | Bank account | N | Under 18 is not possible to have a current account | | 16 | Level of education | Higher | Income | \>27601 | A higher level of education implies earnings in the upper range | | 17 | Number of children under 18 years | \>1 | Number of household members | \>3 | The number of household members is directly dependent on the number of children under 18 ages | | 18 | Income | \>27601 | Taxes and insurance | \>2500 | Earnings in the upper range correspond to higher taxes and insurance | Table 2: Sample of business logic 1.8. **Reliability** **Validation process**. Crucial step in the data generation process. **Data Analysis.** Examination of the synthesized data. **Data Validation.** Verifying that the generated dataset aligns with the original statistical distributions. **Adjacent Frequencies.** A smooth transition between these values is vital for model validation, as it helps avoid abrupt fluctuations that could indicate issues in the simulation. **Quality Assessment.** Evaluate the quality of the information obtained. 1.9. **Accuracy** The Kolmogorov-Smirnov (K-S) test is employed as the primary method for data validation. **One-Sample K-S Test** The one-sample K-S test compares the ECDF of a sample with the cumulative distribution function (CDF) of a theoretical distribution. The ECDF represents the proportion of observations in a sample that are less than or equal to a certain value, while the CDF indicates the theoretical probability of obtaining a random observation from that distribution that is also less than or equal to that value. **Two-Sample K-S Test** The two-sample K-S test evaluates whether there is a significant correspondence between two univariate probability distributions. The test statistic D for this test is defined as the maximum absolute difference between the two ECDFs. **Hypotheses** In both the one-sample and two-sample K-S tests, the null hypothesis (H0) posits that the sample(s) conform to the specified distribution (for one sample) or that both samples originate from the same distribution (for two samples). 1.10. **Update Frequency** The data should be obtained once. In case of a change in the general conditions for the main groups of variables considered, a re-generation of the data set can be envisaged. 1.11. **Contact Information** Corresponding author – Vasil Marchev, [vmarchev@unwe.bg](mailto:vmarchev@unwe.bg) 2. **METADATA FOR STATISTICAL FEAURE** Concerning the approach considered for simulating a multidimensional array of synthetic data, it is necessary to prepare a detailed description of each of the considered characteristics. The metadata provides essential context and documentation for statistical data. It encompasses structured information that describes the data, its processes, and methodologies, which aids in understanding, interpreting, and utilizing statistical information effectively. A complete list of the detailed variables contained in the generated dataset is available in Table 3. <table><thead><tr><th><p><a id="_Hlk190016016"></a><strong>Feature Name</strong></p></th><th><p><strong>Description</strong></p></th><th><p><strong>Calc. method Formula</strong></p></th><th><p><strong>Calculation Method</strong></p><p><strong>Data Sources</strong></p></th><th><p><strong>Unit of Measure</strong></p></th><th><p><strong>Relevance</strong></p></th><th><p><strong>Sampling Error</strong></p></th><th><p><strong>Non-sampling Error</strong></p></th><th><p><strong>Geo.Disaggregation</strong></p></th><th><p><strong>Temporal Disaggregation</strong></p></th><th><p><strong>Comparability – Time</strong></p></th><th><p><strong>Comparability Regions</strong></p></th></tr></thead><tbody><tr><td><p><strong>Sex/Gender</strong></p></td><td><p>Shows gender identity</p></td><td><p>Synthesized variable*</p></td><td><p>The distribution is derived from the Census 2021 in Bulgaria</p></td><td><p><strong>Nominal:</strong></p><p>M/F</p></td><td><p>The aim is to set up possible correlations between sex/gender &amp; other individual characteristics.</p></td><td><p>Official data – Census 2021</p></td><td><p>Potential errors include mis recording</p></td><td><p>Data for Bulgaria</p></td><td><p>This data is static and reflects the Sex/Gender at the time of the 2021 Census.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Age</strong></p></td><td><p>Stands for the number of years.</p></td><td><p>Synthesized variable</p></td><td><p>The distribution is derived from the last national Census in Bulgaria conducted in 2021</p></td><td><p><strong>Continuous:</strong></p><p>20 - 85</p></td><td><p>Age is a fundamental demographic factor essential for analyzing various social dynamics.</p></td><td><p>Official data – Census 2021</p></td><td><p>Potential errors include mis recording or incorrect date of birth in administrative records.</p></td><td><p>Data for</p><p>Bulgaria</p></td><td><p>The data is static and reflects the population's age as of the census date (2021)</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Level of Education</strong></p></td><td><p>Completed level of education.</p></td><td><p>Synthesized variable</p></td><td><p>The distribution is derived from the Census 2021 in Bulgaria</p></td><td><p><strong>Ordinal:</strong></p><p>-Incomplete primary</p><p>-Primary school</p><p>-Secondary school</p><p>-College degree</p><p>-University degree</p></td><td><p>Education level is a key demographic characteristic used to analyze individual and community outcomes.</p></td><td><p>Official data – Census 2021.</p></td><td><p>Potential errors could stem from incorrect self-reporting or classification during data collection.</p></td><td><p>Data for Bulgaria</p></td><td><p>This data is static and reflects the education levels as reported during the 2021 Census.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Employment Status</strong></p></td><td><p>Indicates the current labor force participation of an individual.</p></td><td><p>Synthesized variable</p></td><td><p>The distribution is derived from the Census 2021 in Bulgaria &amp; labor force survey</p></td><td><p><strong>Nominal:</strong></p><p>-Employers</p><p>-Self-employed</p><p>-Employees in private enterprises</p><p>-Employees in public enterprises</p><p>-Unpaid family workers</p><p>-Unemployed</p></td><td><p>Employment status is a critical demographic characteristic used to evaluate labor market dynamics.</p></td><td><p>Official data – Census 2021.</p></td><td><p>Errors may arise from misclassification or non-response.</p></td><td><p>Data for Bulgaria</p></td><td><p>This data is static and reflects the employment status during the reference period of the 2021 Census.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Marital Status</strong></p></td><td><p>Stands for an individual's legal relationship status.</p></td><td><p>Synthesized variable</p></td><td><p>The distribution is derived from Census 2021 Bulgaria</p></td><td><p><strong>Nominal:</strong></p><p>-Single</p><p>-Married</p><p>-Divorced</p><p>-Widower</p></td><td><p>Demographic characteristics for understanding household composition, and social dynamics.</p></td><td><p>Official data – Census 2021.</p></td><td><p>Potential errors may arise from misreporting.</p></td><td><p>Data for Bulgaria</p></td><td><p>This data is static and reflects marital status at the time of the 2021 Census.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Number of Household Members</strong></p></td><td><p>Stands for the total number of individuals residing in a household.</p></td><td><p>Synthesized variable</p></td><td><p>The distribution is derived from the Census 2021 in Bulgaria.</p></td><td><p><strong>Interval:</strong></p><p>1; 2; 3; 4; 5+</p></td><td><p>Used to analyze living arrangements, household size trends, &amp; socioeconomic forecasting</p></td><td><p>Official data – Census 2021.</p></td><td><p>Potential errors include misreporting household composition.</p></td><td><p>Data for</p><p>Bulgaria</p></td><td><p>This data is static and reflects the number of household members at the time of the 2021 Census.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Number of Children Under 18</strong></p></td><td><p>Stands for the total number of individuals below 18 years.</p></td><td><p>Synthesized variable</p></td><td><p>The distribution is derived from the Census 2021 in Bulgaria, with data collected from household questionnaires.</p></td><td><p><strong>Interval:</strong></p><p>1; 2; 3; 4+</p></td><td><p>The number of children under 18 assesses dependency ratios, education structure, and understanding of family structures.</p></td><td><p>Official data – Census 2021.</p></td><td><p>Potential errors include misclassification of age or omission of household members.</p></td><td><p>Data for Bulgaria</p></td><td><p>This data is static and reflects the number of children under 18 at the time of the 2021 Census.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Nationality</strong></p></td><td><p>Indicates the legal or self-identified national affiliation of an individual.</p></td><td><p>Synthesized variable</p></td><td><p>The distribution is derived from the Census 2021 in Bulgaria, with data collected from household questionnaires.</p></td><td><p><strong>Nominal**</strong></p><p>From Census 2021</p></td><td><p>A key demographic characteristic for analyzing population diversity, cultural composition, and community integration.</p></td><td><p>Official data – Census 2021.</p></td><td><p>Potential errors include reluctance to show, or data entry mistakes.</p></td><td><p>Data for Bulgaria</p></td><td><p>This data is static and reflects the self-identified nationality at the time of the 2021 Census.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Religion</strong></p></td><td><p>Stands for an individual’s religious affiliation, belief system, or self-identified lack thereof.</p></td><td><p>Synthesized variable</p></td><td><p>The distribution is derived from the Census 2021 in Bulgaria</p></td><td><p><strong>Nominal**</strong></p><p>From Census 2021</p></td><td><p>Demographic factor for understanding cultural diversity, social dynamics &amp; its influence on traditions, and community engagement.</p></td><td><p>Official data – Census 2021.</p></td><td><p>Potential errors include reluctance to disclose, or data entry mistakes.</p></td><td><p>Data for Bulgaria</p></td><td><p>This data is static and reflects religious affiliation as self-identified during the 2021 Census.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Profession/Industry</strong></p></td><td><p>Stands for the type of occupation or industry in which an individual is employed.</p></td><td><p>Synthesized variable</p></td><td><p>The distribution is based on information from the National Statistical Institute.</p></td><td><p><strong>Nominal**:</strong></p><p>From NSI</p></td><td><p>The profession/industry - important demographic factor for understanding employment trends, economic structure, and the distribution of labor across various sectors.</p></td><td><p>Official data – NSI.</p></td><td><p>Errors may occur if individuals provide inaccurate responses.</p></td><td><p>Data for Bulgaria</p></td><td><p>This data is static and reflects the profession/industry status during the reference period of the 2021 Census.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Professional status</strong></p></td><td><p>Stands for the type of employment of an individual, categorized based on their role in the labor market.</p></td><td><p>Synthesized variable</p></td><td><p>The distribution is based on information from the National Statistical Institute.</p></td><td><p><strong>Nominal**:</strong></p><p>From Infostat</p></td><td><p>Professional status provides information about socio-economic position, its role in the labor market, employment trends &amp; economic inequalities.</p></td><td><p>Official data – NSI</p></td><td><p>Errors may occur due to incorrect completion of surveys or errors in data entry or classification.</p></td><td><p>Data for Bulgaria</p></td><td><p>These data are static and reflect socioeconomic status during the reference period of the 2021 Census.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Apartment/house numbers</strong></p></td><td><p>Stands for the number of residential units (apartments or houses) owned by a household.</p></td><td><p>Synthesized variable</p></td><td><p>The distribution is based on the 2021 Census in Bulgaria, administrative and statistical reports.</p></td><td><p><strong>Interval:</strong></p><p>0; 1; 2+</p></td><td><p>Provides information on access to housing and conditions. Helps analyze the distribution of housing resources and living standards in different social groups.</p></td><td><p>Official data – Census 2021</p></td><td><p>Errors may occur if individuals provide inaccurate responses.</p></td><td><p>Data for Bulgaria</p></td><td><p>These data are static and reflect the number of apartments/houses during the 2021 census reference period.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Plots of Land</strong></p></td><td><p>Stands for agricultural land owned by an individual highlighting ownership percentage.</p></td><td><p>Synthesized variable</p></td><td><p>The distribution is derived from the land registry &amp; data from the Ministry of Agriculture &amp; Assumptions.</p></td><td><p><strong>Binary:</strong></p><p>Yes/No</p></td><td><p>Provides information on land access, ownership, and the distribution of agricultural resources across the regions and social groups.</p></td><td><p>Assumption discrepancies are possible</p></td><td><p>Errors can occur due to registration errors, inaccuracies in data entry, or missing information.</p></td><td><p>Data for Bulgaria</p></td><td><p>These data are static and reflect the number of land plots during the 2021 census reference period.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Household car</strong></p></td><td><p>Stands for the number of cars owned by a household.</p></td><td><p>Synthesized variable</p></td><td><p>The distribution is derived from the 2021 Census in Bulgaria.</p></td><td><p><strong>Interval:</strong></p><p>0; 1; 2; 3+</p></td><td><p>The number of cars helps analyze mobility and living conditions. Also revealing socio-economic differences between households.</p></td><td><p>Official data – Census 2021</p></td><td><p>Error includes inaccuracies in self-reporting, misunderstanding of questions, or missing data.</p></td><td><p>Data for Bulgaria</p></td><td><p>These data are static and reflect the household car during the 2021 census reference period</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Education</strong></p></td><td><p>Education shows the distribution of individuals across different fields of study.</p></td><td><p>Synthesized variable</p></td><td><p>The distribution is based on information from the National Statistical Institute.</p></td><td><p><strong>Nominal**:</strong></p><p>From Infostat</p></td><td><p>Reveals trends in the educational structure of the population, highlighting differences in access to educational resources and opportunities for professional development.</p></td><td><p>Official data – NSI</p></td><td><p>Misreporting educational levels, non-response bias, data processing mistakes, and inaccuracies in classifying education levels</p></td><td><p>Data for Bulgaria</p></td><td><p>These data static and reflect the Education during the 2021 census reference period.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Temperament</strong></p></td><td><p>Temperament reflects the distribution of individuals across different personality traits.</p></td><td><p>Synthesized variable</p></td><td><p>The distribution is derived from an international survey with more than 15k respondents.</p></td><td><p><strong>Nominal:</strong></p><ul><li>Choleric</li><li>Phlegmatic</li><li>Sanguine</li><li>Melancholic</li></ul></td><td><p>Provides information about personality traits and behavioral patterns, highlighting their impact on social interactions.</p></td><td><p>Minimal possibility in the data from the study</p></td><td><p>Errors in temperament may include biases in self-assessment, or subjectivity.</p></td><td><p>Data for Bulgaria</p></td><td><p>Periodic data updates are needed over a relatively long period (&gt;5 years)</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Individual risk</strong></p></td><td><p>Reflects the distribution of individuals based on their willingness to take investment risks.</p></td><td><p>Synthesized variable</p></td><td><p>The distribution is derived from an internal survey with more than 900 respondents.</p></td><td><p><strong>Continuous:</strong></p><p>0 - 1</p></td><td><p>Individual risk preferences reflect decision-making under uncertainty, highlighting individuals' behavior in financial markets.</p></td><td><p>Safe environment. Difficulties in assessing behavior in a real situation.</p></td><td><p>May include inaccurate self-reporting. Safe environment. Difficulties in assessing behavior in a real situation.</p></td><td><p>Data for Bulgaria</p></td><td><p>Ongoing research. Stable results. No sharp fluctuations are expected.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Investment exp</strong></p></td><td><p>Previous investment experience, measured in years, reflects an individual’s history in investment, and financial decision-making.</p></td><td><p>Synthesized variable</p></td><td><p>A plausible assumption and a priori simulation</p></td><td><p><strong>Ordinal*:</strong></p><p>0; 1-5; 6-10; 11-15; 16-25</p><p>*Interval variable converted into Ordinal</p></td><td><p>Reflects decision-making in investments, highlighting financial behavior and its impact on economic choices.</p></td><td><p>Minimal in the data from the study</p></td><td><p>Errors could include misinterpretation of financial terms, subjective reporting, or inaccurate self-assessment.</p></td><td><p>Data for Bulgaria</p></td><td><p>Periodic data updates are needed over a relatively long period (&gt;5 years)</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Shares</strong></p></td><td><p>Shows the distribution of individuals who invest in shares.</p></td><td><p>Synthesized variable</p></td><td><p>The distribution is derived from publicly available data from the BNB.</p></td><td><p><strong>Binary:</strong></p><p>Yes/No</p></td><td><p>The shares segment reflects the presence or absence of investments in shares, highlighting the financial behavior of investors.</p></td><td><p>Official data from BNB</p></td><td><p>Errors for the shares segment may occur due to inaccurate self-reporting.</p></td><td><p>Data for Bulgaria</p></td><td><p>Periodic data updates are needed. No more often than once a year.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Obligations</strong></p></td><td><p>Shows the distribution of individuals who invest in Obligations.</p></td><td><p>Synthesized variable</p></td><td><p>The distribution is derived from publicly available data from the BNB.</p></td><td><p><strong>Binary:</strong></p><p>Yes/No</p></td><td><p>Reflects the investments in obligations, highlighting the financial behavior of investors</p></td><td><p>Official data from BNB</p></td><td><p>Potential inaccuracies may stem from misreporting or incomplete representation.</p></td><td><p>Data for Bulgaria</p></td><td><p>Periodic data updates are needed. No more often than once a year.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Others</strong></p></td><td><p>Shows the distribution of individuals who invest in other investment instruments</p></td><td><p>Synthesized variable</p></td><td><p>The distribution is derived from publicly available data from the BNB.</p></td><td><p><strong>Binary:</strong></p><p>Yes/No</p></td><td><p>The other investments reflect the presence or absence of investments in other investment instruments.</p></td><td><p>Official data from BNB</p></td><td><p>Potential inaccuracies may stem from misreporting or incomplete representation.</p></td><td><p>Data for Bulgaria</p></td><td><p>Periodic data updates are needed. No more often than once a year.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Investment funds</strong></p></td><td><p>It shows the distribution of investors who have investment experience with currencies</p></td><td><p>Synthesized variable</p></td><td><p>The distribution is derived from publicly available data from the BNB.</p></td><td><p><strong>Binary:</strong></p><p>Yes/No</p></td><td><p>Provides information for assessing individual investment strategies, wealth accumulation, and financial risk exposure</p></td><td><p>Official data from BNB</p></td><td><p>Inaccuracies can arise from incorrect classification.</p></td><td><p>Data for Bulgaria</p></td><td><p>Periodic data updates are needed. No more often than once a year.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Cash</strong></p></td><td><p>It shows the distribution of investors who have investment experience with currencies.</p></td><td><p>Synthesized variable</p></td><td><p>The distribution is derived from publicly available data from the BNB.</p></td><td><p><strong>Binary:</strong></p><p>Yes/No</p></td><td><p>Investing in currency provides information about portfolio diversification &amp; knowledge of the forex markets.</p></td><td><p>Official data from BNB</p></td><td><p>Inaccuracies may occur from the misclassification of currency investors</p></td><td><p>Data for Bulgaria</p></td><td><p>Periodic data updates are needed. No more often than once a year.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Cryptocurrency</strong></p></td><td><p>Provides information for investors who have experience with cryptocurrencies.</p></td><td><p>Synthesized variable</p></td><td><p>The distribution is derived from publicly available data from the BNB.</p></td><td><p><strong>Binary:</strong></p><p>Yes/No</p></td><td><p>Indicator of an individual’s involvement in the digital asset market. It also reflects broader trends in the adoption of decentralized finance.</p></td><td><p>Official data from BNB</p></td><td><p>Inaccuracies may arise from misreporting, lack of visibility into private cryptocurrency wallets.</p></td><td><p>Data for Bulgaria</p></td><td><p>Periodic data updates are needed. No more often than once a year.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Gov</strong> <strong>bonds</strong></p></td><td><p>Provides information on whether the investors under consideration have experience with investments in government bonds.</p></td><td><p>Synthesized variable</p></td><td><p>The distribution is derived from publicly available data from the BNB.</p></td><td><p><strong>Binary:</strong></p><p>Yes/No</p></td><td><p>Indicator of individual investment behavior in low-risk, stable financial instruments. They provide insight into financial strategies and trust in government bonds.</p></td><td><p>Official data from BNB</p></td><td><p>Inaccuracies may occur due to misreporting.</p></td><td><p>Data for Bulgaria</p></td><td><p>Periodic data updates are needed. No more often than once a year.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Deposits</strong></p></td><td><p>Provides information on whether the investors under consideration have experience with investments in bank deposits.</p></td><td><p>Synthesized variable</p></td><td><p>The distribution is derived from publicly available data from the BNB.</p></td><td><p><strong>Binary:</strong></p><p>Yes/No</p></td><td><p>Provides insight into the financial habits of individuals, the penetration of banking products, and consumer trust in the bank system.</p></td><td><p>Official data from BNB</p></td><td><p>Inaccuracies could result from misreporting.</p></td><td><p>Data for Bulgaria</p></td><td><p>Periodic data updates are needed. No more often than once a year.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Income</strong></p></td><td><p>Stands for the distribution of income across different income brackets within a population. The data provides information about the corresponding percentage of individuals falling within each one.</p></td><td><p>Synthesized variable</p></td><td><p>The distribution of personal income is based on data collected through a survey of 491 people.</p></td><td><p><strong>Ordinal*:</strong></p><p>up to 19 200</p><p>19 201 - 27 600</p><p>27 601 - 54 000</p><p>54 001 - 82 800</p><p>from 82&nbsp;801</p><p>*Interval variable converted into Ordinal</p></td><td><p>Income distribution is critical for understanding economic inequality, social stratification, and wealth concentration within a population.</p></td><td><p>Possible discrepancies in data if the survey sample does not fully stand for the population.</p></td><td><p>Distortion may occur if data is filled in incorrectly</p></td><td><p>Data for Bulgaria</p></td><td><p>Dynamic variable. Data updates are needed once per year.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Personal exp</strong></p></td><td><p>Stands for the total personal expenses of an individual. This data helps to understand the spending behavior of individuals across different income groups.</p></td><td><p>Synthesized</p><p>variable</p></td><td><p>The distribution of expenses is derived from a priori knowledge and assumptions about the types of household expenses.</p></td><td><p><strong>Ordinal*:</strong></p><p>Group 1</p><p>Group 2</p><p>Group 3</p><p>Group 4</p><p>*Interval variable converted into Ordinal</p></td><td><p>Indicator for assessing financial health, economic behavior, and consumption patterns across various demographics. It helps to find potential areas for improvement in savings behavior.</p></td><td><p>Possible discrepancies in assumptions if the environment changes. Example – inflation.</p></td><td><p>Potential issues include inconsistent classifications of expenditures or biases in categorization.</p></td><td><p>Data for Bulgaria</p></td><td><p>Dynamic variable. Data updates are needed once per year.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>House exp</strong></p></td><td><p>It refers to the total expenditure incurred by an individual on housing-related expenses, including rent, mortgage payments, utilities, and maintenance. These expenses are fundamental to understanding financial stability &amp; behavioral patterns.</p></td><td><p>Synthesized variable</p></td><td><p>The distribution of expenses is derived from a priori knowledge and assumptions about the types of household expenses.</p></td><td><p><strong>Ordinal*:</strong></p><p>Group 1</p><p>Group 2</p><p>Group 3</p><p>Group 4</p><p>*Interval variable converted into Ordinal</p></td><td><p>Measure of financial well-being, assessing the affordability of housing in different economic contexts. It helps find how much of a household's income is dedicated to housing.</p></td><td><p>Possible discrepancies in assumptions if the environment changes. Example - inflation.</p></td><td><p>Inconsistencies in the definition or categorization may occur.</p></td><td><p>Data for Bulgaria</p></td><td><p>Dynamic variable. Data updates are needed once per year.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Taxes</strong></p></td><td><p>Stands for the total taxes and social security contributions paid by an individual, including income taxes, social security, pension contributions, health insurance, and other mandatory payments.</p></td><td><p>Synthesized variable</p></td><td><p>The distribution of expenses is derived from a priori knowledge and assumptions about the types of household expenses.</p></td><td><p><strong>Ordinal*:</strong></p><p>Group 1</p><p>Group 2</p><p>Group 3</p><p>Group 4</p><p>*Interval variable converted into Ordinal</p></td><td><p>Tax and social security contributions are important for assessing individual financial obligations and understanding the impact of taxation on disposable income.</p></td><td><p>Possible discrepancies in assumptions if the environment changes. Example - inflation.</p></td><td><p>Inconsistencies in the definition or categorization may occur.</p></td><td><p>Data for Bulgaria</p></td><td><p>Dynamic variable. Data updates are needed once per year.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Transportation &amp; telecom</strong></p></td><td><p>Stands for the total expenditure an individual spends on transportation (e.g., public transport, car expenses, taxis) and communication services (e.g., mobile phone bills, internet, postal services).</p></td><td><p>Synthesized variable</p></td><td><p>The distribution of expenses is derived from a priori knowledge and assumptions about the types of household expenses.</p></td><td><p><strong>Ordinal*:</strong></p><p>Group 1</p><p>Group 2</p><p>Group 3</p><p>Group 4</p><p>*Interval variable converted into Ordinal</p></td><td><p>Essential to understanding individuals' mobility patterns and their access to communication. These costs highlight differences in financial capabilities between the groups.</p></td><td><p>Possible discrepancies in assumptions if the environment changes. Example - inflation.</p></td><td><p>Inconsistencies in the definition or categorization may occur.</p></td><td><p>Data for Bulgaria</p></td><td><p>Dynamic variable. Data updates are needed once per year.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Hobby exp</strong></p></td><td><p>Stands for the total expenditure an individual allocates towards leisure activities, hobbies, and entertainment. This feature provides insight into an individual's lifestyle, and priorities.</p></td><td><p>Synthesized variable</p></td><td><p>The distribution of expenses is derived from a priori knowledge and assumptions about the types of household expenses.</p></td><td><p><strong>Ordinal*:</strong></p><p>Group 1</p><p>Group 2</p><p>Group 3</p><p>Group 4</p><p>*Interval variable converted into Ordinal</p></td><td><p>Hobby expenses are important for understanding an individual's discretionary income and lifestyle preferences. An indicator of economic well-being.</p></td><td><p>Possible discrepancies in assumptions if the environment changes. Example - inflation.</p></td><td><p>Inconsistencies in the definition or categorization may occur.</p></td><td><p>Data for Bulgaria</p></td><td><p>Dynamic variable. Data updates are needed once per year.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Preferred method of banking</strong></p></td><td><p>Stands for the preferred mode of banking for individuals, whether they prefer online banking or onside banking. This feature provides insights into digital adoption trends and regional or demographic differences in banking behavior.</p></td><td><p>Synthesized variable</p></td><td><p>The distribution is derived from NSI data.</p></td><td><p><strong>Nominal:</strong></p><p>Online/Offline</p></td><td><p>This feature is crucial for understanding consumer behavior and guiding decisions on resource allocation, as well as describing the level of trust in digital payment systems.</p></td><td><p>Official data from NSI</p></td><td><p>Discrepancies may occur if there are individuals with regular banking both online and offline.</p></td><td><p>Data for Bulgaria</p></td><td><p>Data updates are needed once per year.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Avg num banking</strong></p></td><td><p>Provides information about the active banking user by analyzing the average number of banking transactions performed by an individual in a month. This includes deposits, withdrawals, transfers, bill payments, etc.</p></td><td><p>Synthesized variable</p></td><td><p>A priori simulated distribution</p></td><td><p><strong>Ordinal:</strong></p><p>up to 10</p><p>11 - 14</p><p>15 - 20</p><p>21 - 26</p><p>from 27</p></td><td><p>The frequency of banking transactions is critical for financial institutions to evaluate the activity of the customers. This data can also assist in determining the most commonly used bank services, and in the customer segmentation process.</p></td><td><p>Possible discrepancies in assumptions. Possibility of bias in the sample (focusing only on a specific group of customers).</p></td><td><p>Potential errors include incorrect categorization of transactions or missed transactions that occurred on platforms outside the bank's recorded systems.</p></td><td><p>Data for Bulgaria</p></td><td><p>Data updates are needed once per year.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Debit card</strong></p></td><td><p>It stands for the percentage of people who have one or more debit cards. The function provides information about the penetration of banking services among the population.</p></td><td><p>Synthesized variable</p></td><td><p>A priori simulated distribution</p></td><td><p><strong>Interval:</strong></p><p>0; 1; 2; 3</p></td><td><p>The number of debit cards owned is important for understanding customer behavior. Customers who own multiple debit cards may have different banking needs, such as separate cards for personal and business use, or for different spending categories.</p></td><td><p>Possible discrepancies in assumptions.</p></td><td><p>Potential errors include misreporting or lack of clarity.</p></td><td><p>Data for Bulgaria</p></td><td><p>Data updates are needed every three/five year.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr><tr><td><p><strong>Bank acc</strong></p></td><td><p>Stands for the percentage of individuals who own a bank account in Bulgarian Lev (BGN). This feature helps to understand the penetration of basic banking services across different customer segments, particularly about the presence of an account.</p></td><td><p>Synthesized variable</p></td><td><p>The distribution is derived from publicly available information about the banking system.</p></td><td><p><strong>Binary:</strong></p><p>Yes/No</p></td><td><p>Ownership of a bank account is the most important indicator of the economically active client. A bank account is a fundamental tool for managing finances and engaging with the broader economy, making this feature critical for understanding financial habits.</p></td><td><p>Possible discrepancies in data if the survey sample does not fully stand for the population.</p></td><td><p>Misunderstanding the types of accounts or inaccurately reporting ownership.</p></td><td><p>Data for Bulgaria</p></td><td><p>Data updates are needed every three/five year.</p></td><td><p>Not applicable</p></td><td><p>Not applicable</p></td></tr></tbody></table> Table 3: Full list with described variables **\*** Synthesized through a simulation method based on distributions and business logic **\*\*** Nominal variables with detailed possible values are presented in Table 4 | **Feature Name** | **Type** | **Data source** | **Possible values** | | --- | --- | --- | --- | | **Gender** | Nominal | From Census 2021 | \- M<br><br>\- F | | **Employment status** | Nominal | From Census 2021<br><br>& labor force survey | \-Employers<br><br>\-Self-employed<br><br>\-Employees in private enterprises<br><br>\-Employees in public enterprises<br><br>\-Unpaid family workers<br><br>\-Unemployed | | **Marital status** | Nominal | From Census 2021 | \- Single<br><br>\- Married<br><br>\- Divorced<br><br>\- Widower | | **Nationality** | Nominal | From Census 2021 | \- Bulgarian <br>\- European Union <br>\- Other | | **Religion** | Nominal | From Census 2021 | \- Orthodox <br>\- Protestant <br>\- Catholic <br>\- Muslim <br>\- Other <br>\- No religion <br>\- I don't want to answer | | **Profession/Industry** | Nominal | From NSI | \- Agriculture, forestry and fishing <br>\- Mining, quarrying & Manufacturing <br>\- Electricity, gas, steam and air conditioning supply. Water supply, sewerage, waste management and remediation activities <br>\- Construction <br>\- Wholesale and retail trade; repair of motor vehicles and motorcycles <br>\- Transportation and storage <br>\- Accommodation and food service activities <br>\- Information and communication <br>\- Financial and insurance activities. Real estate activities <br>\- Education, professional, scientific and technical activities. <br>\- Administrative and support service activities. Public administration and defense; compulsory social security <br>\- Human health and social work activities <br>\- Arts, entertainment and recreation. Other service activities | | **Professional status** | Nominal | From NSI | \- Management contract <br>\- Employment contract <br>\- Civil contract <br>\- Self-employed person <br>\- Unemployed <br>\- Pensioner | | **Owner of a house** | Nominal | From Census 2021 & NSI | \- Yes<br><br>\- No | | **Education** | Nominal | From NSI | \- Educational Sciences<br><br>\- Humanities<br><br>\- Social, Economic, and Legal Sciences<br><br>\- Natural Sciences, Mathematics, and Informatics<br><br>\- Technical Sciences<br><br>\- Agricultural Sciences and Veterinary Medicine<br><br>\- Health and Sports<br><br>\- Arts<br><br>\- Security and Defense | | **Temperament** | Nominal | International survey (Tipatov, 2009) | \- Choleric<br><br>\- Phlegmatic<br><br>\- Sanguine<br><br>\- Melancholic | | **Preferred method of banking** | Nominal | From NSI | \- Online<br><br>\- Offline | Table: 4 Nominal variables with detailed possible values
chiyuanhsiao/v14
chiyuanhsiao
"2024-12-02T17:04:20Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T05:50:25Z"
--- dataset_info: features: - name: id dtype: string - name: question dtype: string - name: question_speech dtype: audio - name: question_unit sequence: int64 - name: instruction dtype: string - name: response_text dtype: string - name: response_speech dtype: audio splits: - name: train num_bytes: 5435743.0 num_examples: 32 download_size: 5381302 dataset_size: 5435743.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
amuvarma/2m-fac-raw-1dups
amuvarma
"2024-12-02T07:25:47Z"
32
0
[ "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T06:40:45Z"
--- dataset_info: features: - name: transcript dtype: string - name: facodec_0 sequence: int64 - name: facodec_1 sequence: int64 - name: facodec_2 sequence: int64 - name: facodec_3 sequence: int64 - name: facodec_4 sequence: int64 - name: facodec_5 sequence: int64 splits: - name: train num_bytes: 99196504511 num_examples: 2282634 download_size: 15870388094 dataset_size: 99196504511 configs: - config_name: default data_files: - split: train path: data/train-* ---
ryusangwon/deprecated_nq_wiki_top20
ryusangwon
"2024-12-02T07:43:21Z"
32
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T07:42:32Z"
--- 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
"2024-12-02T08:56:31Z"
32
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T08:56:27Z"
--- 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-* ---
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_64_16_0.05_64_BestF1
ferrazzipietro
"2024-12-02T09:08:34Z"
32
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T09:08:31Z"
--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 881564 num_examples: 604 - name: test num_bytes: 6527783 num_examples: 4020 download_size: 1677053 dataset_size: 7409347 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_64_32_0.05_64_BestF1
ferrazzipietro
"2024-12-02T09:12:26Z"
32
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T09:12:23Z"
--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 881564 num_examples: 604 - name: test num_bytes: 6527783 num_examples: 4020 download_size: 1676494 dataset_size: 7409347 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_64_32_0.01_64_BestF1
ferrazzipietro
"2024-12-02T09:29:39Z"
32
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T09:29:36Z"
--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 881564 num_examples: 604 - name: test num_bytes: 6527783 num_examples: 4020 download_size: 1677168 dataset_size: 7409347 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_bec52a64-6903-45fa-af37-03f734a82077
argilla-internal-testing
"2024-12-02T11:10:09Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T11:10:08Z"
--- 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-* ---
NinaCalvi/ultra-rm-truthfulness-1000-ArmoRM_average
NinaCalvi
"2024-12-02T12:33:12Z"
32
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T12:33:10Z"
--- dataset_info: features: - name: instruction dtype: string - name: assistant_response dtype: string - name: judgement_for_assembly dtype: float64 - name: messages list: - name: content dtype: string - name: role dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 11061580 num_examples: 3000 download_size: 6185481 dataset_size: 11061580 configs: - config_name: default data_files: - split: train path: data/train-* ---
qinchihongye/test1111
qinchihongye
"2024-12-02T14:17:09Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T14:17:02Z"
--- dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: input_ids sequence: 'null' - name: attention_mask sequence: 'null' - name: labels sequence: 'null' splits: - name: train num_bytes: 711 num_examples: 1 - name: test num_bytes: 388 num_examples: 1 download_size: 10176 dataset_size: 1099 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-sk-unrevised_NoQuant_64_16_0.01_64_BestF1_sk
ferrazzipietro
"2024-12-02T17:57:18Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T17:57:16Z"
--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 148050 num_examples: 102 - name: test num_bytes: 1034730 num_examples: 653 download_size: 248592 dataset_size: 1182780 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-sk-unrevised_NoQuant_64_16_0.05_64_BestF1_sk
ferrazzipietro
"2024-12-02T17:57:40Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T17:57:38Z"
--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 148050 num_examples: 102 - name: test num_bytes: 1034730 num_examples: 653 download_size: 248356 dataset_size: 1182780 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-sk-unrevised_NoQuant_32_16_0.05_64_BestF1_sk
ferrazzipietro
"2024-12-02T17:59:36Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T17:59:33Z"
--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 148050 num_examples: 102 - name: test num_bytes: 1034730 num_examples: 653 download_size: 248437 dataset_size: 1182780 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-sk-unrevised_NoQuant_16_32_0.01_64_BestF1_sk
ferrazzipietro
"2024-12-02T17:59:58Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T17:59:55Z"
--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 148050 num_examples: 102 - name: test num_bytes: 1034730 num_examples: 653 download_size: 248164 dataset_size: 1182780 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-sk-unrevised_NoQuant_32_64_0.05_64_BestF1_sk
ferrazzipietro
"2024-12-02T18:03:07Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T18:02:53Z"
--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 148050 num_examples: 102 - name: test num_bytes: 1034730 num_examples: 653 download_size: 248066 dataset_size: 1182780 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-sk-unrevised_NoQuant_32_32_0.01_64_BestF1_pl
ferrazzipietro
"2024-12-02T18:13:58Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T18:13:55Z"
--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 157591 num_examples: 101 - name: test num_bytes: 1105280 num_examples: 654 download_size: 273543 dataset_size: 1262871 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-sk-unrevised_NoQuant_32_64_0.05_64_BestF1_pl
ferrazzipietro
"2024-12-02T18:15:27Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T18:15:24Z"
--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 157591 num_examples: 101 - name: test num_bytes: 1105280 num_examples: 654 download_size: 273752 dataset_size: 1262871 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-sk-unrevised_NoQuant_32_32_0.05_64_BestF1_pl
ferrazzipietro
"2024-12-02T18:17:05Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T18:17:02Z"
--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 157591 num_examples: 101 - name: test num_bytes: 1105280 num_examples: 654 download_size: 273713 dataset_size: 1262871 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-sk-unrevised_NoQuant_32_64_0.05_64_BestF1_gr
ferrazzipietro
"2024-12-02T18:21:10Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T18:21:07Z"
--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 172478 num_examples: 94 - name: test num_bytes: 1556265 num_examples: 738 download_size: 313532 dataset_size: 1728743 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-sk-unrevised_NoQuant_16_16_0.05_64_BestF1_gr
ferrazzipietro
"2024-12-02T18:21:42Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T18:21:39Z"
--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 172478 num_examples: 94 - name: test num_bytes: 1556265 num_examples: 738 download_size: 313929 dataset_size: 1728743 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-sk-unrevised_NoQuant_32_16_0.05_64_BestF1_gr
ferrazzipietro
"2024-12-02T18:26:19Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T18:26:16Z"
--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 172478 num_examples: 94 - name: test num_bytes: 1556265 num_examples: 738 download_size: 315830 dataset_size: 1728743 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-sk-unrevised_NoQuant_64_64_0.01_64_BestF1_gr
ferrazzipietro
"2024-12-02T18:27:21Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T18:27:19Z"
--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 172478 num_examples: 94 - name: test num_bytes: 1556265 num_examples: 738 download_size: 313916 dataset_size: 1728743 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-sk-unrevised_NoQuant_16_16_0.01_64_BestF1_gr
ferrazzipietro
"2024-12-02T18:28:22Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T18:28:20Z"
--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 172478 num_examples: 94 - name: test num_bytes: 1556265 num_examples: 738 download_size: 312628 dataset_size: 1728743 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-sk-unrevised_NoQuant_32_16_0.05_64_BestF1_en
ferrazzipietro
"2024-12-02T18:31:25Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T18:31:22Z"
--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: id dtype: string - name: offsets sequence: int64 - name: role dtype: string - name: semantic_type_id dtype: string - name: text dtype: string - name: type dtype: string - name: original_text dtype: string - name: original_id dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 418249 num_examples: 106 - name: test num_bytes: 2472788 num_examples: 666 download_size: 292856 dataset_size: 2891037 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
marco-schouten/exp10
marco-schouten
"2024-12-02T18:36:37Z"
32
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T18:36:33Z"
--- dataset_info: features: - name: input_image dtype: image - name: edit_prompt dtype: string - name: edited_image dtype: image splits: - name: train num_bytes: 8311728.384 num_examples: 2328 download_size: 2712417 dataset_size: 8311728.384 configs: - config_name: default data_files: - split: train path: data/train-* ---
AnonymousLLMer/mcqa-finance-corpus-wiki
AnonymousLLMer
"2024-12-02T18:37:30Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T18:37:26Z"
--- dataset_info: features: - name: category dtype: string - name: instruction dtype: string - name: output dtype: string - name: match dtype: string - name: text dtype: string - name: source dtype: string splits: - name: train num_bytes: 2083754 num_examples: 281 download_size: 653623 dataset_size: 2083754 configs: - config_name: default data_files: - split: train path: data/train-* ---
Caua261/information
Caua261
"2024-12-02T18:56:16Z"
32
0
[ "task_categories:text-generation", "region:us", "code" ]
[ "text-generation" ]
"2024-12-02T18:53:37Z"
--- task_categories: - text-generation tags: - code --- JavaScript JavaScript é uma linguagem de programação amplamente utilizada para desenvolvimento web. É uma linguagem de tipagem dinâmica e orientada a objetos baseada em protótipos. JavaScript permite a manipulação do Document Object Model (DOM), permitindo que desenvolvedores criem interatividade nas páginas da web. Além disso, suporta programação assíncrona por meio de promises e a sintaxe async/await, facilitando a realização de operações que podem levar tempo, como chamadas a APIs. Exemplos de Uso: Validação de formulários Criação de animações Interação com APIs usando fetch ou XMLHttpRequest Node.js Node.js é um ambiente de execução que permite executar JavaScript no lado do servidor. Ele é baseado em um modelo de I/O não bloqueante e orientado a eventos, o que o torna altamente eficiente para aplicações que requerem operações em tempo real. O Node.js possui um vasto ecossistema de pacotes disponíveis através do npm (Node Package Manager), o que facilita a adição de funcionalidades às aplicações. Exemplos de Uso: Criação de servidores web Manipulação de bancos de dados (como MySQL e MongoDB) Desenvolvimento de APIs RESTful HTML HTML (HyperText Markup Language) é a linguagem padrão para estruturar páginas web. Ela utiliza uma série de elementos e tags para organizar o conteúdo da página, como cabeçalhos, parágrafos, links e formulários. HTML é fundamental para qualquer desenvolvimento web, pois define a estrutura básica do conteúdo que será exibido no navegador. Exemplos de Uso: Estruturação do conteúdo da página Criação de formulários interativos CSS CSS (Cascading Style Sheets) é uma linguagem utilizada para descrever a apresentação visual de documentos HTML. Com CSS, os desenvolvedores podem aplicar estilos aos elementos da página, como cores, fontes e layout. CSS também permite a criação de layouts responsivos por meio de media queries, garantindo que as páginas sejam exibidas corretamente em diferentes dispositivos. Exemplos de Uso: Estilização de elementos HTML Criação de layouts responsivos Para criar um arquivo de texto: Abra um editor de texto (como Notepad no Windows ou TextEdit no macOS). Copie o conteúdo acima. Cole no editor. Salve o arquivo com um nome apropriado, como explicacao_web.txt. JavaScript JavaScript é uma linguagem de programação de alto nível, interpretada e orientada a objetos, que se tornou essencial para o desenvolvimento web moderno. Sua principal função é adicionar interatividade às páginas web, permitindo que os desenvolvedores criem experiências dinâmicas e responsivas. JavaScript é executado no navegador do cliente, o que significa que pode manipular elementos da página em tempo real sem a necessidade de recarregar a página. Características Principais: Interatividade: Permite criar elementos interativos como sliders, modais e menus dinâmicos. Manipulação do DOM: Pode acessar e modificar a estrutura da página HTML através do DOM (Document Object Model). Programação Assíncrona: Suporta operações assíncronas com callbacks, promises e async/await, facilitando chamadas a APIs sem bloquear a interface do usuário. Node.js Node.js é uma plataforma que permite executar código JavaScript no lado do servidor. Utilizando o motor V8 do Google Chrome, o Node.js transforma JavaScript em uma linguagem de backend poderosa. É especialmente popular para construir aplicações em tempo real, como chats e jogos online, devido à sua natureza não bloqueante e orientada a eventos. Características Principais: Desempenho: O modelo de I/O não bloqueante permite que o Node.js manipule múltiplas conexões simultaneamente com alta eficiência. Ecossistema Rico: Com o npm (Node Package Manager), os desenvolvedores têm acesso a milhares de bibliotecas e frameworks que aceleram o desenvolvimento. Full Stack JavaScript: Permite que desenvolvedores usem JavaScript tanto no frontend quanto no backend, facilitando a comunicação entre as duas camadas. HTML HTML (HyperText Markup Language) é a espinha dorsal da web. É uma linguagem de marcação que define a estrutura básica das páginas web. Os elementos HTML são usados para criar conteúdo como textos, imagens, links e formulários. Cada elemento HTML é representado por uma tag que indica seu tipo e função. Características Principais: Estrutura Semântica: HTML5 introduziu novas tags semânticas (como <article>, <section>, <header>, <footer>) que melhoram a acessibilidade e SEO (Search Engine Optimization). Formulários Interativos: Permite criar formulários complexos para coleta de dados do usuário. Multimídia: Suporta a incorporação de vídeos e áudios diretamente nas páginas com as tags <video> e <audio>. CSS CSS (Cascading Style Sheets) é a linguagem usada para estilizar documentos HTML. Com CSS, os desenvolvedores podem controlar o layout, cores, fontes e outros aspectos visuais das páginas web. A separação entre conteúdo (HTML) e apresentação (CSS) permite um desenvolvimento mais organizado e flexível. Características Principais: Estilização Avançada: Permite aplicar estilos complexos usando seletores, pseudo-classes e pseudo-elementos. Layouts Responsivos: Com media queries, o CSS pode adaptar o layout da página para diferentes tamanhos de tela, garantindo uma boa experiência em dispositivos móveis. Animações e Transições: Suporta animações CSS que podem melhorar a experiência do usuário ao fornecer feedback visual. Para criar um arquivo de texto: Abra um editor de texto (como Notepad no Windows ou TextEdit no macOS). Copie o conteúdo acima. Cole no editor. Salve o arquivo com um nome apropriado, como explicacao_web_v2.txt.
nicholas-miklaucic/mptrj-graphs
nicholas-miklaucic
"2024-12-02T20:46:46Z"
32
0
[ "license:mit", "region:us" ]
null
"2024-12-02T19:50:11Z"
--- license: mit ---
SoufianeDahimi/Tamazight_ASR_Dataset
SoufianeDahimi
"2024-12-02T21:46:35Z"
32
0
[ "license:apache-2.0", "region:us" ]
null
"2024-12-02T21:46:35Z"
--- license: apache-2.0 ---
pclucas14/nqa-RAG-256_8_24
pclucas14
"2024-12-02T22:56:05Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T22:56:03Z"
--- dataset_info: features: - name: text sequence: sequence: string - name: questions sequence: string - name: answers sequence: sequence: string - name: document_id dtype: string - name: split dtype: string splits: - name: train num_bytes: 25864745 num_examples: 66 download_size: 9692932 dataset_size: 25864745 configs: - config_name: default data_files: - split: train path: data/train-* ---
pclucas14/nqa-RAG-256_7_24
pclucas14
"2024-12-02T23:03:28Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-02T23:03:27Z"
--- dataset_info: features: - name: text sequence: sequence: string - name: questions sequence: string - name: answers sequence: sequence: string - name: document_id dtype: string - name: split dtype: string splits: - name: train num_bytes: 26787005 num_examples: 66 download_size: 10503985 dataset_size: 26787005 configs: - config_name: default data_files: - split: train path: data/train-* ---
julia-se/tracka_mistral_fewshot_anger
julia-se
"2024-12-03T00:44:17Z"
32
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T00:44:15Z"
--- dataset_info: features: - name: id dtype: string - name: text dtype: string - name: Anger dtype: int64 - name: Disgust dtype: int64 - name: Fear dtype: int64 - name: Joy dtype: int64 - name: Sadness dtype: int64 - name: Surprise dtype: int64 - name: predicted_is_anger dtype: int64 - name: y_anger dtype: int64 splits: - name: train num_bytes: 472807 num_examples: 2226 download_size: 217016 dataset_size: 472807 configs: - config_name: default data_files: - split: train path: data/train-* ---
juliadollis/teste2_personal_mistral
juliadollis
"2024-12-03T03:11:49Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T03:11:47Z"
--- dataset_info: features: - name: Texto Original dtype: string - name: Encorajador_acolhedor dtype: string - name: Inspirador_personalizado dtype: string - name: Calmo_instrutivo dtype: string - name: Tecnico dtype: string splits: - name: train num_bytes: 6963 num_examples: 5 download_size: 13269 dataset_size: 6963 configs: - config_name: default data_files: - split: train path: data/train-* ---
juliadollis/teste3_personal_mistral
juliadollis
"2024-12-03T03:44:27Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T03:44:25Z"
--- dataset_info: features: - name: Texto Original dtype: string - name: Encorajador_acolhedor dtype: string - name: Inspirador_personalizado dtype: string - name: Calmo_instrutivo dtype: string - name: Tecnico dtype: string splits: - name: train num_bytes: 5992 num_examples: 5 download_size: 11657 dataset_size: 5992 configs: - config_name: default data_files: - split: train path: data/train-* ---
KaranCirusbug/guanaco-llama2-1k
KaranCirusbug
"2024-12-03T05:28:19Z"
32
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T05:26:12Z"
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1654448 num_examples: 1000 download_size: 966692 dataset_size: 1654448 configs: - config_name: default data_files: - split: train path: data/train-* ---
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_64_32_0.05_64_BestF1_it
ferrazzipietro
"2024-12-03T08:19:55Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T08:19:53Z"
--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 146361 num_examples: 100 - name: test num_bytes: 1023662 num_examples: 655 download_size: 228434 dataset_size: 1170023 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_32_64_0.01_64_BestF1_it
ferrazzipietro
"2024-12-03T08:20:41Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T08:20:39Z"
--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 146361 num_examples: 100 - name: test num_bytes: 1023662 num_examples: 655 download_size: 228486 dataset_size: 1170023 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_16_16_0.05_64_BestF1_sk
ferrazzipietro
"2024-12-03T08:23:11Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T08:23:08Z"
--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 148050 num_examples: 102 - name: test num_bytes: 1034730 num_examples: 653 download_size: 248498 dataset_size: 1182780 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_64_16_0.05_64_BestF1_sk
ferrazzipietro
"2024-12-03T08:23:57Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T08:23:54Z"
--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 148050 num_examples: 102 - name: test num_bytes: 1034730 num_examples: 653 download_size: 248386 dataset_size: 1182780 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_64_64_0.05_64_BestF1_sk
ferrazzipietro
"2024-12-03T08:26:55Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T08:26:52Z"
--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 148050 num_examples: 102 - name: test num_bytes: 1034730 num_examples: 653 download_size: 248308 dataset_size: 1182780 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_32_16_0.01_64_BestF1_sk
ferrazzipietro
"2024-12-03T08:29:08Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T08:29:05Z"
--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 148050 num_examples: 102 - name: test num_bytes: 1034730 num_examples: 653 download_size: 248376 dataset_size: 1182780 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_48503ed1-9c86-4d10-a41f-b40823ff650d
argilla-internal-testing
"2024-12-03T08:32:40Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T08:32:39Z"
--- 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_7d066179-dcbf-44f6-8a8a-85336a3db0e0
argilla-internal-testing
"2024-12-03T08:33:43Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T08:33:41Z"
--- 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-* ---
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_32_16_0.05_64_BestF1_gr
ferrazzipietro
"2024-12-03T08:49:29Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T08:49:26Z"
--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 172478 num_examples: 94 - name: test num_bytes: 1556265 num_examples: 738 download_size: 311988 dataset_size: 1728743 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_32_32_0.01_64_BestF1_gr
ferrazzipietro
"2024-12-03T08:52:34Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T08:52:31Z"
--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 172478 num_examples: 94 - name: test num_bytes: 1556265 num_examples: 738 download_size: 312097 dataset_size: 1728743 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_16_64_0.05_64_BestF1_en
ferrazzipietro
"2025-01-07T09:47:54Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T08:55:38Z"
--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: validation num_bytes: 104259 num_examples: 99 - name: test num_bytes: 919895 num_examples: 752 download_size: 223051 dataset_size: 1024154 configs: - config_name: default data_files: - split: validation path: data/validation-* - split: test path: data/test-* ---
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_ad084bf3-0563-4bb0-8a8d-c4c3c3acc149
argilla-internal-testing
"2024-12-03T11:01:30Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T11:01:29Z"
--- 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_db584a2f-ade2-4a74-8642-3a699920025c
argilla-internal-testing
"2024-12-03T11:01:32Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T11:01:30Z"
--- 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_a341d6c8-8318-4244-b864-ae20dc1f2b68
argilla-internal-testing
"2024-12-03T11:01:35Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T11:01:34Z"
--- 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_ded99b17-77e5-4376-97bd-34fa8f5bab07
argilla-internal-testing
"2024-12-03T11:01:38Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T11:01:38Z"
--- 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-* ---
wassim249/CICD
wassim249
"2024-12-03T12:58:25Z"
32
0
[ "size_categories:1M<n<10M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T12:57:48Z"
--- dataset_info: features: - name: destination_port dtype: int64 - name: flow_duration dtype: int64 - name: total_fwd_packets dtype: int64 - name: total_backward_packets dtype: int64 - name: total_length_of_fwd_packets dtype: int64 - name: total_length_of_bwd_packets dtype: int64 - name: fwd_packet_length_max dtype: int64 - name: fwd_packet_length_min dtype: int64 - name: fwd_packet_length_mean dtype: float64 - name: fwd_packet_length_std dtype: float64 - name: bwd_packet_length_max dtype: int64 - name: bwd_packet_length_min dtype: int64 - name: bwd_packet_length_mean dtype: float64 - name: bwd_packet_length_std dtype: float64 - name: flow_bytes/s dtype: float64 - name: flow_packets/s dtype: float64 - name: flow_iat_mean dtype: float64 - name: flow_iat_std dtype: float64 - name: flow_iat_max dtype: int64 - name: flow_iat_min dtype: int64 - name: fwd_iat_total dtype: int64 - name: fwd_iat_mean dtype: float64 - name: fwd_iat_std dtype: float64 - name: fwd_iat_max dtype: int64 - name: fwd_iat_min dtype: int64 - name: bwd_iat_total dtype: int64 - name: bwd_iat_mean dtype: float64 - name: bwd_iat_std dtype: float64 - name: bwd_iat_max dtype: int64 - name: bwd_iat_min dtype: int64 - name: fwd_header_length dtype: int64 - name: bwd_header_length dtype: int64 - name: fwd_packets/s dtype: float64 - name: bwd_packets/s dtype: float64 - name: min_packet_length dtype: int64 - name: max_packet_length dtype: int64 - name: packet_length_mean dtype: float64 - name: packet_length_std dtype: float64 - name: packet_length_variance dtype: float64 - name: psh_flag_count dtype: int64 - name: ack_flag_count dtype: int64 - name: urg_flag_count dtype: int64 - name: down/up_ratio dtype: int64 - name: average_packet_size dtype: float64 - name: avg_fwd_segment_size dtype: float64 - name: avg_bwd_segment_size dtype: float64 - name: fwd_header_length.1 dtype: int64 - name: subflow_fwd_packets dtype: int64 - name: subflow_fwd_bytes dtype: int64 - name: subflow_bwd_packets dtype: int64 - name: subflow_bwd_bytes dtype: int64 - name: init_win_bytes_forward dtype: int64 - name: init_win_bytes_backward dtype: int64 - name: act_data_pkt_fwd dtype: int64 - name: min_seg_size_forward dtype: int64 - name: active_max dtype: int64 - name: active_min dtype: int64 - name: idle_mean dtype: float64 - name: idle_std dtype: float64 - name: idle_max dtype: int64 - name: idle_min dtype: int64 - name: label dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1418135552 num_examples: 2801091 - name: validation num_bytes: 14181625 num_examples: 28011 - name: test num_bytes: 143263 num_examples: 283 download_size: 543098167 dataset_size: 1432460440 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
jeongseokoh/MATH-SHEPHERD-seperated
jeongseokoh
"2024-12-03T13:57:39Z"
32
0
[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T13:57:21Z"
--- dataset_info: features: - name: question dtype: string - name: steps sequence: string - name: labels sequence: int64 splits: - name: train num_bytes: 396190163 num_examples: 444539 download_size: 180585510 dataset_size: 396190163 configs: - config_name: default data_files: - split: train path: data/train-* ---
sherzoyjan/kitti-labelled-1K
sherzoyjan
"2024-12-03T14:22:34Z"
32
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T14:22:13Z"
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 831256289.0 num_examples: 1000 download_size: 822701795 dataset_size: 831256289.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
code2t/rotating_verification_code
code2t
"2024-12-03T14:41:20Z"
32
0
[ "license:mit", "size_categories:1K<n<10K", "format:imagefolder", "modality:image", "library:datasets", "library:mlcroissant", "region:us" ]
null
"2024-12-03T14:38:20Z"
--- license: mit ---
LuckyLukke/NEGOTIO_evaluate_evaluator
LuckyLukke
"2024-12-03T14:39:26Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T14:39:25Z"
--- dataset_info: features: - name: id dtype: int64 - name: starting_agent dtype: int64 - name: game dtype: string - name: trajectory_starter list: - name: content dtype: string - name: role dtype: string - name: trajectory_responder list: - name: content dtype: string - name: role dtype: string - name: model_agent_1 dtype: string - name: model_agent_2 dtype: string splits: - name: train num_bytes: 4522806 num_examples: 500 download_size: 1122857 dataset_size: 4522806 configs: - config_name: default data_files: - split: train path: data/train-* ---
bombshelll/brain_location
bombshelll
"2024-12-03T15:07:16Z"
32
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:image", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T14:44:41Z"
--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': left '1': right splits: - name: train num_bytes: 146505220.412 num_examples: 1599 - name: validation num_bytes: 45674771.0 num_examples: 465 - name: test num_bytes: 33866810.0 num_examples: 364 download_size: 199712145 dataset_size: 226046801.412 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
XueyingJia/review-search-dataset
XueyingJia
"2024-12-03T16:20:01Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T16:19:55Z"
--- dataset_info: features: - name: user_id dtype: int64 - name: item_id dtype: int64 - name: review dtype: string - name: query dtype: string - name: qid dtype: int64 splits: - name: train num_bytes: 931 num_examples: 2 download_size: 4645 dataset_size: 931 configs: - config_name: default data_files: - split: train path: data/train-* ---
XueyingJia/amazon-search-val
XueyingJia
"2024-12-03T16:31:01Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T16:30:59Z"
--- dataset_info: features: - name: user_id dtype: int64 - name: item_id dtype: int64 - name: review dtype: string - name: query dtype: string - name: qid dtype: int64 splits: - name: train num_bytes: 20850 num_examples: 28 download_size: 17076 dataset_size: 20850 configs: - config_name: default data_files: - split: train path: data/train-* ---
juliadollis/teste2_personal_gpt
juliadollis
"2024-12-03T16:52:27Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T16:35:47Z"
--- dataset_info: features: - name: Texto dtype: string - name: Estrategia de Prompt dtype: string splits: - name: train num_bytes: 57335 num_examples: 225 download_size: 25486 dataset_size: 57335 configs: - config_name: default data_files: - split: train path: data/train-* ---
BhuvanaNagaraj/Resume
BhuvanaNagaraj
"2024-12-03T16:59:20Z"
32
0
[ "license:llama3.2", "size_categories:1K<n<10K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T16:56:46Z"
--- license: llama3.2 ---
jeongseokoh/GSM8K-STEP-ANS
jeongseokoh
"2024-12-05T07:30:26Z"
32
0
[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T17:39:03Z"
--- dataset_info: features: - name: question dtype: string - name: past_steps sequence: string - name: answer dtype: string - name: original_question dtype: string - name: original_rp dtype: string - name: negative_steps sequence: string - name: task dtype: string splits: - name: train num_bytes: 171166958 num_examples: 121266 download_size: 90978963 dataset_size: 171166958 configs: - config_name: default data_files: - split: train path: data/train-* ---
RyanYr/self-reflect_mini8Bit-t0_mistlarge-t12_om2-460k_binlabel
RyanYr
"2024-12-03T18:47:49Z"
32
0
[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T18:47:13Z"
--- dataset_info: features: - name: problem dtype: string - name: generated_solution dtype: string - name: answer dtype: string - name: problem_source dtype: string - name: response@0 sequence: string - name: response@1 sequence: string - name: response@2 sequence: string - name: response@0_ans sequence: string - name: response@0_correctness sequence: bool - name: response@2_ans sequence: string - name: response@2_correctness sequence: bool splits: - name: train num_bytes: 2452077199 num_examples: 460273 download_size: 1075478874 dataset_size: 2452077199 configs: - config_name: default data_files: - split: train path: data/train-* ---
taufiqsyed/salami_neural_demo_enriched
taufiqsyed
"2024-12-03T19:19:33Z"
32
0
[ "size_categories:n<1K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T19:19:20Z"
--- dataset_info: features: - name: audio dtype: audio - name: song_id dtype: string - name: structure dtype: string - name: start_time dtype: float64 - name: end_time dtype: float64 - name: tempos dtype: string - name: keys dtype: string - name: instruments dtype: string - name: genres dtype: string - name: moods dtype: string - name: metadata dtype: string splits: - name: train num_bytes: 26462307.0 num_examples: 10 - name: eval num_bytes: 84679315.0 num_examples: 32 download_size: 108368116 dataset_size: 111141622.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: eval path: data/eval-* ---
mlgawd/final_dpo_nemo_v10
mlgawd
"2024-12-03T20:32:04Z"
32
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T20:32:02Z"
--- dataset_info: features: - name: questions dtype: string - name: accepted dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 28102869 num_examples: 5877 download_size: 15924726 dataset_size: 28102869 configs: - config_name: default data_files: - split: train path: data/train-* ---
mlgawd/final_dpo_nemo_v12
mlgawd
"2024-12-03T20:45:50Z"
32
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T20:45:47Z"
--- dataset_info: features: - name: questions dtype: string - name: accepted dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 28031447 num_examples: 5864 download_size: 15892999 dataset_size: 28031447 configs: - config_name: default data_files: - split: train path: data/train-* ---
qfq/trainnov28_timelimit_sft_tokensleft
qfq
"2024-12-07T22:46:47Z"
32
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T21:20:07Z"
--- 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 - name: text dtype: string splits: - name: train num_bytes: 15181837 num_examples: 1088 - name: test num_bytes: 785967 num_examples: 58 download_size: 6922708 dataset_size: 15967804 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
jvandomburgh/study_skills
jvandomburgh
"2024-12-03T21:52:36Z"
32
0
[ "license:apache-2.0", "region:us" ]
null
"2024-12-03T21:52:36Z"
--- license: apache-2.0 ---
mlfoundations-dev/evol_instruct_gpt-4o-mini_scale_x.5
mlfoundations-dev
"2024-12-03T21:59:54Z"
32
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T21:59:43Z"
--- dataset_info: features: - name: evolved_instruction dtype: string - name: completion dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 445557832 num_examples: 72773 download_size: 250158412 dataset_size: 445557832 configs: - config_name: default data_files: - split: train path: data/train-* ---
CambioMoney/ami-speaker-analysis_full_run_4
CambioMoney
"2024-12-03T23:49:26Z"
32
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
null
"2024-12-03T23:30:57Z"
--- dataset_info: features: - name: meeting_id dtype: string - name: audio_id dtype: string - name: text dtype: string - name: audio struct: - name: array sequence: float64 - name: path dtype: string - name: sampling_rate dtype: int64 - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: microphone_id dtype: string - name: speaker_id dtype: string - name: is_complete dtype: bool - name: original_segment dtype: bool splits: - name: train num_bytes: 1351372453 num_examples: 3000 download_size: 285277771 dataset_size: 1351372453 configs: - config_name: default data_files: - split: train path: data/train-* ---