Datasets:

librarian-bots

/

dataset_cards_with_metadata

like 11

barbaroo/STS

barbaroo

--- language: - fo size_categories: - 1K<n<10K license: cc-by-4.0 --- This is a synthetic Faroese Semantic Textual Similarity dataset. Labels range from 0 (no similarity) to 5 (the two sentences are completely equivalent). The dataset was generated by: - Translating sentences from the Basic Faroese Language Resource Kit (BLARK) corpus to English by leveraging a Nordic LLM, GPT-Sw3. - Sentences were compared to each other in terms of semantic similarity by Sentence BERT (SBERT, ) - Pairs of sentences were then sampled uniformly in terms of similarity score, to compile a balanced dataset. The dataset contains 200 sentences for each class (Similarity = 0,1,2,3,4,5).

CognitiveLab/Arc_kan

CognitiveLab

--- language: - kn configs: - config_name: ARC Challenge data_files: - split: train path: "ARC Challenge/arc_kan-train.json" - split: test path: "ARC Challenge/arc_kan-test.json" - split: validation path: "ARC Challenge/arc_kan-validation.json" - config_name: ARC Easy data_files: - split: train path: "ARC Easy/arc_easy_kan-train.json" - split: test path: "ARC Easy/arc_easy_kan-test.json" - split: validation path: "ARC Easy/arc_easy_kan-validation.json" --- # ARC Kannada

liuhyuu/NetEaseCrowd

liuhyuu

--- license: cc-by-sa-4.0 language: - en tags: - Crowdsourcing - Truth Inference - Label Aggregation pretty_name: 'NetEaseCrowd: A Dataset for Long-term and Online Crowdsourcing Truth Inference' size_categories: - 1M<n<10M --- # 🧑‍🤝‍🧑 NetEaseCrowd: A Dataset for Long-term and Online Crowdsourcing Truth Inference [View it in GitHub](https://github.com/fuxiAIlab/NetEaseCrowd-Dataset) ## Introduction We introduce NetEaseCrowd, a large-scale crowdsourcing annotation dataset based on a mature Chinese data crowdsourcing platform of NetEase Inc.. NetEaseCrowd dataset contains about **2,400** workers, **1,000,000** tasks, and **6,000,000** annotations between them, where the annotations are collected in about 6 months. In this dataset, we provide ground truths for all the tasks and record timestamps for all the annotations. ### Task NetEaseCrowd dataset is built based on a gesture comparison task. Each task contains three choices, where two are similar gestures and the other one is not. Annotators are required to pick out the different one. ### Comparison with existing datasets Compared with the existing crowdsourcing datasets, our NetEaseCrowd dataset has the following characteristics: | Characteristic | Existing datasets | NetEaseCrowd dataset | |----------------|------------------------------------------------------|-----------------------------------------------------------| | Scalability | Relatively small sizes in #workers/tasks/annotations | Lage-scale data collection with 6 millions of annotations | | Timestamps | Short-term data with no timestamps recorded | Complete timestamps recorded during a 6-month timespan | | Task Type | Single type of tasks | Various task types with different required capabilities | <!-- ## Citation If you use the dataset in your work, please cite: @inproceedings{TODO} --> ## Dataset Statistics The basic statistics of NetEaseCrowd dataset and [other previous datasets](#other-public-datasets) are as follows: | Dataset | \#Worker | \#Task | \#Groundtruth | \#Anno | Avg(\#Anno/worker) | Avg(\#Anno/task) | Timestamp | Task type | |--------------------------------------------|----------|---------|---------------|-----------|--------------------|------------------|--------------|-----------| | NetEaseCrowd | 2,413 | 999,799 | 999,799 | 6,016,319 | 2,493.3 | 6.0 | ✔︎ | Multiple | | Adult | 825 | 11,040 | 333 | 92,721 | 112.4 | 8.4 | ✘ | Single | | Birds | 39 | 108 | 108 | 4,212 | 108.0 | 39.0 | ✘ | Single | | Dog | 109 | 807 | 807 | 8,070 | 74.0 | 10.0 | ✘ | Single | | CF | 461 | 300 | 300 | 1,720 | 3.7 | 5.7 | ✘ | Single | | CF\_amt | 110 | 300 | 300 | 6030 | 54.8 | 20.1 | ✘ | Single | | Emotion | 38 | 700 | 565 | 7,000 | 184.2 | 10.0 | ✘ | Single | | Smile | 64 | 2,134 | 159 | 30,319 | 473.7 | 14.2 | ✘ | Single | | Face | 27 | 584 | 584 | 5,242 | 194.1 | 9.0 | ✘ | Single | | Fact | 57 | 42,624 | 576 | 216,725 | 3802.2 | 5.1 | ✘ | Single | | MS | 44 | 700 | 700 | 2,945 | 66.9 | 4.2 | ✘ | Single | | product | 176 | 8,315 | 8,315 | 24,945 | 141.7 | 3.0 | ✘ | Single | | RTE | 164 | 800 | 800 | 8,000 | 48.8 | 10.0 | ✘ | Single | | Sentiment | 1,960 | 98,980 | 1,000 | 569,375 | 290.5 | 5.8 | ✘ | Single | | SP | 203 | 4,999 | 4,999 | 27,746 | 136.7 | 5.6 | ✘ | Single | | SP\_amt | 143 | 500 | 500 | 10,000 | 69.9 | 20.0 | ✘ | Single | | Trec | 762 | 19,033 | 2,275 | 88,385 | 116.0 | 4.6 | ✘ | Single | | Tweet | 85 | 1,000 | 1,000 | 20,000 | 235.3 | 20.0 | ✘ | Single | | Web | 177 | 2,665 | 2,653 | 15,567 | 87.9 | 5.8 | ✘ | Single | | ZenCrowd\_us | 74 | 2,040 | 2,040 | 12,190 | 164.7 | 6.0 | ✘ | Single | | ZenCrowd\_in | 25 | 2,040 | 2,040 | 11,205 | 448.2 | 5.5 | ✘ | Single | | ZenCrowd\_all | 78 | 2,040 | 2,040 | 21,855 | 280.2 | 10.7 | ✘ | Single | <!-- The basic statistics of NetEaseCrowd dataset shows as follows: | | NetEaseCrowd | | ------------- | ------------ | | #Workers | 2,413 | | #Tasks | 999,799 | | #Groundtruths | 999,799 | | #Annotations | 6,016,319 | --> ## Data Content and Format ### Obtain the data Two ways to access the dataset: * Directly download overall NetEaseCrowd in [Hugging Face](https://huggingface.co/datasets/liuhyuu/NetEaseCrowd) [**Recommended**] * Under the [`data/` folder](https://github.com/fuxiAIlab/NetEaseCrowd-Dataset/tree/main/data), the NetEaseCrowd dataset is provided in partitions in the csv file format. Each partition is named as `NetEaseCrowd_part_x.csv`. Concat them to get the entire NetEaseCrowd dataset. ### Dataset format In the dataset, each line of record represents an interaction between a worker and a task, with the following columns: * **taskId**: The unique id of the annotated task. * **tasksetId**: The unique id of the task set. Each task set contains unspecified number of tasks. Each task belongs to exactly one task set. * **workerId**: The unique id of the worker. * **answer**: The annotation given by the worker, which is an enumeric number starting from 0. * **completeTime**: The integer timestamp recording the completion time of the annotation. * **truth**: The groundtruth of the annotated task, which, in consistency with answer, is also an enumeric number starting from 0. * **capability**: The unique id of the capability required by the annotated taskset. Each taskset belongs to exactly one capability. *For the privacy concerns, all sensitive content like as -Ids, has been anonymized.* ### Data sample | tasksetId | taskId | workerId | answer | completeTime | truth | capability | |-----------|---------------------|----------|--------|---------------|-------|------------| | 6980 | 1012658482844795232 | 64 | 2 | 1661917345953 | 1 | 69 | | 6980 | 1012658482844795232 | 150 | 1 | 1661871234755 | 1 | 69 | | 6980 | 1012658482844795232 | 263 | 0 | 1661855450281 | 1 | 69 | In the example above, there are three annotations, all from the same taskset 6980 and the same task 1012658482844795232. Three annotators, with ids 64, 150, and 263, provide annotations of 2, 1, and 0, respectively. They do the task at different time. The truth label for this task is 1, and the capability id of the task is 69. ## Baseline Models We test several existing truth inference methods in our dataset, and detailed analysis with more experimental setups can be found in our paper. | Method | Accuracy | F1-score | |----------------|----------|----------| | MV | 0.92695 | 0.92692 | | DS | 0.95178 | 0.94817 | | MACE | 0.95991 | 0.94957 | | Wawa | 0.94814 | 0.94445 | | ZeroBasedSkill | 0.94898 | 0.94585 | | GLAD | 0.95064 | 0.95058 | | EBCC | 0.91071 | 0.90996 | | ZC | 0.95305 | 0.95301 | | TiReMGE | 0.92713 | 0.92706 | | LAA | 0.94173 | 0.94169 | | BiLA | 0.88036 | 0.87896 | ### Test with the dataset directly from crowd-kit The NetEaseCrowd dataset has been integrated into the [crowd-kit](https://github.com/Toloka/crowd-kit) (with pull request [here](https://github.com/Toloka/crowd-kit/pull/101)), you can use it directly in your code with the following code(with crowd-kit version > 1.2.1): ```python from crowdkit.aggregation import DawidSkene from crowdkit.datasets import load_dataset df, gt = load_dataset('netease_crowd') ds = DawidSkene(10) result = ds.fit_predict(df) print(len(result)) # 999799 ``` ## Other public datasets We provide a curated list for other public datasets towards truth inference task. | Dataset Name | Resource | |----------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | adult | Quality management on amazon mechanical turk. [[paper](https://dl.acm.org/doi/abs/10.1145/1837885.1837906)][[data](https://github.com/ipeirotis/Get-Another-Label/tree/master/data)] | | sentiment<br>fact | Workshops Held at the First AAAI Conference on Human Computation and Crowdsourcing: A Report. [[paper](https://ojs.aaai.org/index.php/aimagazine/article/view/2537/2427)][[data](https://sites.google.com/site/crowdscale2013/home)] | | MS<br>zencrowd_all<br>zencrowd_us<br>zencrowd_in<br>sp<br>sp_amt<br>cf<br>cf_amt | The active crowd toolkit: An open-source tool for benchmarking active learning algorithms for crowdsourcing research. [[paper](https://ojs.aaai.org/index.php/HCOMP/article/download/13256/13104)][[data](https://github.com/orchidproject/active-crowd-toolkit)] | | Product<br>tweet<br>dog<br>face<br>duck<br>relevance<br>smile | Truth inference in crowdsourcing: Is the problem solved? [[paper](https://hub.hku.hk/bitstream/10722/243527/1/content.pdf?accept=1)][[data](https://zhydhkcws.github.io/crowd_truth_inference/)] <br> *Note that tweet dataset is called sentiment in this source. It is different from the sentiment dataset in CrowdScale2013.* | | bird<br>rte<br>web<br>trec | Spectral methods meet em: A provably optimal algorithm for crowdsourcing. [[paper](https://proceedings.neurips.cc/paper/2014/file/788d986905533aba051261497ecffcbb-Paper.pdf)][[data](https://github.com/zhangyuc/SpectralMethodsMeetEM)] | ## Citation If you use this project in your research or work, please cite it using the following BibTeX entry: ```bibtex @misc{wang2024dataset, title={A Dataset for the Validation of Truth Inference Algorithms Suitable for Online Deployment}, author={Fei Wang and Haoyu Liu and Haoyang Bi and Xiangzhuang Shen and Renyu Zhu and Runze Wu and Minmin Lin and Tangjie Lv and Changjie Fan and Qi Liu and Zhenya Huang and Enhong Chen}, year={2024}, eprint={2403.08826}, archivePrefix={arXiv}, primaryClass={cs.HC} } ``` ## License The NetEaseCrowd dataset is licensed under [CC-BY-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/deed.en).

aborruso/opencoesione

aborruso

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

schwepat/anonymized-amazon-dataset

schwepat

--- license: mit ---

JaehyungKim/p2c_dynasent2_all

JaehyungKim

--- license: other license_name: following-original-dataset license_link: LICENSE ---

minimario/math-openwebmath-retrievals

minimario

--- dataset_info: features: - name: problem dtype: string - name: level dtype: string - name: type dtype: string - name: solution dtype: string - name: answer dtype: string - name: p_retrievals sequence: string - name: s_retrievals sequence: string - name: ps_retrievals sequence: string splits: - name: test num_bytes: 261081887 num_examples: 5000 - name: train num_bytes: 399597127 num_examples: 7500 download_size: 329948190 dataset_size: 660679014 configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* ---

anjan77/guanaco-llama2-1k

anjan77

--- 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-* ---

audibeal/my-cool-dataset-4

audibeal

--- configs: - config_name: train data_files: "train.csv" - config_name: dev data_files: "dev.csv" - config_name: test data_files: "test.csv" ---

AabirDey/job-queries-and-customer-service

AabirDey

--- language: - en license: mit ---

iNeil77/pseudo-mini-pile

iNeil77

--- dataset_info: - config_name: all features: - name: content dtype: string splits: - name: train num_bytes: 360187653412.6177 num_examples: 56194997 download_size: 199030076349 dataset_size: 360187653412.6177 - config_name: c4_realnews features: - name: content dtype: string splits: - name: train num_bytes: 31597106256.723488 num_examples: 11427438 download_size: 19889880484 dataset_size: 31597106256.723488 - config_name: openwebtext features: - name: content dtype: string splits: - name: train num_bytes: 30974178275.039234 num_examples: 6474479 download_size: 19069709415 dataset_size: 30974178275.039234 - config_name: peS2o features: - name: content dtype: string splits: - name: train num_bytes: 221900508006.5479 num_examples: 32612199 download_size: 116217303065 dataset_size: 221900508006.5479 - config_name: redpajama_books features: - name: content dtype: string splits: - name: train num_bytes: 49246538575.26426 num_examples: 107443 download_size: 29612204926 dataset_size: 49246538575.26426 - config_name: stackexchange features: - name: content dtype: string splits: - name: train num_bytes: 2034535930.2150385 num_examples: 716532 download_size: 1222605537 dataset_size: 2034535930.2150385 - config_name: uspto features: - name: content dtype: string splits: - name: train num_bytes: 14755999149.910166 num_examples: 3247716 download_size: 7058272149 dataset_size: 14755999149.910166 - config_name: wiki features: - name: content dtype: string splits: - name: train num_bytes: 7528525537.163156 num_examples: 1609190 download_size: 4593971902 dataset_size: 7528525537.163156 configs: - config_name: all data_files: - split: train path: all/train-* - config_name: c4_realnews data_files: - split: train path: c4_realnews/train-* - config_name: openwebtext data_files: - split: train path: openwebtext/train-* - config_name: peS2o data_files: - split: train path: peS2o/train-* - config_name: redpajama_books data_files: - split: train path: redpajama_books/train-* - config_name: stackexchange data_files: - split: train path: stackexchange/train-* - config_name: uspto data_files: - split: train path: uspto/train-* - config_name: wiki data_files: - split: train path: wiki/train-* task_categories: - text-generation language: - en size_categories: - 10M<n<100M --- A small, aggressively cleaned and de-duped pre-training corpus for academic settings. It aims to recreate something akin to [The Pile](https://huggingface.co/datasets/EleutherAI/pile) but prioritizes quality for the constrained token budget academic researchers live with. It has seven config subsets and an eighth `all` subset that combines them for a total of ~91B tokens (GPT2 Tokenizer estimate). These splits are as follows: 1. `c4_realnews`: The RealNews domain subset of the C4 dataset containing news articles. 2. `openwebtext`: The OpenWebText dataset containing the contents of the links mentioned in Reddit posts with at least 3 upvotes. 3. `peS2o`: The PeS2o dataset containing academic articles from Semantic Scholar. 4. `redpajama_books`: The books subset of RedPajama V1. 5. `stackexchange`: The EN StackExchange non-code subset of the BigScience ROOTs dataset. 6. `uspto`: The EN USPTO patent applications contents' subset of the BigScience ROOTs dataset. 7. `wiki`: The EN Wiki subset of the BigScience ROOTs dataset. The following processing and filtering steps have been applied: 1. Removed citation text and bibliography information for academic texts. 2. Ran a perplexity filter using a KenLM model trained on the EN OSCAR corpus and removed documents with a perplexity of more than 325 and less than 7. 3. Removed samples which have a repeating <=4-gram proportion of 15%. 4. Removed samples which have lower than 99% confidence of being EN using the lingua language detector. 5. Performed an aggressive MinHash de-dupe using a shingle size of 8 and a low threshold of 0.5.

Iker/NoticIA_Human_Validation

Iker

--- language: - es license: cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - summarization pretty_name: NoticIA Human Validation dataset_info: features: - name: web_url dtype: string - name: web_headline dtype: string - name: summary dtype: string - name: summary2 dtype: string - name: web_text dtype: string splits: - name: test num_examples: 100 configs: - config_name: default data_files: - split: test path: test.jsonl tags: - summarization - clickbait - news --- <p align="center"> <img src="https://huggingface.co/datasets/Iker/NoticIA/resolve/main/assets/logo.png" style="height: 250px;"> </p> <h3 align="center">"A Clickbait Article Summarization Dataset in Spanish."</h3> This repository contains the manual annotations from a second human to validate the test set of the NoticIA dataset. The full NoticIA dataset is available here: [https://huggingface.co/datasets/Iker/NoticIA](https://huggingface.co/datasets/Iker/NoticIA) # Data explanation - **web_url** (int): The URL of the news article - **web_headline** (str): The headline of the article, which is a Clickbait. - **summary** (str): The original summary in the NoticIA dataset. - **summary2** (str): The second summary written by another human to validate the quality of `summary` - **web_text** (int): The body of the article. # Dataset Description - **Curated by:** [Iker García-Ferrero](https://ikergarcia1996.github.io/Iker-Garcia-Ferrero/), [Begoña Altura](https://www.linkedin.com/in/bego%C3%B1a-altuna-78014139) - **Language(s) (NLP):** Spanish - **License:** apache-2.0 # Dataset Usage ```Python # pip install datasets evaluate rouge-score from datasets import load_dataset from evaluate import load dataset = load_dataset("Iker/NoticIA_Human_Validation",split="test") rouge = load("rouge") results = rouge.compute( predictions=[x["summary2"] for x in dataset], references=[[x["summary"]] for x in dataset], use_aggregator=True, ) print(results) ``` # Uses This dataset is intended to build models tailored for academic research that can extract information from large texts. The objective is to research whether current LLMs, given a question formulated as a Clickbait headline, can locate the answer within the article body and summarize the information in a few words. The dataset also aims to serve as a task to evaluate the performance of current LLMs in Spanish. # Out-of-Scope Use You cannot use this dataset to develop systems that directly harm the newspapers included in the dataset. This includes using the dataset to train profit-oriented LLMs capable of generating articles from a short text or headline, as well as developing profit-oriented bots that automatically summarize articles without the permission of the article's owner. Additionally, you are not permitted to train a system with this dataset that generates clickbait headlines. This dataset contains text and headlines from newspapers; therefore, you cannot use it for commercial purposes unless you have the license for the data. # Dataset Creation The dataset has been meticulously created by hand. We utilize two sources to compile Clickbait articles: - The Twitter user [@ahorrandoclick1](https://twitter.com/ahorrandoclick1), who reposts Clickbait articles along with a hand-crafted summary. Although we use their summaries as a reference, most of them have been rewritten (750 examples from this source). - The web demo [⚔️ClickbaitFighter⚔️](https://iker-clickbaitfighter.hf.space/), which operates a pre-trained model using an early iteration of our dataset. We collect all the model inputs/outputs and manually correct them (100 examples from this source). # Who are the annotators? The dataset was originally by [Iker García-Ferrero](https://ikergarcia1996.github.io/Iker-Garcia-Ferrero/) and has been validated by [Begoña Altura](https://www.linkedin.com/in/bego%C3%B1a-altuna-78014139). The annotation took ~40 hours. # Citation ```bittext @misc{noticia2024, title={NoticIA: A Clickbait Article Summarization Dataset in Spanish}, author={Iker García-Ferrero and Begoña Altuna}, year={2024}, eprint={2404.07611}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

valurank/spam_ham_comments

valurank

--- license: other license_name: valurank license_link: LICENSE ---

imperialwarrior/open-australian-legal-qa-paraphrased-easy-gemini

imperialwarrior

--- dataset_info: features: - name: index dtype: 'null' - name: pipeline_1_result dtype: string - name: pipeline_1_result_embeddings dtype: string - name: pipeline_2_context dtype: string - name: pipeline_2_result dtype: string - name: pipeline_2_result_embeddings dtype: string - name: pipeline_3_context dtype: string - name: pipeline_3_result dtype: string - name: pipeline_3_result_embeddings dtype: string - name: pipeline_4_context dtype: string - name: pipeline_4_result dtype: string - name: pipeline_4_result_embeddings dtype: string - name: pipeline_5_context dtype: string - name: pipeline_5_result dtype: string - name: pipeline_5_result_embeddings dtype: string - name: pipeline_6_context dtype: string - name: pipeline_6_result dtype: string - name: pipeline_6_result_embeddings dtype: string - name: pipeline_7_context dtype: string - name: pipeline_7_result dtype: string - name: pipeline_7_result_embeddings dtype: string - name: referenced_question dtype: string - name: answer dtype: string - name: question dtype: string - name: question_non_retrieval_embeddings dtype: string - name: answer_non_retrieval_embeddings dtype: string - name: question_retrieval_embeddings dtype: string - name: answer_retrieval_embeddings dtype: string - name: __index_level_0__ dtype: float64 - name: case_index dtype: float64 - name: pipeline_6_case_indexes sequence: int64 - name: pipeline_7_case_indexes sequence: int64 splits: - name: train num_bytes: 41703799 num_examples: 207 download_size: 14322382 dataset_size: 41703799 configs: - config_name: default data_files: - split: train path: data/train-* ---

imperialwarrior/open-australian-legal-qa-paraphrased-hard-gemini

imperialwarrior

--- dataset_info: features: - name: index dtype: 'null' - name: pipeline_1_result dtype: string - name: pipeline_1_result_embeddings dtype: string - name: pipeline_2_context dtype: string - name: pipeline_2_result dtype: string - name: pipeline_2_result_embeddings dtype: string - name: pipeline_3_context dtype: string - name: pipeline_3_result dtype: string - name: pipeline_3_result_embeddings dtype: string - name: pipeline_4_context dtype: string - name: pipeline_4_result dtype: string - name: pipeline_4_result_embeddings dtype: string - name: pipeline_5_context dtype: string - name: pipeline_5_result dtype: string - name: pipeline_5_result_embeddings dtype: string - name: pipeline_6_context dtype: string - name: pipeline_6_result dtype: string - name: pipeline_6_result_embeddings dtype: string - name: pipeline_7_context dtype: string - name: pipeline_7_result dtype: string - name: pipeline_7_result_embeddings dtype: string - name: referenced_question dtype: string - name: answer dtype: string - name: question dtype: string - name: question_non_retrieval_embeddings dtype: string - name: answer_non_retrieval_embeddings dtype: string - name: question_retrieval_embeddings dtype: string - name: answer_retrieval_embeddings dtype: string - name: __index_level_0__ dtype: float64 - name: case_index dtype: float64 - name: pipeline_6_case_indexes sequence: int64 - name: pipeline_7_case_indexes sequence: int64 splits: - name: train num_bytes: 40967131 num_examples: 203 download_size: 14378490 dataset_size: 40967131 configs: - config_name: default data_files: - split: train path: data/train-* ---

ariji1/acn_train_Test

ariji1

--- license: apache-2.0 dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 74288.00649350649 num_examples: 123 - name: test num_bytes: 18722.993506493505 num_examples: 31 download_size: 49882 dataset_size: 93011.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

GusPuffy/python-decompiler-37-0.7-train

GusPuffy

--- license: unknown tags: - python - python3.7 - code pretty_name: p --- I don't know what the licenses are with the code that is in this dataset, so keep that in mind if you are using it. Pulled source code from the top 8k PyPi projects. If you own part of the data in the dataset and want to request it be removed please let me know and I will remove it.

ritwikraha/random-storage

ritwikraha

--- license: creativeml-openrail-m ---

carlavic/Ani

carlavic

--- license: openrail ---

aryamannningombam/indian-female-tts-dataset

aryamannningombam

--- dataset_info: features: - name: file dtype: string - name: text dtype: string - name: tag dtype: string - name: file_path dtype: string - name: y sequence: float32 - name: emotional_embedding sequence: int64 - name: non_characters dtype: 'null' - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 923807734 num_examples: 2843 download_size: 911770863 dataset_size: 923807734 configs: - config_name: default data_files: - split: train path: data/train-* ---

florentgbelidji/ncbi_extracted_running

florentgbelidji

--- dataset_info: features: - name: id dtype: string - name: text dtype: string - name: title dtype: string - name: date dtype: string - name: authors dtype: string - name: language dtype: string splits: - name: train num_bytes: 19228492 num_examples: 432 download_size: 10192770 dataset_size: 19228492 --- # Dataset Card for "ncbi_extracted_running" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

previsone/modwell-kitch03

previsone

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 824628362.19 num_examples: 6862 download_size: 823779542 dataset_size: 824628362.19 configs: - config_name: default data_files: - split: train path: data/train-* ---

charkgan/TheMET

charkgan

--- license: cc0-1.0 ---

Romildon/boa

Romildon

--- license: openrail ---

FreedomIntelligence/ALLaVA-4V-Chinese

FreedomIntelligence

--- license: apache-2.0 task_categories: - question-answering - text-generation language: - zh tags: - GPT-4V - LVLM - Vision - Language size_categories: - 1M<n<10M configs: - config_name: allava_laion data_files: - split: caption path: "allava_laion/ALLaVA-Caption-LAION-4V_Chinese.json" - split: instruct path: "allava_laion/ALLaVA-Instruct-LAION-4V_Chinese.json" - config_name: allava_vflan data_files: - split: caption path: "allava_vflan/ALLaVA-Caption-VFLAN-4V_Chinese.json" - split: instruct path: "allava_vflan/ALLaVA-Instruct-VFLAN-4V_Chinese.json" # - config_name: allava_laion_instruction # data_files: "allava_laion/ALLaVA-Instruct-LAION-4V.json" # configs: # - config_name: default # data_files: # - split: allava_laion_caption # path: "allava_laion/ALLaVA-Caption-LAION-4V.json" # - split: allava_laion_instruction # path: "allava_laion/ALLaVA-Instruction-LAION-4V.json" # configs: # - config_name: default # - data_files: # - split: allava_laion_caption # - path: # - "allava_laion/ALLaVA-Caption-LAION-4V.json" # - split: allava_laion_instruction # - path: # - "allava_laion/ALLaVA-Instruction-LAION-4V.json" --- ## ALLaVA-4V for Chinese This is the Chinese version of the ALLaVA-4V data. We have translated the ALLaVA-4V data into Chinese through ChatGPT and instructed ChatGPT not to translate content related to OCR. The original dataset can be found [here](https://huggingface.co/datasets/FreedomIntelligence/ALLaVA-4V), and the image data can be downloaded from [ALLaVA-4V](https://huggingface.co/datasets/FreedomIntelligence/ALLaVA-4V). #### Citation If you find our data useful, please consider citing our work! We are FreedomIntelligence from Shenzhen Research Institute of Big Data and The Chinese University of Hong Kong, Shenzhen. ``` @misc{chen2024allava, title={ALLaVA: Harnessing GPT4V-synthesized Data for A Lite Vision-Language Model}, author={Guiming Hardy Chen and Shunian Chen and Ruifei Zhang and Junying Chen and Xiangbo Wu and Zhiyi Zhang and Zhihong Chen and Jianquan Li and Xiang Wan and Benyou Wang}, year={2024}, eprint={2402.11684}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

Kamran1367/Resume_Classificattion_Curated

Kamran1367

--- dataset_info: features: - name: Resume_str_cleaned dtype: string - name: Category dtype: string splits: - name: train num_bytes: 14301269 num_examples: 2484 download_size: 6769854 dataset_size: 14301269 configs: - config_name: default data_files: - split: train path: data/train-* --- This is a curated Resume Classification CSV file

methane69/finetuning_LLaVA

methane69

--- dataset_info: features: - name: image dtype: image - name: caption dtype: string splits: - name: train num_bytes: 8426655.0 num_examples: 138 - name: test num_bytes: 201798.0 num_examples: 3 download_size: 8321060 dataset_size: 8628453.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

Mitsuki-Sakamoto/alpaca_farm-reward-model-deberta-v3-large-v2-re-preference-64-nsample-16_random

Mitsuki-Sakamoto

--- dataset_info: - config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string splits: - name: preference num_bytes: 25791877 num_examples: 20001 download_size: 12310829 dataset_size: 25791877 - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string splits: - name: preference num_bytes: 25837484 num_examples: 20001 download_size: 12262392 dataset_size: 25837484 - config_name: alpaca_instructions-pythia_70m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string splits: - name: preference num_bytes: 25779381 num_examples: 20001 download_size: 11985077 dataset_size: 25779381 configs: - config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 data_files: - split: preference path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/preference-* - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: preference path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/preference-* - config_name: alpaca_instructions-pythia_70m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: preference path: alpaca_instructions-pythia_70m_alpaca_farm_instructions_sft_constant_pa_seed_1/preference-* ---

fede97/external_test_set_v1

fede97

--- dataset_info: features: - name: stable_unclip dtype: image - name: kandisky_2_2 dtype: image - name: multi_diffusion dtype: image - name: self_attention_guidance dtype: image - name: latent_consistency_xl dtype: image - name: kandisky_3 dtype: image - name: deepfloyd_if dtype: image - name: latent_consistency_model_simianluo dtype: image - name: amused dtype: image - name: stabilityai_stable_diffusion_2_1_base dtype: image - name: kandisky_2_1 dtype: image - name: sdxl_turbo dtype: image - name: stabilityai_stable_diffusion_xl_base_1_0 dtype: image - name: compvis_stable_diffusion_v1_4 dtype: image - name: pixart_alpha dtype: image - name: id dtype: string splits: - name: train num_bytes: 71299802571.0 num_examples: 4800 download_size: 71163208124 dataset_size: 71299802571.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

jbrinkma/pile-300k

jbrinkma

--- license: mit dataset_info: features: - name: text dtype: string - name: meta struct: - name: pile_set_name dtype: string splits: - name: train num_bytes: 1675060733 num_examples: 300000 download_size: 873058629 dataset_size: 1675060733 configs: - config_name: default data_files: - split: train path: data/train-* ---

dar-tau/test-test

dar-tau

--- dataset_info: features: - name: logit_loss dtype: float64 - name: log_kl_loss dtype: float64 - name: mask_weight dtype: float64 - name: probe_loss dtype: float64 - name: top_k_acc dtype: float64 - name: attention_maps sequence: float32 - name: attention_maps_shape sequence: int64 splits: - name: train num_bytes: 83472 num_examples: 3 download_size: 11666 dataset_size: 83472 configs: - config_name: default data_files: - split: train path: data/train-* ---

Omriy123/Dogs_vs_Cats

Omriy123

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': cat '1': dog splits: - name: train num_bytes: 525901830.0 num_examples: 25000 download_size: 573158859 dataset_size: 525901830.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

halitefe/lima-tr

halitefe

--- task_categories: - translation - text2text-generation - question-answering language: - tr size_categories: - 1K<n<10K --- # LIMA-TR This project is dedicated to translating the LIMA (Less Is More for Alignment) dataset from English to Turkish using OpenAI's API (`gpt-3.5-turbo`) ## Source Dataset LIMA (Less Is More for Alignment) can be found [paper-link](https://arxiv.org/pdf/2305.11206.pdf) [dataset-link](https://huggingface.co/datasets/GAIR/lima)

tldoan/iNF200

tldoan

--- license: apache-2.0 --- # A streamlined Approach to Multimodal Few-Shot Class Incremental Learning for Fine-Grained Datasets (CLIP-M3) Official Datasets iNaturalist Fungi200 (iNF200)

zliu333/truck_at_port5

zliu333

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 52878226.0 num_examples: 36 download_size: 52870667 dataset_size: 52878226.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

adorkin/evalatin2024

adorkin

--- license: mit task_categories: - text-classification language: - la size_categories: - 10K<n<100K configs: - config_name: default data_files: - split: gpt path: gpt4-turbo-annotations.jsonl - config_name: gpt data_files: - split: gpt path: gpt4-turbo-annotations.jsonl - config_name: heuristics data_files: - split: heuristics path: heuristics-annotations.jsonl --- # TartuNLP at EvaLatin 2024: Emotion Polarity Detection ## BibTeX entry and citation info ``` @inproceedings{dorkin-sirts-2024-tartunlp-evalatin, title = "{T}artu{NLP} at {E}va{L}atin 2024: Emotion Polarity Detection", author = "Dorkin, Aleksei and Sirts, Kairit", editor = "Sprugnoli, Rachele and Passarotti, Marco", booktitle = "Proceedings of the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) @ LREC-COLING-2024", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lt4hala-1.26", pages = "223--228", } ``` ``` @misc{dorkin2024tartunlp, title={TartuNLP at EvaLatin 2024: Emotion Polarity Detection}, author={Aleksei Dorkin and Kairit Sirts}, year={2024}, eprint={2405.01159}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

kovakavics/comfyuicuccaim

kovakavics

--- license: afl-3.0 ---

imperialwarrior/open-australian-legal-qa-paraphrased-moderation-results

imperialwarrior

--- dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: snippet dtype: string - name: question_retrieval_embeddings dtype: string - name: question_non_retrieval_embeddings dtype: string - name: answer_retrieval_embeddings dtype: string - name: answer_non_retrieval_embeddings dtype: string - name: snippet_retrieval_embeddings dtype: string - name: answer_moderation dtype: string - name: question_moderation dtype: string - name: snippet_moderation dtype: string splits: - name: train num_bytes: 509750534 num_examples: 2122 download_size: 176727859 dataset_size: 509750534 configs: - config_name: default data_files: - split: train path: data/train-* ---

mudassar93/data_piano

mudassar93

--- dataset_info: features: - name: response dtype: string - name: instruction dtype: string - name: chat dtype: string splits: - name: train num_bytes: 1080219 num_examples: 1823 download_size: 238567 dataset_size: 1080219 configs: - config_name: default data_files: - split: train path: data/train-* ---

AlexanderHolmes0/true-fake-news

AlexanderHolmes0

--- language: - en license: mit size_categories: - 10K<n<100K task_categories: - text-classification - question-answering - text-generation dataset_info: features: - name: label dtype: class_label: names: '0': 'true' '1': fake - name: text dtype: string splits: - name: train num_bytes: 82978144 num_examples: 33672 - name: test num_bytes: 28512596 num_examples: 11224 download_size: 67949019 dataset_size: 111490740 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* tags: - news --- # True-Fake-News <!-- Provide a quick summary of the dataset. --> These are collected news articles from various sources with curated labels aligning to `true` of `fake` classification. ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> The dataset contains two types of articles fake and real News. This dataset was collected from realworld sources; the truthful articles were obtained by crawling articles from Reuters.com (News website). As for the fake news articles, they were collected from different sources. The fake news articles were collected from unreliable websites that were flagged by Politifact (a fact-checking organization in the USA) and Wikipedia. The dataset contains different types of articles on different topics, however, the majority of articles focus on political and World news topics. ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [Kaggle Repo](https://www.kaggle.com/datasets/emineyetm/fake-news-detection-datasets/data) ## Uses <!-- Address questions around how the dataset is intended to be used. --> Text classification or question answering would be ways to use this dataset. ## Dataset Structure | Classification | Total Number of Articles | Article Type | Article Count | |----------------|--------------------------|--------------|---------------| | Real-News | 21,417 | World | 10,145 | | | | Political | 11,272 | | Fake-News | 23,481 | Government | 1,570 | | | | Middle East | 778 | | | | US | 783 | | | | Left-Leaning | 4,459 | | | | Political | 6,841 | | | | General | 9,050 |

HiTZ/basqueparl

HiTZ

--- license: apache-2.0 task_categories: - text-classification - token-classification - summarization - translation - zero-shot-classification - text-generation language: - es - eu tags: - politics - parliamentary - code switching - multilinguality pretty_name: BasqueParl size_categories: - 100K<n<1M source_datasets: - original multilinguality: - multilingual paperswithcode_id: basqueparl --- # BasqueParl: A Bilingual Corpus of Basque Parliamentary Transcriptions This repository contains **BasqueParl**, a bilingual corpus for political discourse analysis. It covers transcriptions from the Parliament of the Basque Autonomous Community for eight years and two legislative terms (2012-2020), and its main characteristic is the presence of Basque-Spanish code-switching speeches. 📖 Paper: [BasqueParl A Bilingual Corpus of Basque Parliamentary Transcriptions](https://aclanthology.org/2022.lrec-1.361/) In LREC 2022. ## Example The following unprocessed speech combines a **Basque** text (plain) with **Spanish** fragments (highlighted): > Bai, zure baimenarekin hemendik. > > Ba zure desioak, Guanche andrea, gureak ere badira. Harritu nau eta ez nau harritu hitza berriro hartzeak, zeren hitz egiten nengoen bitartean esan diozu albokoari `le voy a contestar. Le voy a contestar`, ondo iruditzen, zure eskubidean zaude, baino beno, ez dut uste inongo astakeriarik esan dudanik. > > Gauzak egiten dira eta uste dut nik, nik ere eskubidea dudala Gobernuak eta beste erakundeek egiten dutena esateko. Zeren beti `ver el vaso medio vacío o medio lleno, pues cambia un poco la perspectiva y vernos siempre en modo Gobierno, creo que no es nada objetivo. Se hacen cosas, se harán cosas y esta vez creo que me deberían reconocer que de la iniciativa primera a lo que hemos acordado, no nos hemos dejado nada o creo que casi nada. Entonces, bueno, sólo querı́a aclarar eso` eta eskerrak berriro. > > Eta ziur egon emakumea dokumentu horietan ez bada agertzen hitzetan, zeren uste dut hori ez dela garrantzitsuena, bai politiketan egongo dela eta dagoela. > > Eskerrik asko. ## Description The specificities of the **BasqueParl** corpus are: - **14 M words** of bilingual parliamentary transcriptions - **Speech paragraphs** as units - Metadata such as **date** and **speaker's name**, **gender** and **party** for each paragraph - **Language** of each paragraph (either Basque or Spanish) - **Lemmas** and **named entities** of each paragraph, with and without stopwords ## Data Fields The **BasqueParl** corpus is written as a Tab Separated Values (TSV) file. Each unit presents the next fields: - **"date"**: Date corresponding to the speech, e.g. _2020-02-07_ - **"speech_id"**: Number that identifies the speech within its date, e.g. _3_ - **"text_id"**: Number that identifies the paragraph within its speech, e.g. _3_ - **"speaker"**: Family names of the speaker, including their position if any, e.g. _Tejeria Otermin LEHENDAKARIA_ - **"birth"**: Year of birth of the speaker, e.g. _1971_ - **"gender"**: Gender of the speaker, either _E_ (_emakumea_) for female or _G_ (_gizona_) for male - **"party"**: Political group of the speaker, e.g. _EAJ_ - **"language"**: Language assigned to a paragraph, either _eu_ for Basque or _es_ for Spanish - **"text"**: Paragraph of the speech text - **"lemmas"**: Lemmatized paragraph - **"lemmas_stw"**: Lemmatized paragraph without stopwords - **"entities"**: Named entities extracted from the paragraph - **"entities_stw"**: Named entities extracted from the paragraph without stopwords Some fields such as gender or party may have been sometimes annotated as missing if the data was impossible to retrieve or was not appropriate. # Citation ````bibtex @inproceedings{escribano-etal-2022-basqueparl, title = "{B}asque{P}arl: A Bilingual Corpus of {B}asque Parliamentary Transcriptions", author = "Escribano, Nayla and Gonzalez, Jon Ander and Orbegozo-Terradillos, Julen and Larrondo-Ureta, Ainara and Pe{\~n}a-Fern{\'a}ndez, Sim{\'o}n and Perez-de-Vi{\~n}aspre, Olatz and Agerri, Rodrigo", booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference (LREC)", year = "2022", publisher = "European Language Resources Association", pages = "3382--3390" } ```` # Contact [Rodrigo Agerri](https://ragerri.github.io/) HiTZ Center - Ixa, University of the Basque Country UPV/EHU

apollo-research/monology-pile-uncopyrighted-tokenizer-EleutherAI-gpt-neox-20b

apollo-research

--- dataset_info: features: - name: input_ids sequence: int32 splits: - name: train num_bytes: 168975915696.0 num_examples: 20616876 download_size: 71503236187 dataset_size: 168975915696.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

dar-tau/grammar-attention-maps-opt-350m

dar-tau

--- dataset_info: features: - name: text dtype: string - name: logit_loss dtype: float64 - name: log_kl_loss dtype: float64 - name: mask_weight dtype: float64 - name: probe_loss dtype: float64 - name: top_k_acc dtype: float64 - name: attention_maps sequence: float32 - name: attention_maps_shape sequence: int64 splits: - name: train num_bytes: 3868218 num_examples: 95 download_size: 703054 dataset_size: 3868218 configs: - config_name: default data_files: - split: train path: data/train-* ---

taylorbollman/wikitext2_tb

taylorbollman

--- dataset_info: features: - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: test num_bytes: 3963136 num_examples: 2192 - name: train num_bytes: 33513088 num_examples: 18536 - name: validation num_bytes: 3467744 num_examples: 1918 download_size: 11981141 dataset_size: 40943968 configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* - split: validation path: data/validation-* ---

saadalafalcon/officialEmails

saadalafalcon

--- license: apache-2.0 ---

Weni/WeniGPT-QA-1.0.1_DPO

Weni

--- language: - pt dataset_info: features: - name: context dtype: string - name: question dtype: string - name: chosen_response dtype: string - name: rejected_response dtype: string - name: correct_ans dtype: int64 - name: flag_type dtype: int64 splits: - name: pt num_bytes: 27689890 num_examples: 3180 download_size: 14905751 dataset_size: 27689890 configs: - config_name: default data_files: - split: pt path: data/pt-* ---

chagasclone/agrecivo

chagasclone

--- license: openrail ---

allandclive/alpaca_4k_luganda

allandclive

--- task_categories: - text2text-generation - text-generation language: - lg --- # Alpaca 4k Luganda Translated using Google Translate

hon9kon9ize/yue-toxic-dpo

hon9kon9ize

--- dataset_info: - config_name: yue features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: id dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 779960 num_examples: 531 download_size: 441753 dataset_size: 779960 - config_name: zh features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: id dtype: string splits: - name: train num_bytes: 825175 num_examples: 541 download_size: 472938 dataset_size: 825175 configs: - config_name: yue data_files: - split: train path: yue/train-* - config_name: zh data_files: - split: train path: zh/train-* tags: - not-for-all-audiences --- # Cantonese Toxic DPO v0.2 This dataset is a Cantonese and Simplified Chinese translation of [unalignment/toxic-dpo-v0.2](https://huggingface.co/datasets/unalignment/toxic-dpo-v0.2). For more detailed information about the original dataset, please refer to the provided link. This dataset is translated by Gemini Pro and has not undergone any manual verification. The content may be inaccurate or misleading. please keep this in mind when using this dataset. ## License This dataset is provided under the same license as the original dataset: CC BY 4.0 ## Limitation and Usage Limits Please check the original dataset for more information.

Rohit-D/synthetic-confidential-information-injected-business-excerpts

Rohit-D

--- license: mit task_categories: - question-answering - text-classification - feature-extraction - summarization language: - en tags: - business - fine-tuning size_categories: - n<=1K --- ## Synthetic Confidential Information Injected Business Excerpts This dataset aims to provide business report excerpts which contain relevant confidential/sensitive information. <pre> This includes mentions of : 1. Internal Marketing Strategies. 2. Proprietary Product Composition. 3. License Internals. 4. Internal Sales Projections. 5. Confidential Patent Details. 6. others. </pre> The dataset contains around 1k business excerpt - Reasons pairs. The Reason field contains the confidential portion from the business excerpt field in quotes and also reasons succintly (in about a line) as to why the quoted portion might be confidential. **Note** : All 'confidential information' injected is purely artifical and the business excerpts themselves along with companies, products, numbers, licenses, patents they reference or mention are hypothetical and artificial. This data is to be treated as pure simulation of what leaks in business excerpts might look like. This data does not contain or intend to provide any kind of actual/real cases of confidential information.

ravithejads/alpaca-cleaned-tagged

ravithejads

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: type dtype: string splits: - name: train num_bytes: 42276800 num_examples: 51760 download_size: 24347133 dataset_size: 42276800 configs: - config_name: default data_files: - split: train path: data/train-* ---

thafiz/llm-crossfit

thafiz

--- dataset_info: features: - name: input_ids sequence: int32 splits: - name: train num_bytes: 319644.0 num_examples: 39 - name: test num_bytes: 40980.0 num_examples: 5 download_size: 199220 dataset_size: 360624.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

fraviofranco/vozcortella

fraviofranco

--- license: openrail ---

dongyoung4091/hh-rlhf_with_features_flan_t5_large

dongyoung4091

--- dataset_info: features: - name: chosen dtype: string - name: rejected dtype: string - name: helpfulness_chosen dtype: int64 - name: helpfulness_rejected dtype: int64 - name: specificity_chosen dtype: int64 - name: specificity_rejected dtype: int64 - name: intent_chosen dtype: int64 - name: intent_rejected dtype: int64 - name: factuality_chosen dtype: int64 - name: factuality_rejected dtype: int64 - name: easy-to-understand_chosen dtype: int64 - name: easy-to-understand_rejected dtype: int64 - name: relevance_chosen dtype: int64 - name: relevance_rejected dtype: int64 - name: readability_chosen dtype: int64 - name: readability_rejected dtype: int64 - name: enough-detail_chosen dtype: int64 - name: enough-detail_rejected dtype: int64 - name: biased:_chosen dtype: int64 - name: biased:_rejected dtype: int64 - name: fail-to-consider-individual-preferences_chosen dtype: int64 - name: fail-to-consider-individual-preferences_rejected dtype: int64 - name: repetetive_chosen dtype: int64 - name: repetetive_rejected dtype: int64 - name: fail-to-consider-context_chosen dtype: int64 - name: fail-to-consider-context_rejected dtype: int64 - name: too-long_chosen dtype: int64 - name: too-long_rejected dtype: int64 - name: human dtype: string - name: assistant_chosen dtype: string - name: assistant_rejected dtype: string - name: log_score_chosen dtype: float64 - name: log_score_rejected dtype: float64 - name: labels dtype: string splits: - name: train num_bytes: 14434424 num_examples: 9574 - name: test num_bytes: 14378349 num_examples: 9574 download_size: 15748504 dataset_size: 28812773 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

dongyoung4091/hh-rlhf_with_features

dongyoung4091

--- dataset_info: features: - name: chosen dtype: string - name: rejected dtype: string - name: helpfulness_chosen dtype: int64 - name: helpfulness_rejected dtype: int64 - name: specificity_chosen dtype: int64 - name: specificity_rejected dtype: int64 - name: intent_chosen dtype: int64 - name: intent_rejected dtype: int64 - name: factuality_chosen dtype: int64 - name: factuality_rejected dtype: int64 - name: easy-to-understand_chosen dtype: int64 - name: easy-to-understand_rejected dtype: int64 - name: relevance_chosen dtype: int64 - name: relevance_rejected dtype: int64 - name: readability_chosen dtype: int64 - name: readability_rejected dtype: int64 - name: enough-detail_chosen dtype: int64 - name: enough-detail_rejected dtype: int64 - name: biased:_chosen dtype: int64 - name: biased:_rejected dtype: int64 - name: fail-to-consider-individual-preferences_chosen dtype: int64 - name: fail-to-consider-individual-preferences_rejected dtype: int64 - name: repetetive_chosen dtype: int64 - name: repetetive_rejected dtype: int64 - name: fail-to-consider-context_chosen dtype: int64 - name: fail-to-consider-context_rejected dtype: int64 - name: too-long_chosen dtype: int64 - name: too-long_rejected dtype: int64 - name: human dtype: string - name: assistant_chosen dtype: string - name: assistant_rejected dtype: string - name: labels dtype: string splits: - name: train num_bytes: 14281240 num_examples: 9574 - name: test num_bytes: 14225165 num_examples: 9574 download_size: 15456243 dataset_size: 28506405 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

dongyoung4091/shp_with_features_20k_flan_t5_large

dongyoung4091

--- dataset_info: features: - name: post_id dtype: string - name: domain dtype: string - name: upvote_ratio dtype: float64 - name: history dtype: string - name: c_root_id_A dtype: string - name: c_root_id_B dtype: string - name: created_at_utc_A dtype: int64 - name: created_at_utc_B dtype: int64 - name: score_A dtype: int64 - name: score_B dtype: int64 - name: human_ref_A dtype: string - name: human_ref_B dtype: string - name: labels dtype: int64 - name: seconds_difference dtype: float64 - name: score_ratio dtype: float64 - name: helpfulness_A dtype: float64 - name: helpfulness_B dtype: float64 - name: specificity_A dtype: float64 - name: specificity_B dtype: float64 - name: intent_A dtype: float64 - name: intent_B dtype: float64 - name: factuality_A dtype: float64 - name: factuality_B dtype: float64 - name: easy-to-understand_A dtype: float64 - name: easy-to-understand_B dtype: float64 - name: relevance_A dtype: float64 - name: relevance_B dtype: float64 - name: readability_A dtype: float64 - name: readability_B dtype: float64 - name: enough-detail_A dtype: float64 - name: enough-detail_B dtype: float64 - name: biased:_A dtype: float64 - name: biased:_B dtype: float64 - name: fail-to-consider-individual-preferences_A dtype: float64 - name: fail-to-consider-individual-preferences_B dtype: float64 - name: repetetive_A dtype: float64 - name: repetetive_B dtype: float64 - name: fail-to-consider-context_A dtype: float64 - name: fail-to-consider-context_B dtype: float64 - name: too-long_A dtype: float64 - name: too-long_B dtype: float64 - name: __index_level_0__ dtype: int64 - name: log_score_A dtype: float64 - name: log_score_B dtype: float64 splits: - name: train num_bytes: 20707062 num_examples: 9459 - name: test num_bytes: 20659940 num_examples: 9459 download_size: 23927350 dataset_size: 41367002 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

dongyoung4091/hh-generated_flan_t5_large_with_features2

dongyoung4091

--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: 'biased:' dtype: int64 - name: easy-to-understand dtype: int64 - name: enough-detail dtype: int64 - name: factuality dtype: int64 - name: fail-to-consider-context dtype: int64 - name: fail-to-consider-individual-preferences dtype: int64 - name: helpfulness dtype: int64 - name: intent dtype: int64 - name: readability dtype: int64 - name: relevance dtype: int64 - name: repetetive dtype: int64 - name: specificity dtype: int64 - name: too-long dtype: int64 splits: - name: train num_bytes: 395323 num_examples: 1600 download_size: 76218 dataset_size: 395323 configs: - config_name: default data_files: - split: train path: data/train-* ---

jtatman/medical-sci-instruct-100k-sharegpt-chatml

jtatman

--- dataset_info: features: - name: text dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 1071589710 num_examples: 96557 download_size: 82474918 dataset_size: 1071589710 configs: - config_name: default data_files: - split: train path: data/train-* license: mit size_categories: - 10K<n<100K --- This is a reformat of the database formatted for Mistral training with chatml tokens added to the tokenizer, and '<|endoftext|>' as an end of sequence token. The max_length setting may need to be adjusted to include the additional tokens for training. This max_length was set to 2030 from 2048 for extra headroom.

gagan3012/arabic-xnli-pairwise

gagan3012

--- dataset_info: features: - name: labels sequence: int64 - name: sent1 sequence: string - name: sent2 sequence: string splits: - name: train num_bytes: 70811123 num_examples: 1 - name: test num_bytes: 850605 num_examples: 1 - name: validation num_bytes: 415074 num_examples: 1 download_size: 37859272 dataset_size: 72076802 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---

dongyoung4091/shp_with_features_20k

dongyoung4091

--- dataset_info: features: - name: post_id dtype: string - name: domain dtype: string - name: upvote_ratio dtype: float64 - name: history dtype: string - name: c_root_id_A dtype: string - name: c_root_id_B dtype: string - name: created_at_utc_A dtype: int64 - name: created_at_utc_B dtype: int64 - name: score_A dtype: int64 - name: score_B dtype: int64 - name: human_ref_A dtype: string - name: human_ref_B dtype: string - name: labels dtype: int64 - name: seconds_difference dtype: float64 - name: score_ratio dtype: float64 - name: helpfulness_A dtype: float64 - name: helpfulness_B dtype: float64 - name: specificity_A dtype: float64 - name: specificity_B dtype: float64 - name: intent_A dtype: float64 - name: intent_B dtype: float64 - name: factuality_A dtype: float64 - name: factuality_B dtype: float64 - name: easy-to-understand_A dtype: float64 - name: easy-to-understand_B dtype: float64 - name: relevance_A dtype: float64 - name: relevance_B dtype: float64 - name: readability_A dtype: float64 - name: readability_B dtype: float64 - name: enough-detail_A dtype: float64 - name: enough-detail_B dtype: float64 - name: biased:_A dtype: float64 - name: biased:_B dtype: float64 - name: fail-to-consider-individual-preferences_A dtype: float64 - name: fail-to-consider-individual-preferences_B dtype: float64 - name: repetetive_A dtype: float64 - name: repetetive_B dtype: float64 - name: fail-to-consider-context_A dtype: float64 - name: fail-to-consider-context_B dtype: float64 - name: too-long_A dtype: float64 - name: too-long_B dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 20555718 num_examples: 9459 - name: test num_bytes: 20508596 num_examples: 9459 download_size: 23638147 dataset_size: 41064314 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

felipesampaio2010/clarestaravenska

felipesampaio2010

--- license: openrail ---

ResplendentAI/Alpaca_NSFW_Shuffled

ResplendentAI

--- license: cc-by-nc-4.0 language: - en tags: - not-for-all-audiences pretty_name: Alpaca NSFW Shuffled size_categories: - n<1K --- Reformatted and pruned this dataset: https://huggingface.co/datasets/athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW-v1-SHUFFLED

ZHLiu627/ultrafeedback_binarized_with_response_full

ZHLiu627

--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: score_chosen dtype: float64 - name: score_rejected dtype: float64 - name: reference_response dtype: string splits: - name: train_prefs num_bytes: 510824465 num_examples: 61135 download_size: 0 dataset_size: 510824465 configs: - config_name: default data_files: - split: train_prefs path: data/train_prefs-* --- # Dataset Card for "ultrafeedback_binarized_with_response_full" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

vinisebk/jc_chasez

vinisebk

--- license: openrail ---

gagan3012/arabic-sts-pairwise

gagan3012

--- dataset_info: features: - name: labels sequence: int64 - name: sent1 sequence: string - name: sent2 sequence: string splits: - name: train num_bytes: 227137 num_examples: 1 - name: validation num_bytes: 63521 num_examples: 1 - name: test num_bytes: 33531 num_examples: 1 download_size: 182982 dataset_size: 324189 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

gagan3012/arabic-mq2q-pairwise

gagan3012

--- dataset_info: features: - name: labels sequence: int64 - name: sent1 sequence: string - name: sent2 sequence: string splits: - name: train num_bytes: 1193021 num_examples: 1 - name: validation num_bytes: 150359 num_examples: 1 - name: test num_bytes: 148942 num_examples: 1 download_size: 523830 dataset_size: 1492322 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

luzDP/Thiago_Minos

luzDP

--- license: openrail ---

gagan3012/arabic-ans-stance-pairwise

gagan3012

--- dataset_info: features: - name: labels sequence: int64 - name: sent1 sequence: string - name: sent2 sequence: string splits: - name: train num_bytes: 511126 num_examples: 1 - name: validation num_bytes: 147950 num_examples: 1 - name: test num_bytes: 73556 num_examples: 1 download_size: 296560 dataset_size: 732632 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

johnsonkuan/wiki_en_chunks_sample

johnsonkuan

--- dataset_info: features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: chunk dtype: string - name: chunk_seq dtype: int64 - name: chunk_md5 dtype: string splits: - name: train num_bytes: 2882990493 num_examples: 6019103 download_size: 1736043605 dataset_size: 2882990493 configs: - config_name: default data_files: - split: train path: data/train-* ---

stanmalkinson199/2Ddattaset

stanmalkinson199

--- license: openrail ---

CronosGhost/cpp-code-reranking

CronosGhost

--- dataset_info: features: - name: query dtype: string - name: positive sequence: string - name: negative sequence: string splits: - name: train num_bytes: 23231663.1 num_examples: 9900 - name: test num_bytes: 2581295.9 num_examples: 1100 download_size: 10424834 dataset_size: 25812959.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

bulkbeings/emma_assistant_conversations_v0.1

bulkbeings

--- license: mit ---

goatman/metahuman-gaze-prediction

goatman

--- license: apache-2.0 --- #Extract and normalize the coordinates (dodgy version for testing) def get_coords_metahuman(file: Path): im_id, character, xcoord, ycoord, xsize, ysize = file.name.split('.jpg')[:-1][0].split('_') xcoord, ycoord, xsize, ysize = float(xcoord), float(ycoord), float(xsize), float(ysize) base_screensize = tensor([46.49, 26.15]) # generic width and height measurement in cms given by gpt4 as a likely mean screen size normalized_screensize = tensor([xsize, ysize])/base_screensize x = (xcoord)/xsize y = (ycoord)/ysize # normalize to range -0.5, 0.5 return tensor([x, y])

RomilsonB/henryfreitas

RomilsonB

--- license: openrail ---

RomilsonB/henry

RomilsonB

--- license: openrail ---

youlive789/instructpix2pix

youlive789

--- license: mit dataset_info: features: - name: original_image dtype: image - name: edited_image dtype: image - name: edit_promt dtype: string splits: - name: train num_bytes: 2478786161.568 num_examples: 2904 download_size: 2239120930 dataset_size: 2478786161.568 configs: - config_name: default data_files: - split: train path: data/train-* ---

angeluriot/DimensionGPT_instruct

angeluriot

--- configs: - config_name: human_conversations data_files: human_conversations.json - config_name: chatbot_conversations data_files: chatbot_conversations.json - config_name: dimension_gpt_conversations data_files: dimension_gpt_conversations.json - config_name: human_preprompts data_files: human_preprompts.json - config_name: chatbot_preprompts data_files: chatbot_preprompts.json - config_name: dimension_gpt_preprompts data_files: dimension_gpt_preprompts.json ---

Aeronsc00ll0l/Smth

Aeronsc00ll0l

--- license: apache-2.0 ---

Vinnyh589/Chaves8

Vinnyh589

--- license: unknown ---

lapp0/hotpot_query_expansion_synthetic_annotated

lapp0

--- dataset_info: features: - name: input dtype: string - name: output dtype: string - name: input_entities sequence: string - name: output_entities sequence: string - name: out_in_ent_score dtype: float64 - name: in_out_ent_score dtype: float64 - name: pair_score dtype: float32 splits: - name: train num_bytes: 27024967 num_examples: 85925 - name: eval num_bytes: 1418300 num_examples: 4522 download_size: 19029050 dataset_size: 28443267 configs: - config_name: default data_files: - split: train path: data/train-* - split: eval path: data/eval-* ---

sunilrufus/Extes_filtered1

sunilrufus

--- dataset_info: features: - name: scene dtype: string - name: description dtype: string - name: content dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 10425199 num_examples: 2864 - name: test num_bytes: 2613907 num_examples: 717 download_size: 5883920 dataset_size: 13039106 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

Manirathinam21/Resume_classification

Manirathinam21

--- license: mit task_categories: - text-classification language: - en size_categories: - n<1K ---

citibankdemobusiness/worldsrecord

citibankdemobusiness

--- license: other license_name: billionaire license_link: https://github.com/CitibankDemoBusiness/billiondollars/blob/git/LICENSE ---

pgajo/subs-v2

pgajo

--- dataset_info: features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 5367257273.704724 num_examples: 79191 - name: test num_bytes: 579733939.4022752 num_examples: 8800 download_size: 5812185768 dataset_size: 5946991213.106999 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

FreedomIntelligence/ALLaVA-4V-Arabic

FreedomIntelligence

--- license: apache-2.0 task_categories: - question-answering - text-generation language: - ar tags: - GPT-4V - LVLM - Vision - Language size_categories: - 1M<n<10M configs: - config_name: allava_laion data_files: - split: caption path: "allava_laion/ALLaVA-Caption-LAION-4V_Arabic.json" # - split: instruct # path: "allava_laion/ALLaVA-Instruct-LAION-4V_Chinese.json" - config_name: allava_vflan data_files: - split: caption path: "allava_vflan/ALLaVA-Caption-VFLAN-4V_Arabic.json" # - split: instruct # path: "allava_vflan/ALLaVA-Instruct-VFLAN-4V_Chinese.json" # - config_name: allava_laion_instruction # data_files: "allava_laion/ALLaVA-Instruct-LAION-4V.json" # configs: # - config_name: default # data_files: # - split: allava_laion_caption # path: "allava_laion/ALLaVA-Caption-LAION-4V.json" # - split: allava_laion_instruction # path: "allava_laion/ALLaVA-Instruction-LAION-4V.json" # configs: # - config_name: default # - data_files: # - split: allava_laion_caption # - path: # - "allava_laion/ALLaVA-Caption-LAION-4V.json" # - split: allava_laion_instruction # - path: # - "allava_laion/ALLaVA-Instruction-LAION-4V.json" --- ## ALLaVA-4V for Arabic This is the Arabic version of the ALLaVA-4V data. We have translated the ALLaVA-4V data into Arabic through ChatGPT and instructed ChatGPT not to translate content related to OCR. The original dataset can be found [here](https://huggingface.co/datasets/FreedomIntelligence/ALLaVA-4V), and the image data can be downloaded from [ALLaVA-4V](https://huggingface.co/datasets/FreedomIntelligence/ALLaVA-4V). #### Citation If you find our data useful, please consider citing our work! We are FreedomIntelligence from Shenzhen Research Institute of Big Data and The Chinese University of Hong Kong, Shenzhen. ``` @misc{chen2024allava, title={ALLaVA: Harnessing GPT4V-synthesized Data for A Lite Vision-Language Model}, author={Guiming Hardy Chen and Shunian Chen and Ruifei Zhang and Junying Chen and Xiangbo Wu and Zhiyi Zhang and Zhihong Chen and Jianquan Li and Xiang Wan and Benyou Wang}, year={2024}, eprint={2402.11684}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

hanesh007/mtsample

hanesh007

--- license: apache-2.0 ---

lapp0/hotpot_query_expansion_synthetic_cleaned

lapp0

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 4908240 num_examples: 25593 - name: eval num_bytes: 264342 num_examples: 1359 download_size: 3390694 dataset_size: 5172582 configs: - config_name: default data_files: - split: train path: data/train-* - split: eval path: data/eval-* ---

arafatar/details_harness_drop

arafatar

--- license: unknown ---

FreezySandy/Chat_doc

FreezySandy

--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 1061539 num_examples: 1000 download_size: 645906 dataset_size: 1061539 configs: - config_name: default data_files: - split: train path: data/train-* ---

imperialwarrior/open-australian-legal-qa-paraphrased-hard-gpt-with-emb

imperialwarrior

--- dataset_info: features: - name: pipeline_1_result dtype: string - name: pipeline_1_result_r_embeddings sequence: float64 - name: pipeline_1_result_nr_embeddings sequence: float64 - name: pipeline_2_context dtype: string - name: pipeline_2_result dtype: string - name: pipeline_2_result_r_embeddings sequence: float64 - name: pipeline_2_result_nr_embeddings sequence: float64 - name: pipeline_3_context dtype: string - name: pipeline_3_result dtype: string - name: pipeline_3_result_r_embeddings sequence: float64 - name: pipeline_3_result_nr_embeddings sequence: float64 - name: pipeline_4_context dtype: string - name: pipeline_4_result dtype: string - name: pipeline_4_result_r_embeddings sequence: float64 - name: pipeline_4_result_nr_embeddings sequence: float64 - name: pipeline_5_context dtype: string - name: pipeline_5_result dtype: string - name: pipeline_5_result_r_embeddings sequence: float64 - name: pipeline_5_result_nr_embeddings sequence: float64 - name: pipeline_6_context dtype: string - name: pipeline_6_result dtype: string - name: pipeline_6_result_r_embeddings sequence: float64 - name: pipeline_6_result_nr_embeddings sequence: float64 - name: pipeline_7_context dtype: string - name: pipeline_7_result dtype: string - name: pipeline_7_result_r_embeddings sequence: float64 - name: pipeline_7_result_nr_embeddings sequence: float64 - name: referenced_question dtype: string - name: answer dtype: string - name: answer_non_retrieval_embeddings dtype: string - name: answer_retrieval_embeddings dtype: string - name: question dtype: string - name: question_retrieval_embeddings dtype: string - name: question_non_retrieval_embeddings dtype: string - name: __index_level_0__ dtype: float64 - name: case_index dtype: float64 - name: pipeline_6_case_indexes sequence: int64 - name: pipeline_7_case_indexes sequence: int64 splits: - name: train num_bytes: 138068314 num_examples: 208 download_size: 33205125 dataset_size: 138068314 configs: - config_name: default data_files: - split: train path: data/train-* ---

imperialwarrior/open-australian-legal-qa-paraphrased-easy-gpt-with-emb

imperialwarrior

--- dataset_info: features: - name: pipeline_1_result dtype: string - name: pipeline_1_result_r_embeddings sequence: float64 - name: pipeline_1_result_nr_embeddings sequence: float64 - name: pipeline_2_context dtype: string - name: pipeline_2_result dtype: string - name: pipeline_2_result_r_embeddings sequence: float64 - name: pipeline_2_result_nr_embeddings sequence: float64 - name: pipeline_3_context dtype: string - name: pipeline_3_result dtype: string - name: pipeline_3_result_r_embeddings sequence: float64 - name: pipeline_3_result_nr_embeddings sequence: float64 - name: pipeline_4_context dtype: string - name: pipeline_4_result dtype: string - name: pipeline_4_result_r_embeddings sequence: float64 - name: pipeline_4_result_nr_embeddings sequence: float64 - name: pipeline_5_context dtype: string - name: pipeline_5_result dtype: string - name: pipeline_5_result_r_embeddings sequence: float64 - name: pipeline_5_result_nr_embeddings sequence: float64 - name: pipeline_6_context dtype: string - name: pipeline_6_result dtype: string - name: pipeline_6_result_r_embeddings sequence: float64 - name: pipeline_6_result_nr_embeddings sequence: float64 - name: pipeline_7_context dtype: string - name: pipeline_7_result dtype: string - name: pipeline_7_result_r_embeddings sequence: float64 - name: pipeline_7_result_nr_embeddings sequence: float64 - name: referenced_question dtype: string - name: answer dtype: string - name: answer_non_retrieval_embeddings dtype: string - name: answer_retrieval_embeddings dtype: string - name: question dtype: string - name: question_retrieval_embeddings dtype: string - name: question_non_retrieval_embeddings dtype: string - name: __index_level_0__ dtype: float64 - name: case_index dtype: float64 - name: pipeline_6_case_indexes sequence: int64 - name: pipeline_7_case_indexes sequence: int64 splits: - name: train num_bytes: 137944644 num_examples: 208 download_size: 32779364 dataset_size: 137944644 configs: - config_name: default data_files: - split: train path: data/train-* ---

Prajwal3009/Gemma_unisys

Prajwal3009

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 295341 num_examples: 1267 download_size: 95471 dataset_size: 295341 configs: - config_name: default data_files: - split: train path: data/train-* ---

jjpetrisko/authentiface_v1.0

jjpetrisko

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': fake '1': real splits: - name: train num_bytes: 3488320382.368 num_examples: 68832 - name: validation num_bytes: 525649096.534 num_examples: 9862 - name: test num_bytes: 1033989495.113 num_examples: 19581 download_size: 5045508970 dataset_size: 5047958974.015 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

oneseco-media/djscrew-dataset

oneseco-media

--- license: artistic-2.0 task_categories: - table-question-answering - question-answering - text-classification tags: - music pretty_name: DJScrewBookofChapters size_categories: - n<1K ---

peterandrew987/dev-indo-tydiaqa

peterandrew987

--- dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers struct: - name: answer_start dtype: int64 - name: text dtype: string - name: indonesian_answers struct: - name: answer_start dtype: int64 - name: text dtype: string - name: postags sequence: sequence: sequence: string splits: - name: train num_bytes: 513862 num_examples: 565 download_size: 284921 dataset_size: 513862 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "dev-indo-tydiaqa" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

botchagalupe/opencontext

botchagalupe

--- license: cc-by-4.0 dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 52445 num_examples: 1413 download_size: 20530 dataset_size: 52445 configs: - config_name: default data_files: - split: train path: data/train-* ---

RomilsonB/henryfreitasss

RomilsonB

--- license: openrail ---

boapps/jowiki-qa

boapps

--- license: cc-by-sa-3.0 task_categories: - question-answering language: - hu size_categories: - 10K<n<100K --- A [jowiki](https://huggingface.co/datasets/boapps/jowiki) korpusz cikkeiből válogattam részeket, amikhez `gemini-pro`-val generáltattam egy kérdést és választ. Ez szerintem hasznos lehet például RAG-ok embedding részének tanításához.

Thunder-rk/stories-t5-1

Thunder-rk

--- dataset_info: features: - name: prompt dtype: string - name: answer dtype: string splits: - name: train num_bytes: 2951011.851190476 num_examples: 1999 - name: test num_bytes: 1265141.1488095238 num_examples: 857 download_size: 1741551 dataset_size: 4216153.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

theojiang/image-text-dataset-subset-300k-captions_only_with_latents

theojiang

--- dataset_info: features: - name: image dtype: image - name: caption dtype: string - name: CLIP_text_latent sequence: float32 - name: SD_VAE_image_latent sequence: sequence: sequence: float32 splits: - name: train num_bytes: 57507528731.75 num_examples: 380530 download_size: 60531502833 dataset_size: 57507528731.75 configs: - config_name: default data_files: - split: train path: data/train-* ---