davanstrien/embedded-cards · Datasets at Hugging Face

Hundred9/Duaaii_5

Hundred9

2023-03-06T20:49:30Z

21

0

null

[ "region:us" ]

2023-03-06T20:49:30Z

2023-03-06T20:45:57.000Z

2023-03-06T20:45:57

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' '5': '5' '6': '6' splits: - name: train num_bytes: 43153426.0 num_examples: 647 download_size: 43177352 dataset_size: 43153426.0 --- # Dataset Card for "Duaaii_5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.7244714498519897, -0.17119766771793365, 0.27720755338668823, 0.45947811007499695, -0.2808840274810791, -0.13915391266345978, 0.6346103549003601, -0.10694564133882523, 0.6986706852912903, 0.5354007482528687, -0.8035892248153687, -0.8658046722412109, -0.4942859709262848, 0.089158467948436...

null

reginaboateng/pico_ebmnlp

reginaboateng

2023-03-20T14:02:22Z

21

0

null

[ "region:us" ]

2023-03-20T14:02:22Z

2023-03-20T14:00:47.000Z

2023-03-20T14:00:47

--- dataset_info: features: - name: tokens sequence: string - name: chunk_tags sequence: string - name: pos_tags sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': I-INT '2': I-OUT '3': I-PAR splits: - name: train num_bytes: 27639457 num_examples: 23952 - name: test num_bytes: 1482730 num_examples: 2064 - name: validation num_bytes: 7446993 num_examples: 7049 download_size: 4096177 dataset_size: 36569180 --- # Dataset Card for "pico_ebmnlp" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.6175578832626343, -0.19674836099147797, 0.02218007668852806, 0.2749638259410858, -0.3702279031276703, -0.08248158544301987, 0.40855416655540466, -0.391313374042511, 1.0291647911071777, 0.3355581760406494, -0.755979061126709, -0.7885810136795044, -0.5184974670410156, -0.21711298823356628...

null

HiTZ/alpaca_mt

HiTZ

2023-04-07T15:15:55Z

21

7

null

[ "task_categories:text-generation", "task_ids:dialogue-modeling", "annotations_creators:no-annotation", "language_creators:machine-generated", "multilinguality:multilingual", "multilinguality:translation", "size_categories:10K<n<100K", "source_datasets:tatsu-lab/alpaca", "language:en", "language:pt...

2023-04-07T15:15:55Z

2023-03-22T15:27:30.000Z

2023-03-22T15:27:30

--- annotations_creators: - no-annotation language: - en - pt - es - ca - eu - gl - at language_creators: - machine-generated license: cc-by-nc-4.0 multilinguality: - multilingual - translation pretty_name: Alpaca MT size_categories: - 10K<n<100K source_datasets: - tatsu-lab/alpaca tags: - instruction-finetuning task_categories: - text-generation task_ids: - dialogue-modeling dataset_info: - config_name: en features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: prompt dtype: string splits: - name: train num_bytes: 32088854 num_examples: 51942 download_size: 22764890 dataset_size: 32088854 - config_name: pt features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: prompt dtype: string splits: - name: train num_bytes: 33600380 num_examples: 51942 download_size: 23513483 dataset_size: 33600380 - config_name: es features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: prompt dtype: string splits: - name: train num_bytes: 35893136 num_examples: 51942 download_size: 24483751 dataset_size: 35893136 - config_name: ca features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: prompt dtype: string splits: - name: train num_bytes: 33938638 num_examples: 51942 download_size: 23096222 dataset_size: 33938638 - config_name: eu features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: prompt dtype: string splits: - name: train num_bytes: 29977672 num_examples: 51942 download_size: 20469814 dataset_size: 29977672 - config_name: gl features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: prompt dtype: string splits: - name: train num_bytes: 32736710 num_examples: 51942 download_size: 22356802 dataset_size: 32736710 - config_name: at features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: prompt dtype: string splits: - name: train num_bytes: 31487842 num_examples: 51942 download_size: 20688305 dataset_size: 31487842 --- # Dataset Card for Alpaca MT ## Dataset Description - **Homepage:** https://crfm.stanford.edu/2023/03/13/alpaca.html - **Repository:** https://github.com/juletx/alpaca-lora-mt - **Paper:** - **Leaderboard:** - **Point of Contact:** Rohan Taori ### Dataset Summary Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's `text-davinci-003` engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better. This dataset also includes machine-translated data for 6 Iberian languages: Portuguese, Spanish, Catalan, Basque, Galician and Asturian. Translation was done using NLLB-200 3.3B model. The authors built on the data generation pipeline from [Self-Instruct framework](https://github.com/yizhongw/self-instruct) and made the following modifications: - The `text-davinci-003` engine to generate the instruction data instead of `davinci`. - A [new prompt](https://github.com/tatsu-lab/stanford_alpaca/blob/main/prompt.txt) was written that explicitly gave the requirement of instruction generation to `text-davinci-003`. - Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation. - The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions. - Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct. This produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500). In a preliminary study, the authors also found that the 52K generated data to be much more diverse than the data released by [Self-Instruct](https://github.com/yizhongw/self-instruct/blob/main/data/seed_tasks.jsonl). ### Supported Tasks and Leaderboards The Alpaca dataset designed for instruction training pretrained language models. ### Languages The original data in Alpaca is in English (BCP-47 en). We also provide machine-translated data for 6 Iberian languages: Portuguese (BCP-47 pt), Spanish (BCP-47 es), Catalan (BCP-47 ca), Basque (BCP-47 eu), Galician (BCP-47 gl) and Asturian (BCP-47 at). ## Dataset Structure ### Data Instances An example of "train" looks as follows: ```json { "instruction": "Create a classification task by clustering the given list of items.", "input": "Apples, oranges, bananas, strawberries, pineapples", "output": "Class 1: Apples, Oranges\nClass 2: Bananas, Strawberries\nClass 3: Pineapples", "text": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\nCreate a classification task by clustering the given list of items.\n\n### Input:\nApples, oranges, bananas, strawberries, pineapples\n\n### Response:\nClass 1: Apples, Oranges\nClass 2: Bananas, Strawberries\nClass 3: Pineapples", } ``` ### Data Fields The data fields are as follows: * `instruction`: describes the task the model should perform. Each of the 52K instructions is unique. * `input`: optional context or input for the task. For example, when the instruction is "Summarize the following article", the input is the article. Around 40% of the examples have an input. * `output`: the answer to the instruction as generated by `text-davinci-003`. * `text`: the `instruction`, `input` and `output` formatted with the [prompt template](https://github.com/tatsu-lab/stanford_alpaca#data-release) used by the authors for fine-tuning their models. ### Data Splits | | train | |---------------|------:| | en | 52002 | | pt | 52002 | | es | 52002 | | ca | 52002 | | eu | 52002 | | gl | 52002 | | at | 52002 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset Excerpt the [blog post](https://crfm.stanford.edu/2023/03/13/alpaca.html) accompanying the release of this dataset: > We believe that releasing the above assets will enable the academic community to perform controlled scientific studies on instruction-following language models, resulting in better science and ultimately new techniques to address the existing deficiencies with these models. At the same time, any release carries some risk. First, we recognize that releasing our training recipe reveals the feasibility of certain capabilities. On one hand, this enables more people (including bad actors) to create models that could cause harm (either intentionally or not). On the other hand, this awareness might incentivize swift defensive action, especially from the academic community, now empowered by the means to perform deeper safety research on such models. Overall, we believe that the benefits for the research community outweigh the risks of this particular release. Given that we are releasing the training recipe, we believe that releasing the data, model weights, and training code incur minimal further risk, given the simplicity of the recipe. At the same time, releasing these assets has enormous benefits for reproducible science, so that the academic community can use standard datasets, models, and code to perform controlled comparisons and to explore extensions. Deploying an interactive demo for Alpaca also poses potential risks, such as more widely disseminating harmful content and lowering the barrier for spam, fraud, or disinformation. We have put into place two risk mitigation strategies. First, we have implemented a content filter using OpenAI’s content moderation API, which filters out harmful content as defined by OpenAI’s usage policies. Second, we watermark all the model outputs using the method described in Kirchenbauer et al. 2023, so that others can detect (with some probability) whether an output comes from Alpaca 7B. Finally, we have strict terms and conditions for using the demo; it is restricted to non-commercial uses and to uses that follow LLaMA’s license agreement. We understand that these mitigation measures can be circumvented once we release the model weights or if users train their own instruction-following models. However, by installing these mitigations, we hope to advance the best practices and ultimately develop community norms for the responsible deployment of foundation models. ### Discussion of Biases [More Information Needed] ### Other Known Limitations The `alpaca` data is generated by a language model (`text-davinci-003`) and inevitably contains some errors or biases. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode). ### Citation Information ``` @misc{alpaca, author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto }, title = {Stanford Alpaca: An Instruction-following LLaMA model}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}}, } ``` ### Contributions [More Information Needed]

[ -0.4171135127544403, -0.7807489633560181, 0.1321282833814621, 0.0874600037932396, -0.26002368330955505, -0.32844099402427673, -0.17014609277248383, -0.48919546604156494, 0.20360693335533142, 0.6376481652259827, -0.6560009717941284, -0.764260470867157, -0.7453687787055969, -0.02380996011197...

null

rcds/swiss_judgment_prediction_xl

rcds

2023-07-20T07:31:57Z

21

0

null

[ "task_categories:text-classification", "size_categories:100K<n<1M", "language:it", "language:de", "language:fr", "license:cc-by-sa-4.0", "arxiv:2306.09237", "region:us" ]

2023-07-20T07:31:57Z

2023-03-23T23:42:15.000Z

2023-03-23T23:42:15

--- license: cc-by-sa-4.0 task_categories: - text-classification language: - it - de - fr pretty_name: Swiss Judgment Prediction XL size_categories: - 100K<n<1M --- # Dataset Card for Swiss Court View Generation ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary Swiss Judgment Prediction is a multilingual, diachronic dataset of 329K Swiss Federal Supreme Court (FSCS) cases. This dataset is part of a challenging text generation task. ### Supported Tasks and Leaderboards ### Languages Switzerland has four official languages with three languages German, French and Italian being represented. The decisions are written by the judges and clerks in the language of the proceedings. | Language | Subset | Number of Documents Full | |------------|------------|--------------------------| | German | **de** | 160K | | French | **fr** | 128K | | Italian | **it** | 41K | ## Dataset Structure ### Data Fields ``` - decision_id: unique identifier for the decision - facts: facts section of the decision - considerations: considerations section of the decision - label: label of the decision - law_area: area of law of the decision - language: language of the decision - year: year of the decision - court: court of the decision - chamber: chamber of the decision - canton: canton of the decision - region: region of the decision ``` ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization The original data are published from the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML. #### Who are the source language producers? The decisions are written by the judges and clerks in the language of the proceedings. ### Annotations #### Annotation process #### Who are the annotators? Metadata is published by the Swiss Federal Supreme Court (https://www.bger.ch). ### Personal and Sensitive Information The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf) © Swiss Federal Supreme Court, 2002-2022 The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made. Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf ### Citation Information Please cite our [ArXiv-Preprint](https://arxiv.org/abs/2306.09237) ``` @misc{rasiah2023scale, title={SCALE: Scaling up the Complexity for Advanced Language Model Evaluation}, author={Vishvaksenan Rasiah and Ronja Stern and Veton Matoshi and Matthias Stürmer and Ilias Chalkidis and Daniel E. Ho and Joel Niklaus}, year={2023}, eprint={2306.09237}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions

[ -0.28456616401672363, -0.5796923637390137, 0.3820797801017761, 0.2762337327003479, -0.5152049660682678, -0.1704990118741989, -0.18632297217845917, -0.21019364893436432, 0.1812967211008072, 0.6449761390686035, -0.7096788883209229, -0.8628866672515869, -0.7208194136619568, 0.0361028648912906...

null

mstz/toxicity

mstz

2023-04-16T18:03:37Z

21

0

null

[ "task_categories:tabular-classification", "size_categories:n<1K", "language:en", "license:cc", "toxicity", "tabular_classification", "binary_classification", "multiclass_classification", "UCI", "region:us" ]

2023-04-16T18:03:37Z

2023-03-31T14:59:54.000Z

2023-03-31T14:59:54

--- language: - en tags: - toxicity - tabular_classification - binary_classification - multiclass_classification - UCI pretty_name: Toxicity size_categories: - n<1K task_categories: - tabular-classification configs: - encoding - income - income-no race - race license: cc --- # Adult The [Toxicity dataset](https://archive-beta.ics.uci.edu/dataset/728/toxicity) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets). The dataset includes 171 molecules designed for functional domains of a core clock protein, CRY1, responsible for generating circadian rhythm. # Configurations and tasks | **Configuration** | **Task** | **Description** | |-------------------|---------------------------|-----------------------------------------------------------------| | toxicity | Binary classification | Is the molecule toxic? | # Usage ```python from datasets import load_dataset dataset = load_dataset("mstz/toxicity")["train"] ```

[ 0.16146326065063477, -0.24916298687458038, 0.21887004375457764, 0.29796111583709717, -0.48594215512275696, -0.2917250394821167, -0.036644935607910156, -0.43102309107780457, 0.01137737650424242, 0.42338958382606506, -0.9064237475395203, -0.7610629796981812, -0.46975767612457275, 0.364036083...

null

shareAI/ShareGPT-Chinese-English-90k

shareAI

2023-11-03T09:27:34Z

21

124

null

[ "license:apache-2.0", "region:us" ]

2023-11-03T09:27:34Z

2023-04-15T16:23:35.000Z

2023-04-15T16:23:35

--- license: apache-2.0 configs: - config_name: default data_files: sharegpt_jsonl/*.jsonl --- # ShareGPT-Chinese-English-90k 中英文双语人机问答数据集中英文平行双语优质人机问答数据集，覆盖真实复杂场景下的用户提问。用于训练高质量的对话模型（比那些通过反复调用api接口生成机器模拟问答的数据在指令分布上更鲁棒）特点： - 1.同时提供意义表达完全相同的中英文平行对照语料，可进行双语对话模型训练。 - 2.所有问题均非人为臆想加上api轮询拟造的假数据（如Moss)，更加符合真实用户场景的指令分布和提问表达。 - 3.sharegpt数据集是由网友自发分享而收集到的，相当于有一层非常天然的过滤（通过人类感觉），筛除了大部分体验不好的对话。补充：该数据收集于chatGPT还未表现出明显智力退化的时间点。（猜测一方面可能是官方为了减小开支把150B的gpt3.5替换成10b左右的蒸馏版本了，另一方面可能是由于引入了更多的拒绝答复导致模型连接知识逻辑的程度退化）优秀对话llm的训练离不开高质量的多轮对话数据集，如果你也想成为志愿者欢迎加入数据集QQ群：130920969，共同进行优质数据集的交流、收集和建设工作

[ -0.5012993216514587, -0.7796489596366882, 0.25588369369506836, 0.9068453907966614, -0.5729519128799438, -0.306650847196579, 0.15132339298725128, -0.5088291168212891, -0.02672532945871353, 0.24290801584720612, -0.5931634902954102, -0.5879868268966675, -0.6859885454177856, 0.0493451319634914...

null

mstz/pums

mstz

2023-04-18T07:42:19Z

21

0

null

[ "task_categories:tabular-classification", "language:en", "pums", "tabular_classification", "binary_classification", "UCI", "region:us" ]

2023-04-18T07:42:19Z

2023-04-18T07:32:38.000Z

2023-04-18T07:32:38

--- language: - en tags: - pums - tabular_classification - binary_classification - UCI pretty_name: Ipums task_categories: # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts - tabular-classification configs: - pums --- # Pums The [Pums dataset](https://archive-beta.ics.uci.edu/dataset/116/us+census+data+1990) from the [UCI repository](https://archive-beta.ics.uci.edu/). U.S.A. Census dataset, classify the income of the individual. # Configurations and tasks | **Configuration** | **Task** | |-----------------------|---------------------------| | pums | Binary classification.|

[ -0.2317095398902893, -0.11407702416181564, -0.10620364546775818, 0.3040872812271118, -0.19519685208797455, 0.3023463487625122, 0.06308853626251221, 0.24472329020500183, 0.39351996779441833, 0.9872480034828186, -0.3681591749191284, -0.5904054641723633, -0.7621175646781921, 0.168312996625900...

null

PaulineSanchez/Traduction_en_fr_food

PaulineSanchez

2023-04-24T17:18:08Z

21

1

null

[ "task_categories:translation", "language:fr", "language:en", "region:us" ]

2023-04-24T17:18:08Z

2023-04-24T07:55:46.000Z

2023-04-24T07:55:46

--- task_categories: - translation language: - fr - en dataset_info: features: - name: alim_nom_fr dtype: string - name: alim_nom_eng dtype: string splits: - name: train num_bytes: 238948 num_examples: 3153 download_size: 114072 dataset_size: 238948 --- - info: This dataset comes from the ANSES-CIQUAL 2020 Table in English in XML format, found on https://www.data.gouv.fr/fr/datasets/table-de-composition-nutritionnelle-des-aliments-ciqual/

[ -0.1406623125076294, -0.20717939734458923, 0.3539305627346039, 0.2774621248245239, 0.10440798103809357, 0.15046723186969757, 0.3584769368171692, -0.4631906747817993, 0.6001715660095215, 0.9782348871231079, -0.9034261703491211, -0.7443004846572876, -0.5652966499328613, 0.5164563655853271, ...

null

jxu124/llava_instruct_150k

jxu124

2023-05-20T18:50:37Z

21

0

null

[ "region:us" ]

2023-05-20T18:50:37Z

2023-04-24T13:17:41.000Z

2023-04-24T13:17:41

--- dataset_info: features: - name: global_image_id dtype: string - name: image_path dtype: string - name: dialog sequence: sequence: string - name: anns_id dtype: string splits: - name: train num_bytes: 187730970 num_examples: 157712 download_size: 95089013 dataset_size: 187730970 --- # Dataset Card for "llava_instruct_150k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.381185919046402, -0.21156223118305206, 0.14976981282234192, 0.4289839565753937, -0.320220947265625, 0.04785778746008873, 0.2565668523311615, -0.15844084322452545, 0.9704613089561462, 0.5461716055870056, -0.7832256555557251, -0.6524551510810852, -0.5638710856437683, -0.1543763428926468, ...

null

Oniichat/bluemoon_roleplay_chat_data_300k_messages

Oniichat

2023-04-29T16:06:27Z

21

39

null

[ "region:us" ]

2023-04-29T16:06:27Z

2023-04-29T14:44:37.000Z

2023-04-29T14:44:37

--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: thread_title dtype: string - name: thread_href dtype: string - name: message_timestamp dtype: string - name: message_username dtype: string - name: message dtype: string splits: - name: train num_bytes: 266368156 num_examples: 261071 download_size: 138428913 dataset_size: 266368156 --- # Dataset Card for "bluemoon_roleplay_chat_data_300k_messages" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.3246379792690277, -0.27379879355430603, -0.10515933483839035, 0.5633514523506165, -0.3035276234149933, -0.11909522116184235, 0.06248752400279045, -0.22396908700466156, 0.6621529459953308, 0.6218224763870239, -1.0587480068206787, -0.6866611838340759, -0.35663291811943054, -0.382660031318...

null

Nan-Do/instructional_code-search-net-java

Nan-Do

2023-05-20T05:22:11Z

21

0

null

[ "task_categories:conversational", "task_categories:text-generation", "task_categories:text2text-generation", "language:en", "license:apache-2.0", "Java", "Code Generation", "Instruction Response", "region:us" ]

2023-05-20T05:22:11Z

2023-05-19T03:38:41.000Z

2023-05-19T03:38:41

--- dataset_info: features: - name: INSTRUCTION dtype: string - name: RESPONSE dtype: string - name: SOURCE dtype: string splits: - name: train num_bytes: 388087123 num_examples: 467959 download_size: 132263577 dataset_size: 388087123 license: apache-2.0 task_categories: - conversational - text-generation - text2text-generation language: - en tags: - Java - Code Generation - Instruction Response pretty_name: Instructional Java Dataset --- # Dataset Card for "instructional_code-search-net-java" ## Dataset Description - **Homepage:** None - **Repository:** https://huggingface.co/datasets/Nan-Do/instructional_code-search-net-java - **Paper:** None - **Leaderboard:** None - **Point of Contact:** [@Nan-Do](https://github.com/Nan-Do) ### Dataset Summary This is an instructional dataset for Java. The dataset contains two different kind of tasks: - Given a piece of code generate a description of what it does. - Given a description generate a piece of code that fulfils the description. ### Languages The dataset is in English. ### Data Splits There are no splits. ## Dataset Creation May of 2023 ### Curation Rationale This dataset was created to improve the coding capabilities of LLMs. ### Source Data The summarized version of the code-search-net dataset can be found at https://huggingface.co/datasets/Nan-Do/code-search-net-java ### Annotations The dataset includes an instruction and response columns. #### Annotation process The annotation procedure was done using templates and NLP techniques to generate human-like instructions and responses. A sample notebook of the process can be found at https://github.com/Nan-Do/OpenAssistantInstructionResponsePython The annontations have been cleaned to make sure there are no repetitions and/or meaningless summaries. ### Licensing Information Apache 2.0

[ -0.36286669969558716, -0.6639443039894104, 0.028247717767953873, 0.2826530337333679, -0.1181660145521164, -0.1153736263513565, -0.2993502914905548, -0.08182907849550247, 0.48599404096603394, 0.5879907608032227, -0.6160436272621155, -0.9101932644844055, -0.49650007486343384, 0.1888033598661...

null

Nan-Do/instructional_code-search-net-go

Nan-Do

2023-05-20T05:40:29Z

21

0

null

[ "region:us" ]

2023-05-20T05:40:29Z

2023-05-19T03:42:14.000Z

2023-05-19T03:42:14

--- dataset_info: features: - name: INSTRUCTION dtype: string - name: RESPONSE dtype: string - name: SOURCE dtype: string splits: - name: train num_bytes: 122612124 num_examples: 203128 download_size: 45476654 dataset_size: 122612124 --- # Dataset Card for "instructional_code-search-net-go" IT STILL REQUIRES MORE WORK. PLEASE DON'T USE IT

[ -0.34575435519218445, -0.23241668939590454, -0.205130472779274, 0.24759937822818756, -0.6062061190605164, -0.2600247263908386, -0.08977492153644562, 0.0012433906085789204, 0.21722064912319183, 0.5682284235954285, -0.8225218057632446, -0.6054564118385315, -0.47608044743537903, 0.04358130320...

null

Pranavkpba2000/skin_cancer_complete_dataset_resized

Pranavkpba2000

2023-05-19T14:27:15Z

21

0

null

[ "region:us" ]

2023-05-19T14:27:15Z

2023-05-19T14:26:34.000Z

2023-05-19T14:26:34

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': AK '1': BCC '2': BKL '3': DF '4': MEL '5': NV '6': SCC '7': VASC splits: - name: train num_bytes: 170443824.892 num_examples: 28516 - name: test num_bytes: 43096803.47 num_examples: 7105 download_size: 203883734 dataset_size: 213540628.362 --- # Dataset Card for "skin_cancer_complete_dataset_resized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.12143640220165253, -0.25855568051338196, 0.2988353967666626, 0.03400927037000656, -0.3669268786907196, 0.14819103479385376, 0.1491997092962265, -0.09654515981674194, 1.0652649402618408, 0.7843487858772278, -0.7701126337051392, -0.9888368248939514, -0.6197947263717651, -0.289598733186721...

null

Patt/copa_th

Patt

2023-06-05T12:36:44Z

21

0

null

[ "language:th", "language:en", "arxiv:1907.04307", "region:us" ]

2023-06-05T12:36:44Z

2023-06-02T09:43:18.000Z

2023-06-02T09:43:18

--- language: - th - en --- # Dataset Card for copa_th ### Dataset Description This dataset is Thai translated version of [copa](https://huggingface.co/datasets/super_glue/viewer/copa) using google translate with [Multilingual Universal Sentence Encoder](https://arxiv.org/abs/1907.04307) to calculate score for Thai translation. ### Languages - EN - TH

[ -0.04422714188694954, -0.5188198685646057, 0.04797809571027756, 0.6286548972129822, -0.8368724584579468, 0.28733447194099426, -0.0996989980340004, -0.41697266697883606, 0.6387519240379333, 0.6029065847396851, -0.4716463088989258, -0.9730432033538818, -0.6112536191940308, 0.2562544047832489...

null

Birchlabs/openai-prm800k-phase1_train-stepwise-critique

Birchlabs

2023-06-02T23:41:25Z

21

0

null

[ "license:mit", "region:us" ]

2023-06-02T23:41:25Z

2023-06-02T23:41:04.000Z

2023-06-02T23:41:04

--- license: mit ---

[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...

null

medmac01/moroccan_history_qa

medmac01

2023-06-07T16:36:23Z

21

2

null

[ "task_categories:question-answering", "size_categories:1K<n<10K", "language:en", "license:cc0-1.0", "history", "Morocco", "region:us" ]

2023-06-07T16:36:23Z

2023-06-07T16:19:03.000Z

2023-06-07T16:19:03

--- license: cc0-1.0 task_categories: - question-answering language: - en tags: - history - Morocco pretty_name: 🇲🇦 Moroccan History Dataset for Contextual Question Answering size_categories: - 1K<n<10K ---

[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...

null

DISCOX/DISCO-10M

DISCOX

2023-06-26T19:54:22Z

21

14

null

[ "size_categories:10M<n<100M", "language:en", "license:cc-by-4.0", "music", "arxiv:2306.13512", "doi:10.57967/hf/1190", "region:us" ]

2023-06-26T19:54:22Z

2023-06-13T07:45:14.000Z

2023-06-13T07:45:14

--- license: cc-by-4.0 language: - en tags: - music size_categories: - 10M<n<100M dataset_info: features: - name: video_url_youtube dtype: string - name: video_title_youtube dtype: string - name: track_name_spotify dtype: string - name: video_duration_youtube_sec dtype: float64 - name: preview_url_spotify dtype: string - name: video_view_count_youtube dtype: float64 - name: video_thumbnail_url_youtube dtype: string - name: search_query_youtube dtype: string - name: video_description_youtube dtype: string - name: track_id_spotify dtype: string - name: album_id_spotify dtype: string - name: artist_id_spotify sequence: string - name: track_duration_spotify_ms dtype: int64 - name: primary_artist_name_spotify dtype: string - name: track_release_date_spotify dtype: string - name: explicit_content_spotify dtype: bool - name: similarity_duration dtype: float64 - name: similarity_query_video_title dtype: float64 - name: similarity_query_description dtype: float64 - name: similarity_audio dtype: float64 - name: audio_embedding_spotify sequence: float32 - name: audio_embedding_youtube sequence: float32 splits: - name: train num_bytes: 73263841657.0 num_examples: 15296232 download_size: 88490703682 dataset_size: 73263841657.0 --- ### Getting Started You can download the dataset using HuggingFace: ```python from datasets import load_dataset ds = load_dataset("DISCOX/DISCO-10M") ``` ## Dataset Structure The dataset contains the following features: ```json { 'video_url_youtube', 'video_title_youtube', 'track_name_spotify', 'video_duration_youtube_sec', 'preview_url_spotify', 'video_view_count_youtube', 'video_thumbnail_url_youtube', 'search_query_youtube', 'video_description_youtube', 'track_id_spotify', 'album_id_spotify', 'artist_id_spotify', 'track_duration_spotify_ms', 'primary_artist_name_spotify', 'track_release_date_spotify', 'explicit_content_spotify', 'similarity_duration', 'similarity_query_video_title', 'similarity_query_description', 'similarity_audio', 'audio_embedding_spotify', 'audio_embedding_youtube', } ``` ## What is DISCO-10M? DISCO-10M is a music dataset created to democratize research on large-scale machine learning models for music. The dataset contains no music due to copyright laws. The audio embedding features were computed using [Laion-CLAP](https://github.com/LAION-AI/CLAP), and can be used instead of the raw audio for many down-stream tasks. In case the raw audio is needed, it can be downloaded from the provided Spotify preview URL or via the YouTube link. DISCO-10M was created by collecting a list of 400,000 artist IDs and 2.6M track IDs from Spotify, and collecting YouTube video links that match the track duration, artist name, and track names. These matches were computed using the following three similarity metrics: - Duration similarity: ` 1 - abs(track_duration_spotify - video_duration_youtube) / max(track_duration_spotify, video_duration_youtube) ` - Text similarity is calculated using the cosine similarity between the embedding of the search query and the embedding of the video title, as well as the search query embedding and the video description embedding. Embeddings are computed using [Sentence Bert](https://huggingface.co/sentence-transformers). - Audio similarity is calculated using the cosine similarity between the Spotify preview snippet audio embedding and the YouTube audio embedding. For DISCO-10M we only keep samples that return true for: ` duration_similarity > 0.25 and (description_similarity > 0.65 or title_similarity > 0.65) and audio_similarity > 0.4 ` We offer three subsets based on DISCO-10M: - [DISCO-10K-random](https://huggingface.co/datasets/DISCOX/DISCO-10K-random): a small subset of random samples from the entire dataset. - [DISCO-200K-random](https://huggingface.co/datasets/DISCOX/DISCO-200K-random): a subset of random samples, useful for a light-weight and representative analysis of the entire dataset. - [DISCO-200K-high-quality](https://huggingface.co/datasets/DISCOX/DISCO-200K-high-quality): a subset of samples which were filtered more strictly to ensure a higher quality match between Spotify tracks and YouTube videos. To cite our work, please refer to our paper [here](https://arxiv.org/abs/2306.13512).

[ -0.674363911151886, -0.6203828454017639, 0.258932888507843, 0.4133884310722351, -0.13405776023864746, -0.08463743329048157, -0.3786267042160034, -0.020894063636660576, 0.6923831701278687, 0.3973049521446228, -0.9724166393280029, -0.8051467537879944, -0.3726343810558319, -0.0380270443856716...

null

BerMaker/test

BerMaker

2023-06-13T09:44:54Z

21

0

null

[ "task_categories:text-classification", "size_categories:n<1K", "license:apache-2.0", "code", "art", "region:us" ]

2023-06-13T09:44:54Z

2023-06-13T08:12:00.000Z

2023-06-13T08:12:00

--- license: apache-2.0 task_categories: - text-classification tags: - code - art size_categories: - n<1K ---

[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...

null

alpindale/visual-novels

alpindale

2023-06-14T14:44:30Z

21

20

null

[ "task_categories:conversational", "task_categories:text-generation", "language:en", "license:apache-2.0", "region:us" ]

2023-06-14T14:44:30Z

2023-06-14T13:15:15.000Z

2023-06-14T13:15:15

--- license: apache-2.0 task_categories: - conversational - text-generation language: - en pretty_name: Visual Novels --- # Visual Novel Dataset This dataset contains parsed Visual Novel scripts for training language models. The dataset consists of approximately 60 million tokens of parsed scripts. ## Dataset Structure The dataset follows a general structure for visual novel scripts: - Dialogue lines: Dialogue lines are formatted with the speaker's name followed by a colon, and the dialogue itself enclosed in quotes. For example: ``` John: "Hello, how are you?" ``` - Actions and narration: Actions and narration within the Visual Novel scripts are often enclosed in asterisks, but it's important to note that not all visual novels follow this convention. Actions and narration provide descriptions of character movements, background settings, or other narrative elements. ``` *John looked around the room, searching for answers.* ``` ## Contents - `visual-novels.txt`: This file contains all the parsed VNs concatenated within a single plaintext file. Each entry is separated with this string: ``` [ - title - {visual-novel-title-1.txt} ] ``` - `VNDB/`: This directory contains `.json` files that contain VNDB IDs for the corresponding VN's characters. Does not include unparsed VNs. - `Archives/visual-novels-parsed.tar.zst`: This archive contains the parsed VNs but with each script in a separate text file (i.e. not concatenated). - `Archives/visual-novels-unparsed.tar.zst`: This archive contains all the unparsed VNs along with the original script for the currently parsed VNs. ## Usage You can utilize this dataset to train language models, particularly for tasks related to natural language processing and text generation. By leveraging the parsed visual novel scripts, you can train models to understand dialogue structures and generate coherent responses. Additionally, the inclusion of the unparsed scripts allows for further analysis and processing. ## Contribution This dataset was gathered and parsed by the [PygmalionAI](https://hugginface.co/PygmalionAI) Data Processing Team. Listed below are the team members, sorted by contribution amount: - **Suikamelon**: [HuggingFace](https://huggingface.co/lemonilia) - (2,787,704 ++ 672,473 --) - **Alpin**: [HuggingFace](https://huggingface.co/alpindale) - [GitHub](https://github.com/AlpinDale) (1,170,985 ++ 345,120 --) - **Spartan**: [GitHub](https://github.com/Spartan9772) (901,046 ++ 467,915 --) - **Unlucky-AI** [GitHub](https://github.com/Unlucky-AI) (253,316 ++ 256 --) ## Citation If you use this dataset in your research or projects, please cite it appropriately. ## Acknowledgements This dataset is compiled and shared for research and educational purposes. The dataset includes parsed visual novel scripts from various sources, which are predominantly copyrighted and owned by their respective publishers and creators. The inclusion of these scripts in this dataset does not imply any endorsement or authorization from the copyright holders. We would like to express our sincere gratitude to the original copyright holders and creators of the visual novels for their valuable contributions to the art and storytelling. We respect and acknowledge their intellectual property rights. We strongly encourage users of this dataset to adhere to copyright laws and any applicable licensing restrictions when using or analyzing the provided content. It is the responsibility of the users to ensure that any use of the dataset complies with the legal requirements governing intellectual property and fair use. Please be aware that the creators and distributors of this dataset disclaim any liability or responsibility for any unauthorized or illegal use of the dataset by third parties. If you are a copyright holder or have any concerns about the content included in this dataset, please contact us at [this email address](mailto:alpin@alpindale.dev) to discuss the matter further and address any potential issues.

[ -0.12256193906068802, -0.51116544008255, 0.25897446274757385, 0.07100122421979904, -0.0005948558682575822, -0.19226036965847015, -0.07760875672101974, -0.17992031574249268, 0.21873702108860016, 0.8207636475563049, -0.5849244594573975, -0.702824592590332, -0.27804499864578247, 0.28966796398...

null

buelfhood/Soco

buelfhood

2023-11-10T13:59:02Z

21

0

null

[ "region:us" ]

2023-11-10T13:59:02Z

2023-06-15T18:56:13.000Z

2023-06-15T18:56:13

Entry not found

[ -0.3227645754814148, -0.22568479180335999, 0.8622264862060547, 0.43461528420448303, -0.52829909324646, 0.7012971639633179, 0.7915720343589783, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104477167129517, 0.5715674161911011, -...

null

ecnu-icalk/educhat-sft-002-data-osm

ecnu-icalk

2023-07-01T10:11:46Z

21

14

null

[ "license:cc-by-nc-4.0", "region:us" ]

2023-07-01T10:11:46Z

2023-06-27T07:48:28.000Z

2023-06-27T07:48:28

--- license: cc-by-nc-4.0 --- 每条数据由一个存放对话的list和与数据对应的system_prompt组成。list中按照Q，A顺序存放对话。数据来源为开源数据，使用[CleanTool](https://github.com/icalk-nlp/EduChat/tree/main/clean_tool)数据清理工具去重。

[ -0.34059879183769226, -0.5356546640396118, 0.03982355073094368, 0.11401523649692535, -0.7113452553749084, 0.11454257369041443, 0.3600074350833893, -0.17466410994529724, 0.5107874870300293, 0.33371496200561523, -0.6063730120658875, -0.48709896206855774, -0.33621466159820557, 0.1527266651391...

null

CreativeLang/EPIC_Irony

CreativeLang

2023-07-11T16:46:43Z

21

1

null

[ "region:us" ]

2023-07-11T16:46:43Z

2023-07-11T16:34:27.000Z

2023-07-11T16:34:27

--- dataset_info: features: - name: user dtype: string - name: label dtype: string - name: timestamp dtype: string - name: source dtype: string - name: subreddit dtype: string - name: id_original dtype: string - name: text dtype: string - name: parent_id_original dtype: string - name: parent_text dtype: string - name: Language_instance dtype: string - name: Language_variety dtype: string - name: Age dtype: string - name: Sex dtype: string - name: Ethnicity simplified dtype: string - name: Country of birth dtype: string - name: Country of residence dtype: string - name: Nationality dtype: string - name: Language_annotator dtype: string - name: Student status dtype: string - name: Employment status dtype: string splits: - name: train num_bytes: 7299373 num_examples: 14172 download_size: 1038853 dataset_size: 7299373 --- # EPIC_Irony - paper: [EPIC: Multi-Perspective Annotation of a Corpus of Irony](https://assets.amazon.science/40/b4/0f6ec06a4a33a44485de1b2b57c7/epic-multi-perspective-annotation-of-a-corpus-of-irony.pdf) at ACL 2023 Key features: - EPIC (English Perspectivist Irony Corpus) is an annotated corpus for irony analysis based on data perspectivism principles. - The corpus contains social media conversations in five regional varieties of English, annotated by contributors from corresponding countries. - The dataset explores the perspectives of annotators, taking into account their origin, age, and gender. - Perspective-aware models were created to validate EPIC, and these proved more effective and confident in identifying irony than non-perspectivist models. - The models showcase variation in irony perception across different demographic groups. - EPIC serves as a valuable resource for training perspective-aware models for irony detection. Metadata in Creative Language Toolkit ([CLTK](https://github.com/liyucheng09/cltk)) - CL Type: Irony - Task Type: detection - Size: 14k - Created time: 2023

[ -0.5514488816261292, -0.579515814781189, 0.48480111360549927, 0.5998189449310303, -0.1795070916414261, 0.27041980624198914, -0.18420928716659546, -0.6347976922988892, 0.2726823389530182, 0.43769970536231995, -0.2049654871225357, -0.639005720615387, -0.6351090669631958, 0.43863335251808167,...

null

Isotonic/OpenOrca-deduped

Isotonic

2023-08-24T13:21:18Z

21

4

null

[ "task_categories:text-generation", "task_categories:text2text-generation", "task_categories:conversational", "task_categories:text-classification", "task_categories:token-classification", "task_categories:table-question-answering", "task_categories:zero-shot-classification", "task_categories:question-...

2023-08-24T13:21:18Z

2023-07-12T22:29:09.000Z

2023-07-12T22:29:09

--- license: mit dataset_info: features: - name: id dtype: string - name: system_prompt dtype: string - name: question dtype: string - name: response dtype: string - name: reward dtype: float32 splits: - name: train num_bytes: 3274600633.90245 num_examples: 2409134 - name: test num_bytes: 409325419.048775 num_examples: 301142 - name: validation num_bytes: 409325419.048775 num_examples: 301142 download_size: 2268645581 dataset_size: 4093251472.0000005 task_categories: - text-generation - text2text-generation - conversational - text-classification - token-classification - table-question-answering - zero-shot-classification - question-answering - summarization - feature-extraction language: - en size_categories: - 1M<n<10M arxiv: - 2301.13688 - 2306.02707 --- # Dataset Card for Isotonic/OpenOrca-deduped ## Dataset Summary This dataset is a deduplicated version of [Open-Orca/OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) *MinHash Deduplication with Jaccard Threshold = 0.80* ``` Original dataset size: 4233923 Number of duplicate clusters: 522077 Files in duplicate cluster: 2115143 Unique files in duplicate cluster: 892638 Filtered dataset size: 3011418 ```

[ -0.43616676330566406, -0.42436307668685913, -0.08104299008846283, 0.20047619938850403, -0.9148666262626648, -0.5285870432853699, -0.1565181463956833, -0.420106440782547, 0.5421155095100403, 0.80739825963974, -0.252019464969635, -0.9673815369606018, -0.43428272008895874, 0.12138815224170685...

null

mrtoy/mobile-ui-design

mrtoy

2023-07-19T09:09:22Z

21

16

null

[ "task_categories:object-detection", "size_categories:n<1K", "license:apache-2.0", "ui", "design", "detection", "region:us" ]

2023-07-19T09:09:22Z

2023-07-13T11:12:51.000Z

2023-07-13T11:12:51

--- license: apache-2.0 dataset_info: features: - name: width dtype: int64 - name: height dtype: int64 - name: image dtype: image - name: objects struct: - name: bbox sequence: sequence: float64 - name: category sequence: string - name: color list: - name: alpha dtype: float64 - name: blue dtype: float64 - name: green dtype: float64 - name: red dtype: float64 - name: radius sequence: float64 - name: text sequence: string splits: - name: train num_bytes: 1253458059.322 num_examples: 7846 download_size: 1160884066 dataset_size: 1253458059.322 task_categories: - object-detection tags: - ui - design - detection size_categories: - n<1K --- # Dataset: Mobile UI Design Detection ## Introduction This dataset is designed for object detection tasks with a focus on detecting elements in mobile UI designs. The targeted objects include text, images, and groups. The dataset contains images and object detection boxes, including class labels and location information. ## Dataset Content Load the dataset and take a look at an example: ```python >>> from datasets import load_dataset >>>> ds = load_dataset("mrtoy/mobile-ui-design") >>> example = ds[0] >>> example {'width': 375, 'height': 667, 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=375x667>, 'objects': {'bbox': [[0.0, 0.0, 375.0, 667.0], [0.0, 0.0, 375.0, 667.0], [0.0, 0.0, 375.0, 20.0], ... ], 'category': ['text', 'rectangle', 'rectangle', ...]}} ``` The dataset has the following fields: - image: PIL.Image.Image object containing the image. - height: The image height. - width: The image width. - objects: A dictionary containing bounding box metadata for the objects in the image: - bbox: The object’s bounding box (xmin,ymin,width,height). - category: The object’s category, with possible values including rectangle、text、group、image - color: The object’s color, text color or rectangle color, or None - radius: The object’s color, rectangle radius, or None - text: text content, or None You can visualize the bboxes on the image using some internal torch utilities. ```python import torch from torchvision.ops import box_convert from torchvision.utils import draw_bounding_boxes from torchvision.transforms.functional import pil_to_tensor, to_pil_image item = ds[0] boxes_xywh = torch.tensor(item['objects']['bbox']) boxes_xyxy = box_convert(boxes_xywh, 'xywh', 'xyxy') to_pil_image( draw_bounding_boxes( pil_to_tensor(item['image']), boxes_xyxy, labels=item['objects']['category'], ) ) ``` ![image](9b8671a5-b529-41dc-b951-b29a8b29da64.png) ![image](11c03c2c-39ac-442b-9c1a-67e1e0a2aea7.png) ![image](ec197c72-f8ba-4f79-81fa-ceaf533cb5e3.png) ## Applications This dataset can be used for various applications, such as: - Training and evaluating object detection models for mobile UI designs. - Identifying design patterns and trends to aid UI designers and developers in creating high-quality mobile app UIs. - Enhancing the automation process in generating UI design templates. - Improving image recognition and analysis in the field of mobile UI design.

[ -0.5113224983215332, -0.5011017918586731, 0.16985833644866943, -0.038685429841279984, -0.27158328890800476, -0.2369052618741989, 0.21459254622459412, -0.22435547411441803, 0.15027467906475067, 0.41543877124786377, -0.4524768590927124, -0.8889833092689514, -0.24973809719085693, -0.077361360...

null

squarelike/sharegpt_deepl_ko_translation

squarelike

2023-10-12T17:11:05Z

21

7

null

[ "region:us" ]

2023-10-12T17:11:05Z

2023-07-14T04:28:43.000Z

2023-07-14T04:28:43

[https://github.com/jwj7140/Gugugo](https://github.com/jwj7140/Gugugo) [sharegpt_deepl_ko](https://huggingface.co/datasets/junelee/sharegpt_deepl_ko)를 한-영 번역데이터로 변환한 데이터입니다. - translation_data_sharegpt.json: 최대 약 1300자 분량의 번역 데이터 모음 - translation_data_sharegpt_long.json: 1300자~7000자 분량의 번역 데이터 모음 sharegpt_deepl_ko에서 몇 가지의 데이터 전처리를 진행했습니다.

[ -0.45616084337234497, -0.7099447250366211, 0.5211774706840515, 0.651600182056427, -0.45719239115715027, -0.0891818106174469, -0.5389081239700317, -0.14301201701164246, 0.371317058801651, 0.16980430483818054, -0.45897552371025085, -1.058142900466919, -1.0188076496124268, -0.0305810384452343...

null

andersonbcdefg/chemistry

andersonbcdefg

2023-07-21T01:24:18Z

21

3

null

[ "region:us" ]

2023-07-21T01:24:18Z

2023-07-21T01:23:53.000Z

2023-07-21T01:23:53

--- dataset_info: features: - name: role_1 dtype: string - name: topic; dtype: string - name: sub_topic dtype: string - name: message_1 dtype: string - name: message_2 dtype: string splits: - name: train num_bytes: 47000178 num_examples: 20000 download_size: 21669458 dataset_size: 47000178 --- # Dataset Card for "chemistry" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.4671776294708252, -0.12926222383975983, 0.6378039717674255, 0.06386177986860275, 0.0969977080821991, 0.270707368850708, 0.2212810516357422, -0.06618616729974747, 0.8364385366439819, 0.3774828314781189, -0.8983457088470459, -0.9349573850631714, -0.4217014014720917, -0.2797333896160126, ...

null

Yuhthe/samsum_vi_word

Yuhthe

2023-07-26T02:57:48Z

21

0

null

[ "task_categories:summarization", "language:vi", "region:us" ]

2023-07-26T02:57:48Z

2023-07-25T07:30:27.000Z

2023-07-25T07:30:27

--- configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: id dtype: string - name: dialogue dtype: string - name: summary dtype: string splits: - name: test num_bytes: 761520 num_examples: 819 - name: train num_bytes: 13465942 num_examples: 14732 - name: validation num_bytes: 733668 num_examples: 818 download_size: 7875036 dataset_size: 14961130 task_categories: - summarization language: - vi --- # Dataset Card for "samsum_vi_word" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.33596137166023254, -0.14180555939674377, 0.22183050215244293, 0.16931387782096863, -0.5185675621032715, -0.11468572169542313, 0.06867137551307678, -0.04038447514176369, 1.0470097064971924, 0.44514530897140503, -0.813039243221283, -0.9405884742736816, -0.8277267813682556, -0.220806419849...

null

xzuyn/futurama-alpaca

xzuyn

2023-08-03T06:49:53Z

21

0

null

[ "task_categories:text-generation", "task_categories:conversational", "size_categories:n<1K", "language:en", "region:us" ]

2023-08-03T06:49:53Z

2023-08-01T20:41:50.000Z

2023-08-01T20:41:50

--- language: - en size_categories: - n<1K task_categories: - text-generation - conversational --- [Original Dataset](https://www.kaggle.com/datasets/josephvm/futurama-seasons-16-transcripts?select=only_spoken_text.csv) 114 episodes. WIP formatting as with LLaMa, it's like 4000+ tokens each. I would like to augment the instruction, and also possibly input a summary. I also want to make a set that includes multiple tv shows. Just not sure how I wanna go about reformatting all this to fit into smaller chunks like 512 tokens, while still understanding the context of being and instruction but the episode at the same time. ``` Instruction: `Generate an episode of Futurama.` Input: `{Episode Name} - {Episode Synopsis}` Output: `{Episode Dialog In Chat Format}` ```

[ -0.13868971168994904, -0.8153975009918213, -0.029083365574479103, 0.344361275434494, -0.26406627893447876, -0.0032029161229729652, -0.6131306290626526, 0.20053748786449432, 0.752689003944397, 0.3752410411834717, -0.9294160008430481, -0.23439133167266846, -0.45044732093811035, 0.35960903763...

null

FreedomIntelligence/sharegpt-deutsch

FreedomIntelligence

2023-08-13T15:58:00Z

21

3

null

[ "license:apache-2.0", "region:us" ]

2023-08-13T15:58:00Z

2023-08-13T15:54:15.000Z

2023-08-13T15:54:15

--- license: apache-2.0 --- Deutsch ShareGPT data translated by gpt-3.5-turbo. The dataset is used in the research related to [MultilingualSIFT](https://github.com/FreedomIntelligence/MultilingualSIFT).

[ -0.5890281796455383, -0.5391521453857422, 0.4158187806606293, 0.48624733090400696, -0.4605351686477661, -0.019513238221406937, -0.30484727025032043, -0.48206207156181335, 0.2687070965766907, 0.14224904775619507, -0.9098215699195862, -0.3965979218482971, -0.738793671131134, 0.10147108882665...

null

botp/yentinglin-zh_TW_c4

botp

2023-08-16T06:55:04Z

21

0

null

[ "task_categories:text-generation", "size_categories:1M<n<10M", "language:zh", "license:odc-by", "arxiv:2305.13711", "arxiv:2104.09864", "region:us" ]

2023-08-16T06:55:04Z

2023-08-16T06:55:04.000Z

2023-08-16T06:55:04

--- dataset_info: features: - name: text dtype: string - name: timestamp dtype: string - name: url dtype: string - name: zh_id dtype: int64 splits: - name: train num_bytes: 15132113806.438198 num_examples: 5179546 - name: validation num_bytes: 14820238.368907347 num_examples: 5099 download_size: 5569491261 dataset_size: 15146934044.807106 license: odc-by task_categories: - text-generation language: - zh pretty_name: Traditional Chinese C4 size_categories: - 1M<n<10M duplicated_from: yentinglin/zh_TW_c4 --- # Language Models for Taiwanese Culture ✍️ <a href="https://huggingface.co/spaces/yentinglin/Taiwan-LLaMa2" target="_blank">Online Demo</a> • 🤗 <a href="https://huggingface.co/yentinglin" target="_blank">HF Repo</a> • 🐦 <a href="https://twitter.com/yentinglin56" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/pdf/2305.13711.pdf" target="_blank">[Paper Coming Soon]</a> • 👨️ <a href="https://yentingl.com/" target="_blank">Yen-Ting Lin</a> <img src="https://www.csie.ntu.edu.tw/~miulab/taiwan-llama/logo-v2.png" width="100"> <a href="https://github.com/tatsu-lab/stanford_alpaca/blob/main/LICENSE"> <img src="https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg"></a> <a href="https://github.com/tatsu-lab/stanford_alpaca/blob/main/DATA_LICENSE"> <img src="https://img.shields.io/badge/Data%20License-CC%20By%20NC%204.0-red.svg"></a> ## Overview Taiwan-LLaMa is a full parameter fine-tuned model based on LLaMa 2 for Traditional Mandarin applications. **Taiwan-LLaMa v1.0** pretrained on over 5 billion tokens and instruction-tuned on over 490k conversations both in traditional mandarin. ## Demo A live demonstration of the model can be accessed at [Hugging Face Spaces](https://huggingface.co/spaces/yentinglin/Taiwan-LLaMa2). ## Key Features 1. **Traditional Mandarin Support**: The model is fine-tuned to understand and generate text in Traditional Mandarin, making it suitable for Taiwanese culture and related applications. 2. **Instruction-Tuned**: Further fine-tuned on conversational data to offer context-aware and instruction-following responses. 3. **Performance on Vicuna Benchmark**: Taiwan-LLaMa's relative performance on Vicuna Benchmark is measured against models like GPT-4 and ChatGPT. It's particularly optimized for Taiwanese culture. 4. **Flexible Customization**: Advanced options for controlling the model's behavior like system prompt, temperature, top-p, and top-k are available in the demo. ## Work in progress - [ ] **Improved pretraining**: A refined pretraining process (e.g. more data from Taiwan, training strategies) is under development, aiming to enhance model performance for better Taiwanese culture. - [ ] **Extend max length**: Utilizing the Rope mechanism as described in [the paper](https://arxiv.org/abs/2104.09864), the model's length will be extended from 4k to 8k. ## Taiwanese Culture Examples | Question | ChatGPT | **Taiwan LLaMa v1.0** | **Taiwan LLaMa v0.0 (w/o pretraining)** | Llama2_Traditional_Chinese_13b_Chat | |:-|:-|:-|:-|:-| | 以惡地景觀著名的是高雄哪一個景點? | 以惡地地景著名的是高雄市的旗津。 | 高雄以擁有惡地景觀著名的景點是田寮月世界。✅ | 以惡地景觀著名的是高雄市的旗津島。旗津島是一個小島，... | 高雄市內有多個以惡地景觀著名的景點，其中以下面幾個最為著名： 1. 高雄捷運紅線沿途景點：高雄市政府為了推廣高雄 | | 唐鳳是誰？ | 唐鳳（Audrey Tang）是台灣的政治家、社會活動家和軟體工程師。她於2016年擔任台灣行政院數位政務委員，是台灣政府歷史上首位沒有政黨背景的政務官。唐鳳在軟體工程領域有豐富的經驗。 ✅ | 唐鳳是政治評論家、資訊社會學研究者，也是台灣第一位擔任政府數位政委的人。 ✅ | 唐鳳（Tang Feng）是一位著名的中國演員和歌手。他出生於1974年12月17日，出生地點在中國北京。唐鳳在1995年開始他的演藝生涯，並在同年發行了他的首張專輯《我不孤單》。此後，他發行了許多受歡迎的歌曲，並在多部電視劇和電影中擔任主角。唐鳳因其精湛的演技和迷人的歌聲而被譽為“亞洲魅力男王”。他的音樂獲得了多個獎項，包括“華表最佳新男演唱獎”、“華表最佳歌曲獎”和“華表最佳舞蹈獎”等。 | 唐鳳是唐朝時期的一位著名文學家，他的代表作品包括《陰騭》、《狂凡子》等。他的文學風格具有強烈的個性和創意，被譽為「唐代文學之父」。 | | NTU 在哪? | NTU位於新加坡，具體地址是50 Nanyang Avenue, Singapore 639798。 | NTU 是國立臺灣大學的縮寫。它是臺灣的一所頂尖研究型大學，位於臺北市。 ✅| NTU 是台灣的一所大學，位於台北市。✅ | NTU 的總部位於台灣新北市板橋區，佔地面積約為1,000公頃。| | 甚麼是22K? | "22K" 可能指的是金的純度，尤其是在珠寶和金飾方面。 | "22K"是指台灣的大學畢業生的起薪，大約是新台幣22,000元。這個詞被用來形容大學畢業生的起薪偏低，並且在就業市場上對於大學畢業生的需求不足。✅| 22K是指溫度尺度上的22,000K（千度）。1K代表一度，即溫度差距，是攝氏溫標（攝氏度）的一百萬分之一。因此，22K等於-253.15°C（攝氏溫標）或-423.67°F（華氏溫標）。 | 22K是一種簡寫，即22,000。它是一個數字，表示22,000個單位。| | 肉圓要不要加香菜？ | 這是個個人口味的問題，有些人喜歡在肉圓中加入香菜，因為香菜的清香可以增添風味；而有些人則不喜歡香菜的味道，覺得它會蓋過其他食材的味道。所以要不要加香菜，可以根據個人喜好來決定。 | 這是一個個人喜好的問題，有些人喜歡在肉圓裡加香菜，有些人不喜歡。**在台灣**，肉圓加香菜是一種常見的做法，這是一種在肉圓上加香菜的做法。然而，在其他國這是一個文化差異的問題。😊 | 肉圓加香菜可以增添一些芳香和美味的口感，如果你喜歡香菜的味道，可以試試加入肉圓中。香菜通常在製作過程中炒香，可以增添一些芳香和美味的味道。如果你想要加入香菜，可以在製作過程中炒香一些香菜，然後將其混入肉圓中。肉圓加香菜可以增添一些芳香和美味的口感，如果你喜歡香菜的味道，可以試試加入肉圓中。| 加香菜的肉圓味道更好，但是不加香菜的肉圓也很好吃。如果你喜歡香菜的味道，那麼加上香菜就好了。如果你不喜歡香菜，那麼不加也不會影響肉 | ## Model We provide a number of model checkpoints that we trained. Please find them on Hugging Face [here](https://huggingface.co/models?search=taiwan-llama). Here are some quick links to the checkpoints that are finetuned from LLaMa 2: | **Model** | **13B** | |--------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------| | **Taiwan-LLaMa v1.0** (_better for Taiwanese Culture_) | 🤗 <a href="https://huggingface.co/yentinglin/Taiwan-LLaMa-v1.0" target="_blank">yentinglin/Taiwan-LLaMa-v1.0</a> | | Taiwan-LLaMa v0.9 (partial instruction set) | 🤗 <a href="https://huggingface.co/yentinglin/Taiwan-LLaMa-v0.9" target="_blank">yentinglin/Taiwan-LLaMa-v0.9</a> | | Taiwan-LLaMa v0.0 (no Traditional Mandarin pretraining) | 🤗 <a href="https://huggingface.co/yentinglin/Taiwan-LLaMa-v0.0" target="_blank">yentinglin/Taiwan-LLaMa-v0.0</a> | ## Data Here are some quick links to the datasets that we used to train the models: | **Dataset** | **Link** | |---------------------------------|-------------------------------------------------------------------------------------------------------------------------------| | **Instruction-tuning** | 🤗 <a href="https://huggingface.co/datasets/yentinglin/traditional_mandarin_instructions" target="_blank">yentinglin/traditional_mandarin_instructions</a> | | Traditional Mandarin Pretraining | 🤗 <a href="https://huggingface.co/datasets/yentinglin/zh_TW_c4" target="_blank">yentinglin/zh_TW_c4</a> | ## Architecture Taiwan-LLaMa is based on LLaMa 2, leveraging transformer architecture, <a href="https://github.com/Dao-AILab/flash-attention" target="_blank">flash attention 2</a>, and bfloat16. It includes: * Pretraining Phase: Pretrained on a vast corpus of over 5 billion tokens, extracted from common crawl in Traditional Mandarin. * Fine-tuning Phase: Further instruction-tuned on over 490k multi-turn conversational data to enable more instruction-following and context-aware responses. ## Generic Capabilities on Vicuna Benchmark The data is translated into traditional mandarin for evaluating the general capability. <img src="./images/zhtw_vicuna_bench_chatgptbaseline.png" width="700"> The scores are calculated with ChatGPT as the baseline, represented as 100%. The other values show the relative performance of different models compared to ChatGPT. | Language Model | Relative Score (%) | |-------------------------------------|--------------------| | GPT-4 | 102.59% | | ChatGPT | 100.00% | | **Taiwan-LLaMa v1.0** | 76.76% | | Claude-Instant-1.2 | 74.04% | | Llama2_Traditional_Chinese_13b_Chat | 56.21% | ## How to deploy the model on my own machine? We recommend hosting models with [🤗 Text Generation Inference](https://github.com/huggingface/text-generation-inference). Please see their [license](https://github.com/huggingface/text-generation-inference/blob/main/LICENSE) for details on usage and limitations. ```bash bash run_text_generation_inference.sh "yentinglin/Taiwan-LLaMa" NUM_GPUS DIR_TO_SAVE_MODEL PORT MAX_INPUT_LEN MODEL_MAX_LEN ``` Prompt format follows vicuna-v1.1 template: ``` A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {user} ASSISTANT: ``` ## Setup development environment ```bash conda create -n taiwan-llama python=3.10 -y conda activate taiwan-llama pip install -r requirements.txt ``` ## Citations If you use our code, data, or models in your research, please cite this repository. You can use the following BibTeX entry: ```bibtex @inproceedings{lin-chen-2023-llm, title = "{LLM}-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models", author = "Lin, Yen-Ting and Chen, Yun-Nung", booktitle = "Proceedings of the 5th Workshop on NLP for Conversational AI (NLP4ConvAI 2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.nlp4convai-1.5", pages = "47--58" } @misc{taiwanllama, author={Lin, Yen-Ting and Chen, Yun-Nung}, title={Taiwanese-Aligned Language Models based on Meta-Llama2}, year={2023}, url={https://github.com/adamlin120/Taiwan-LLaMa}, note={Code and models available at https://github.com/adamlin120/Taiwan-LLaMa}, } ``` ## Collaborate With Us If you are interested in contributing to the development of Traditional Mandarin language models, exploring new applications, or leveraging Taiwan-LLaMa for your specific needs, please don't hesitate to contact us. We welcome collaborations from academia, industry, and individual contributors. ## License The code in this project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details. The models included in this project are licensed under the LLAMA 2 Community License. See the [LLAMA2 License](https://github.com/facebookresearch/llama/blob/main/LICENSE) for full details. ## OpenAI Data Acknowledgment The data included in this project were generated using OpenAI's models and are subject to OpenAI's Terms of Use. Please review [OpenAI's Terms of Use](https://openai.com/policies/terms-of-use) for details on usage and limitations. ## Acknowledgements We thank [Meta LLaMA team](https://github.com/facebookresearch/llama) and [Vicuna team](https://github.com/lm-sys/FastChat) for their open-source efforts in democratizing large language models.

[ -0.4460890293121338, -0.6757620573043823, 0.32770195603370667, 0.3203395903110504, -0.5107942819595337, 0.11846049129962921, -0.10599502921104431, -0.6208014488220215, 0.5243902802467346, 0.34746599197387695, -0.6181227564811707, -0.491379976272583, -0.43354710936546326, 0.1882582157850265...

null

LibrAI/do-not-answer

LibrAI

2023-08-28T18:22:52Z

21

5

null

[ "task_categories:text-generation", "size_categories:n<1K", "language:en", "license:apache-2.0", "safety", "large language model", "region:us" ]

2023-08-28T18:22:52Z

2023-08-28T17:37:24.000Z

2023-08-28T17:37:24

--- license: apache-2.0 task_categories: - text-generation language: - en tags: - safety - large language model pretty_name: do-not-answer size_categories: - n<1K dataset_info: features: - name: id dtype: int64 - name: risk_area dtype: string - name: types_of_harm dtype: string - name: specific_harms dtype: string - name: question dtype: string - name: GPT4_response dtype: string - name: GPT4_harmful dtype: int64 - name: GPT4_action dtype: int64 - name: ChatGPT_response dtype: string - name: ChatGPT_harmful dtype: int64 - name: ChatGPT_action dtype: int64 - name: Claude_response dtype: string - name: Claude_harmful dtype: int64 - name: Claude_action dtype: int64 - name: ChatGLM2_response dtype: string - name: ChatGLM2_harmful dtype: int64 - name: ChatGLM2_action dtype: int64 - name: llama2-7b-chat_response dtype: string - name: llama2-7b-chat_harmful dtype: int64 - name: llama2-7b-chat_action dtype: int64 - name: vicuna-7b_response dtype: string - name: vicuna-7b_harmful dtype: int64 - name: vicuna-7b_action dtype: int64 splits: - name: train num_bytes: 3997193 num_examples: 939 download_size: 1709142 dataset_size: 3997193 --- # Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs ## Overview *Do not answer* is an open-source dataset to evaluate LLMs' safety mechanism at a low cost. The dataset is curated and filtered to consist only of prompts to which responsible language models do not answer. Besides human annotations, *Do not answer* also implements model-based evaluation, where a 600M fine-tuned BERT-like evaluator achieves comparable results with human and GPT-4. ## Instruction Taxonomy Instructions are organized by a three-level hierarchical taxonomy, covering 61 specific harms. ![taxonomy](assets/dna.png) *Do not answer* collects 939 instructions, the distribution across five risk areas and 12 harm types is shown below: ![risk_area](assets/instruction_dist.png) ## Response Assessment We assess six models' responses (GPT-4, ChatGPT, Claude, LLaMA-2, Vicuna, ChatGLM2) from two levels. * Harmfulness: whether a response is harmful (a binary label). * Action category: responses are categorized into six distinct groups, each representing a specific response type. ![action](assets/action.png) ### Human Evaluation High percentage of safe responses across five risk areas for six models demonstrates that they are basically safe. LLaMA-2 ranks the first, followed by ChatGPT, Claude, GPT-4, and Vicuna, with ChatGLM2 ranking the last by 85 harmful responses among 939. * Harmlessness Rank ![harmful_num](assets/harmful_num.png) * Action Category Distribution * ![action_distribution](assets/action_dist.png) ### Automatic Evaluation Longformer evaluators achieve comparable results with human and GPT-4. * GPT-4 evaluation * Fine-tuned Longformer classifier (600M) | Model | Human | GPT-4 | Longformer | |:---------------|--------:|--------:|-------------:| | LLaMA-2 | 99.7 | 99.4 | 99.3 | | ChatGPT | 98.5 | 97.7 | 97.9 | | Claude | 98.3 | 98.3 | 97.6 | | GPT-4 | 97.6 | 96.5 | 97.2 | | vicuna | 94.5 | 94.9 | 95.0 | | ChatGLM2 | 90.9 | 92.9 | 92.9 | ## Citation If our work is useful for your own, you can cite us with the following BibTex entry: ```bibtex @misc{wang2023donotanswer, author = {Wang, Yuxia, and Li, Haonan and Han, Xudong and Nakov, Preslav and Baldwin, Timothy}, title = {Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs}, year = {2023}, howpublished = {arXiv preprint arXiv:2308.13387}, } ``` ## License All datasets in this repository are released under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-nc-sa/4.0/). All source files in this repository are released under the Apache 2.0 license, the text of which can be found in the LICENSE file.

[ -0.1874537467956543, -0.7977202534675598, 0.5509212613105774, -0.11248783767223358, -0.16249346733093262, -0.06645555794239044, 0.25858739018440247, -0.5452867746353149, -0.06356805562973022, 0.3743128776550293, -0.6380550265312195, -0.8323789238929749, -0.5099366307258606, 0.2322638779878...

null

Azure99/blossom-math-v2

Azure99

2023-09-05T03:28:13Z

21

0

null

[ "task_categories:text-generation", "task_categories:text2text-generation", "size_categories:10K<n<100K", "language:zh", "license:apache-2.0", "region:us" ]

2023-09-05T03:28:13Z

2023-09-05T03:19:29.000Z

2023-09-05T03:19:29

--- license: apache-2.0 task_categories: - text-generation - text2text-generation language: - zh size_categories: - 10K<n<100K --- # BLOSSOM MATH V2 ### 介绍 Blossom Math V2是基于Math23K和GSM8K衍生而来的中英双语数学对话数据集，适用于数学问题微调。相比于blossom-math-v1，新增了2500条GSM8K数据和翻译为中文的2500条GSM8K-CN数据。此外，优化了答案的检查逻辑，还移除了<<1+1=2>>等计算步骤，以统一推理步骤的风格。本数据集采用全量Math23K、GSM8K和翻译后的GSM8K的问题，随后调用gpt-3.5-turbo-0613生成结果，并使用原始数据集中的答案对生成的结果进行验证，过滤掉错误答案，很大程度上保证了问题和答案的准确性。本次发布了全量数据的25%，包含10K记录。 ### 语言中文和英文 ### 数据集结构每条数据代表一个完整的题目及答案，包含id、input、output、answer、dataset四个字段。 - id：字符串，代表原始数据集中的题目id，与dataset字段结合可确定唯一题目。 - input：字符串，代表问题。 - output：字符串，代表gpt-3.5-turbo-0613生成的答案。 - answer：字符串，代表正确答案。 - dataset：字符串，代表原始数据集。 ### 数据集限制本数据集的所有响应均由gpt-3.5-turbo-0613生成，并经过初步校验，但仍可能包含不准确的回答。

[ -0.32095199823379517, -0.6685713529586792, 0.18161076307296753, 0.7725106477737427, -0.6781514883041382, -0.06783218681812286, 0.22066541016101837, -0.15589432418346405, 0.27801772952079773, 0.24460305273532867, -0.878951907157898, -0.5524999499320984, -0.7650193572044373, -0.0787137970328...

null

TrainingDataPro/ripe-strawberries-detection

TrainingDataPro

2023-09-26T08:38:14Z

21

2

null

[ "task_categories:image-classification", "task_categories:image-to-image", "task_categories:object-detection", "language:en", "license:cc-by-nc-nd-4.0", "code", "biology", "region:us" ]

2023-09-26T08:38:14Z

2023-09-08T09:29:07.000Z

2023-09-08T09:29:07

--- language: - en license: cc-by-nc-nd-4.0 task_categories: - image-classification - image-to-image - object-detection tags: - code - biology dataset_info: features: - name: id dtype: int32 - name: name dtype: string - name: image dtype: image - name: mask dtype: image - name: width dtype: uint16 - name: height dtype: uint16 - name: shapes sequence: - name: label dtype: class_label: names: '0': strawberry - name: type dtype: string - name: points sequence: sequence: float32 - name: rotation dtype: float32 - name: attributes sequence: - name: name dtype: string - name: text dtype: string splits: - name: train num_bytes: 127730244 num_examples: 40 download_size: 126412271 dataset_size: 127730244 --- # Ripe Strawberries Detection The dataset consists of photos of strawberries for the identification and recognition of **ripe berries**. The images are annotated with **bounding boxes** that accurately demarcate the location of the ripe strawberries within the image. Each image in the dataset showcases a strawberry plantation, and includes a diverse range of *backgrounds, lighting conditions, and orientations*. The photos are captured from various *angles and distances*, providing a realistic representation of strawberries. The dataset can be utilised for enabling advancements in *strawberry production, quality control, and greater precision in agricultural practices*. ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F2d778d74efed2287072dc1757ff9953c%2FFrame%209.png?generation=1694156229544667&alt=media) # Get the dataset ### This is just an example of the data Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=ripe-strawberries-detection) to discuss your requirements, learn about the price and buy the dataset. # Dataset structure - **images** - contains of original images of strawberries - **boxes** - includes bounding box labeling for the original images - **annotations.xml** - contains coordinates of the bounding boxes and labels, created for the original photo # Data Format Each image from `images` folder is accompanied by an XML-annotation in the `annotations.xml` file indicating the coordinates of the bounding boxes for ripe strawberries detection. For each point, the x and y coordinates are provided. Visibility of the ripe strawberry is also provided by the attribute **occluded** (0, 1). # Example of XML file structure ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F88f5b20367a30de6a40961fb40ccacc6%2Fcarbon.png?generation=1694156401436654&alt=media) # Strawberry Detection might be made in accordance with your requirements. ## [**TrainingData**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=ripe-strawberries-detection) provides high-quality data annotation tailored to your needs More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets** TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**

[ -0.3831401765346527, -0.5840170383453369, 0.3552841246128082, -0.18327274918556213, 0.05860058218240738, -0.19983519613742828, 0.023963259533047676, -0.6440508961677551, 0.32349443435668945, 0.9856953620910645, -0.7947802543640137, -0.7224445343017578, -0.49765512347221375, 0.1021678000688...

null

MikhailT/speaker-embeddings

MikhailT

2023-09-22T08:01:44Z

21

0

null

[ "region:us" ]

2023-09-22T08:01:44Z

2023-09-19T12:34:03.000Z

2023-09-19T12:34:03

--- configs: - config_name: speakers version: 1.0.0 data_files: data/speakers.jsonl - config_name: models version: 1.0.0 data_files: data/models.jsonl - config_name: datasets version: 1.0.0 data_files: data/datasets.jsonl - config_name: dataset_utterances version: 1.0.0 data_files: - split: aru path: data/aru/utterances*.jsonl - split: cmu_arctic path: data/cmu_arctic/utterances*.jsonl - config_name: utterance_embeddings version: 1.0.0 data_files: - split: aru path: data/aru/utterance_embeddings*.jsonl - split: cmu_arctic path: data/cmu_arctic/utterance_embeddings*.jsonl - config_name: speaker_embeddings version: 1.0.0 data_files: - split: speechbrain_spkrec_xvect_voxceleb path: data/*/speaker_embeddings_*001.jsonl - split: speechbrain_spkrec_ecapa_voxceleb path: data/*/speaker_embeddings_*002.jsonl - split: speechbrain_spkrec_xvect_voxceleb_mean path: data/*/speaker_embeddings_mean_001.jsonl - split: speechbrain_spkrec_ecapa_voxceleb_mean path: data/*/speaker_embeddings_mean_002.jsonl - split: speechbrain_spkrec_xvect_voxceleb_sets path: data/*/speaker_embeddings_sets_001.jsonl - split: speechbrain_spkrec_ecapa_voxceleb_sets path: data/*/speaker_embeddings_sets_002.jsonl dataset_info: - config_name: speakers features: - name: id dtype: string - name: name dtype: string - name: lang dtype: string - name: sex dtype: string - name: age dtype: int32 - name: country dtype: string - name: accent dtype: string - config_name: models features: - name: id dtype: string - name: name dtype: string - name: size dtype: int32 - name: sample_rate dtype: int32 - config_name: datasets features: - name: id dtype: string - name: name dtype: string - name: sample_rate dtype: int32 - config_name: dataset_utterances features: - name: id dtype: string - name: name dtype: string - name: duration dtype: float32 - name: speaker_id dtype: string - name: dataset_id dtype: string - config_name: utterance_embeddings features: - name: speaker_id dtype: string - name: file_id dtype: string - name: dataset_id dtype: string - name: model_id dtype: string - name: embedding sequence: float32 - config_name: speaker_embeddings features: - name: speaker_id dtype: string - name: model_id dtype: string - name: set dtype: string - name: embedding sequence: float32 pretty_name: Speaker Embeddings ---

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null

gary-roach/NLP

gary-roach

2023-09-21T01:40:44Z

21

0

null

[ "region:us" ]

2023-09-21T01:40:44Z

2023-09-21T01:32:07.000Z

2023-09-21T01:32:07

Entry not found

[ -0.32276487350463867, -0.22568444907665253, 0.8622263073921204, 0.43461570143699646, -0.5282988548278809, 0.7012969255447388, 0.7915717363357544, 0.07618642598390579, 0.7746027112007141, 0.25632190704345703, -0.7852815389633179, -0.22573848068714142, -0.910447895526886, 0.5715675354003906,...

null

sarahpann/gsm8k_small_test

sarahpann

2023-09-23T20:21:36Z

21

0

null

[ "region:us" ]

2023-09-23T20:21:36Z

2023-09-23T20:18:53.000Z

2023-09-23T20:18:53

--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: prompt dtype: string - name: answer dtype: string splits: - name: test num_bytes: 109109 num_examples: 200 download_size: 64934 dataset_size: 109109 --- # Dataset Card for "gsm8k_small_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.7259178757667542, -0.13163627684116364, 0.21974387764930725, 0.07131125032901764, -0.32468336820602417, -0.2717601954936981, 0.20831425487995148, 0.025113727897405624, 0.7604683637619019, 0.3655509650707245, -0.7540602684020996, -0.6294835805892944, -0.5146699547767639, -0.0584688000380...

null

Weni/Zeroshot_Train-20K_other_tweet-format

Weni

2023-09-28T18:41:59Z

21

0

null

[ "task_categories:zero-shot-classification", "size_categories:10K<n<100K", "language:pt", "region:us" ]

2023-09-28T18:41:59Z

2023-09-28T15:42:14.000Z

2023-09-28T15:42:14

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: source_text dtype: string - name: target_text dtype: string splits: - name: train num_bytes: 4369715 num_examples: 20000 download_size: 1752054 dataset_size: 4369715 language: - pt size_categories: - 10K<n<100K task_categories: - zero-shot-classification --- # Dataset Card for "Zeroshot_Train-20K_other_tweet-format" This dataset is a train dataset for the Zeroshot models. It has 20.000 data in a prompt format exclusively for train with class 'other' in Brazilian Portuguese. Prompt: ``` "Classifique o tweet entre 'classe1', 'classe2', 'classe3', 'classe4', 'other' \\n\\nTweet: frase \\n\\nLabel: 'other' ``` The dataset was divided as follows: ``` - 6,000 data: prompt with class option without target class (other) - 7,000 data: prompt with class option + target class included as an option. target class is not correct - 7,000 data: prompt with class option + target class. target class is correct ``` ## How to load and use this dataset: ``` from datasets import load_dataset dataset = load_dataset("Weni/Zeroshot_Train-20K_other_tweet-format") dataset ``` [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.23933596909046173, -0.30037230253219604, 0.09042870253324509, 0.5432130694389343, -0.4443835914134979, -0.030664831399917603, -0.03044438175857067, -0.28130003809928894, 0.4700409471988678, 0.3689350187778473, -0.7721100449562073, -0.6927088499069214, -0.466494619846344, -0.057779282331...

null

jfrei/GPTNERMED

jfrei

2023-10-08T22:05:18Z

21

0

null

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:de", "bio", "biomedical", "medical", "c...

2023-10-08T22:05:18Z

2023-10-05T12:39:54.000Z

2023-10-05T12:39:54

--- annotations_creators: - machine-generated language: - de language_creators: - machine-generated license: [] multilinguality: - monolingual pretty_name: GPTNERMED size_categories: - 1K<n<10K source_datasets: - original tags: - bio - biomedical - medical - clinical task_categories: - token-classification task_ids: - named-entity-recognition --- # GPTNERMED Dataset for German medical NER entities ## Dataset Description - **Repository:** https://github.com/frankkramer-lab/GPTNERMED - **Paper:** https://doi.org/10.1016/j.jbi.2023.104478 - **ArXiv-Preprint:** https://arxiv.org/abs/2208.14493 ## Dataset Summary This dataset contains the synthetic German sentences with annotated entities (`Medikation`, `Dosis`, `Diagnose`) from the GPTNERMED project. The sentences as well as the annotations are **not** manually validated by medical professionals and therefore this dataset is **no** gold standard dataset. The dataset consists of 9,845 sentences (121,027 tokens by SpaCy Tokenizer, 245,107 tokens by the GPT tokenizer) with the following labels: | Label | Count | #Tokens (SpaCy) | | --- | --- | -- | | Medikation | 9868 | 10138 | | Dosis | 7547 | 15845 | | Diagnose | 5996 | 7656 | ## Dataset Structure The train/test/dev-split (80%, 10%, 10%) of the data loader is as follows:\ `<-- train: 0.8 --><-- test: 0.1 --><-- dev: 0.1 -->`\ The splits are selected arbitrarily as the dataloader requires a split configuration. All sample sentences are however homogeneous in origin and splits could also be performed otherwise. Every sample is a sentence with its text (property `sentence`) and its corresponding NER labels (property `ner_labels` / List of labels).\ Every NER label entry has a char-wise start and stop index (property `start`, `stop`) and a label class (property `ner_class`). ### Citation Information If you like our work, cite our paper and give us a star on GitHub.\ (See the links above)

[ -0.37037554383277893, -0.7462859749794006, 0.45078369975090027, 0.10975170135498047, -0.14462706446647644, -0.3314889371395111, -0.18236258625984192, -0.3088720440864563, 0.35071277618408203, 0.4625687003135681, -0.5291818976402283, -0.6116166114807129, -0.6515517830848694, 0.3632512390613...

null

ShashiVish/cover-letter-dataset

ShashiVish

2023-10-15T15:20:47Z

21

1

null

[ "region:us" ]

2023-10-15T15:20:47Z

2023-10-14T14:37:08.000Z

2023-10-14T14:37:08

--- dataset_info: features: - name: Job Title dtype: string - name: Preferred Qualifications dtype: string - name: Hiring Company dtype: string - name: Applicant Name dtype: string - name: Past Working Experience dtype: string - name: Current Working Experience dtype: string - name: Skillsets dtype: string - name: Qualifications dtype: string - name: Cover Letter dtype: string splits: - name: train num_bytes: 1048886.142857143 num_examples: 813 - name: test num_bytes: 450259.85714285716 num_examples: 349 download_size: 507518 dataset_size: 1499146.0 --- # Dataset Card for "cover-letter-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.5082902312278748, 0.025600997731089592, 0.20393604040145874, 0.1738148182630539, -0.055836014449596405, 0.018329817801713943, 0.33202821016311646, -0.014157934114336967, 1.02180814743042, 0.576270341873169, -1.0422379970550537, -0.9465879201889038, -0.6072818636894226, -0.27037206292152...

null

bobbybelajar/AmazonMixedLength

bobbybelajar

2023-10-15T07:19:36Z

21

0

null

[ "region:us" ]

2023-10-15T07:19:36Z

2023-10-15T07:19:12.000Z

2023-10-15T07:19:12

Entry not found

[ -0.3227645754814148, -0.22568479180335999, 0.8622264862060547, 0.43461528420448303, -0.52829909324646, 0.7012971639633179, 0.7915720343589783, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104477167129517, 0.5715674161911011, -...

null

tyzhu/find_first_sent_train_100_eval_10

tyzhu

2023-10-31T14:48:31Z

21

0

null

[ "region:us" ]

2023-10-31T14:48:31Z

2023-10-19T15:56:50.000Z

2023-10-19T15:56:50

--- dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: title dtype: string - name: context dtype: string splits: - name: train num_bytes: 267331 num_examples: 210 - name: validation num_bytes: 10399 num_examples: 10 download_size: 135617 dataset_size: 277730 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* --- # Dataset Card for "find_first_sent_train_100_eval_10" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.6245697736740112, -0.32223349809646606, 0.2845980226993561, 0.4964664578437805, -0.0682099312543869, -0.11849130690097809, 0.24953055381774902, 0.307274729013443, 0.7851583957672119, 0.3308938145637512, -1.000707745552063, -0.7305555939674377, -0.6303924918174744, -0.17641472816467285, ...

null

kardosdrur/folketinget-discussions

kardosdrur

2023-10-24T11:53:06Z

21

0

null

[ "license:mit", "region:us" ]

2023-10-24T11:53:06Z

2023-10-24T08:48:35.000Z

2023-10-24T08:48:35

--- license: mit configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: comment dtype: string - name: response dtype: string splits: - name: train num_bytes: 7032676.035654362 num_examples: 3814 - name: test num_bytes: 1759090.9643456375 num_examples: 954 download_size: 4898174 dataset_size: 8791767.0 --- # Discussions in Folketinget The dataset is based on data from Folketinget in the Danish Gigaword corpus. Comment-response pairs are purely extracted on the basis of heuristics, and have not been manually evaluated. The dataset was created for aiding the training of sentence transformer models in the Danish Foundation Models project. The dataset is currently not recommended for production use.

[ -0.6056040525436401, -0.6305659413337708, 0.4549265503883362, 0.12162110954523087, -0.2183971405029297, 0.14110037684440613, -0.17779475450515747, -0.48550543189048767, 0.5974078178405762, 0.8436447978019714, -0.7700057029724121, -0.16280964016914368, -0.4659677743911743, 0.325259476900100...

null

Schandkroete/RandomEmployeeProfilesV1

Schandkroete

2023-10-27T21:56:58Z

21

1

null

[ "region:us" ]

2023-10-27T21:56:58Z

2023-10-27T21:53:51.000Z

2023-10-27T21:53:51

Entry not found

[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

theophilusijiebor1/chest-xray

theophilusijiebor1

2023-10-29T03:08:57Z

21

0

null

[ "region:us" ]

2023-10-29T03:08:57Z

2023-10-29T03:07:44.000Z

2023-10-29T03:07:44

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': NORMAL '1': PNEUMONIA splits: - name: train num_bytes: 3186635036.504 num_examples: 5216 - name: validation num_bytes: 3030633.0 num_examples: 16 - name: test num_bytes: 79062317.0 num_examples: 624 download_size: 1230487171 dataset_size: 3268727986.504 --- # Dataset Card for "chest-xray" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.5514674186706543, 0.040062543004751205, 0.3523052930831909, -0.035563696175813675, -0.4192042052745819, 0.0015731991734355688, 0.5902133584022522, -0.18907251954078674, 0.8261512517929077, 0.5640028119087219, -0.8294750452041626, -0.8280811905860901, -0.7145045399665833, -0.368674665689...

null

piecake/mulqa

piecake

2023-10-29T10:57:33Z

21

0

null

[ "region:us" ]

2023-10-29T10:57:33Z

2023-10-29T10:36:23.000Z

2023-10-29T10:36:23

--- # For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/datasets-cards {} --- # Dataset Card for Dataset Name  This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## Dataset Details ### Dataset Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

[ -0.5322356224060059, -0.5534716844558716, 0.1290130317211151, 0.23470577597618103, -0.39626216888427734, -0.11762470006942749, -0.03545305132865906, -0.6389272212982178, 0.5699822306632996, 0.7838326692581177, -0.7834625840187073, -0.9173274040222168, -0.55633145570755, 0.13078093528747559...

null

abhinand/tamil-llama-eval

abhinand

2023-11-13T17:20:06Z

21

1

null

[ "task_categories:text-generation", "size_categories:n<1K", "language:ta", "license:gpl", "arxiv:2311.05845", "region:us" ]

2023-11-13T17:20:06Z

2023-10-29T15:27:53.000Z

2023-10-29T15:27:53

--- license: gpl task_categories: - text-generation language: - ta pretty_name: tamil-llama-eval size_categories: - n<1K --- # Dataset Card for "tamil-alpaca" This repository includes evaluation instructions to quickly test the Tamil LLaMA family of instruction models. To dive deep into the development and capabilities of the models, please read the [research paper](https://arxiv.org/abs/2311.05845) and the [introductory blog post (WIP) ]() that outlines our journey and the model's potential impact. **GitHub Repository:** [https://github.com/abhinand5/tamil-llama](https://github.com/abhinand5/tamil-llama) ## Models evaluated using this dataset | Task Type | [Tamil-LLaMA-7B](abhinand/tamil-llama-7b-instruct-v0.1) | [Tamil-LLaMA-13B](abhinand/tamil-llama-13b-instruct-v0.1) | [gpt-3.5-turbo](https://platform.openai.com/docs/models/gpt-3-5) | |-----------------|----------------|-----------------|---------------| | Question Answering | 77.00 | 75.33 | 54.33 | | Open-ended QA | 84.47 | 85.26 | 58.68 | | Reasoning | 47.50 | 64.25 | 63.50 | | Literature | 45.50 | 40.00 | 71.00 | | Entertainment | 43.33 | 50.00 | 60.00 | | Creative Writing| 92.50 | 95.62 | 59.69 | | Translation | 60.56 | 66.67 | 92.78 | | Coding | 63.57 | 76.07 | 57.14 | | Ethics | 23.75 | 57.50 | 40.00 | | **Overall** | **63.83** | **71.17** | **61.33** | ## Meet the Developers Get to know the creators behind this innovative model and follow their contributions to the field: - [Abhinand Balachandran](https://www.linkedin.com/in/abhinand-05/) ## Citation If you use this model or any of the the Tamil-Llama datasets in your research, please cite: ```bibtex @misc{balachandran2023tamilllama, title={Tamil-Llama: A New Tamil Language Model Based on Llama 2}, author={Abhinand Balachandran}, year={2023}, eprint={2311.05845}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

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null

dltjdgh0928/open_orca_ko_10000

dltjdgh0928

2023-10-30T06:15:24Z

21

1

null

[ "region:us" ]

2023-10-30T06:15:24Z

2023-10-30T05:58:33.000Z

2023-10-30T05:58:33

Entry not found

[ -0.3227645754814148, -0.22568479180335999, 0.8622263669967651, 0.43461522459983826, -0.52829909324646, 0.7012971639633179, 0.7915719747543335, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104475975036621, 0.5715674161911011, -...

null

Eitanli/meal_type

Eitanli

2023-11-02T13:51:48Z

21

0

null

[ "region:us" ]

2023-11-02T13:51:48Z

2023-10-30T08:16:00.000Z

2023-10-30T08:16:00

--- dataset_info: features: - name: id dtype: int64 - name: recipe dtype: string - name: meal_type_title dtype: string splits: - name: train num_bytes: 107900952 num_examples: 74465 download_size: 54288491 dataset_size: 107900952 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "meal_type" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.368934690952301, -0.34682050347328186, 0.11957457661628723, 0.19810724258422852, 0.030622044578194618, -0.05710891634225845, 0.3118149936199188, -0.09473884850740433, 1.1204071044921875, 0.5640775561332703, -0.5865943431854248, -0.6661612391471863, -0.6003037691116333, -0.00843838416039...

null

phanvancongthanh/enamine_leadlike_600M

phanvancongthanh

2023-10-31T13:58:25Z

21

0

null

[ "region:us" ]

2023-10-31T13:58:25Z

2023-10-31T11:34:47.000Z

2023-10-31T11:34:47

Entry not found

[ -0.3227645754814148, -0.22568479180335999, 0.8622264862060547, 0.43461528420448303, -0.52829909324646, 0.7012971639633179, 0.7915720343589783, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104477167129517, 0.5715674161911011, -...

null

Falah/fashion_moodboards_prompts

Falah

2023-11-19T08:59:15Z

21

0

null

[ "region:us" ]

2023-11-19T08:59:15Z

2023-11-01T06:36:25.000Z

2023-11-01T06:36:25

--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 141480 num_examples: 1000 download_size: 22359 dataset_size: 141480 --- # Dataset Card for "fashion_moodboards_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.5473146438598633, -0.32294338941574097, 0.4426268935203552, 0.4725632667541504, -0.37791532278060913, -0.045761074870824814, 0.26940661668777466, 0.18323250114917755, 0.9924519062042236, 0.28700363636016846, -1.3817654848098755, -0.9960726499557495, -0.36243757605552673, -0.183055996894...

null

meisin123/iban_speech_corpus

meisin123

2023-11-02T04:39:07Z

21

0

null

[ "region:us" ]

2023-11-02T04:39:07Z

2023-11-01T10:12:03.000Z

2023-11-01T10:12:03

--- dataset_info: features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 1014986154.58 num_examples: 3132 download_size: 981436514 dataset_size: 1014986154.58 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "iban_speech_corpus" ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Dataset Creation](#dataset-creation) - [Source Data](#source-data) - [Additional Information](#additional-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** The original dataset is found on [Sarah Juan's github link](https://github.com/sarahjuan/iban) - **Paper:** "Using Resources from a closely-Related language to develop ASR for a very under-resourced Language: A case study for Iban" ### Dataset Summary This Iban speech corpus is used for training of a Automatic Speech Recognition (ASR) model. This dataset contains the audio files (wav files) with its corresponding transcription. For other resources such as pronunciation dictionary and Iban language model, please refer to the original dataset respository [here](https://github.com/sarahjuan/iban). ### How to use The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. ```python from datasets import load_dataset dataset = load_dataset("meisin123/iban_speech_corpus", split="train") ``` ## Dataset Structure ### Data Instances ``` {'audio': {'path': 'ibf_001_001.wav', 'array': array([ 5.72814941e-01, 5.49011230e-01, -1.82495117e-02, ..., -2.31628418e-02, -1.26342773e-02, -3.05175781e-05]), 'sampling_rate': 16000}, 'transcription': 'pukul sepuluh malam'} ``` ### Data Fields - audio: A dictionary containing the audio filename, the decoded audio array, and the sampling rate. - transcription: the transcription of the audio file. ## Dataset Creation - Iban Data collected by Sarah Samson Juan and Laurent Besacier ### Source Data The audio files are news data provided by a local radio station in Sarawak, Malaysia. ## Additional Information ### Citation Information Details on the corpora and the experiments on iban ASR can be found in the following list of publication. The original authors appreciate if you cite them if you intend to publish. ``` @inproceedings{Juan14, Author = {Sarah Samson Juan and Laurent Besacier and Solange Rossato}, Booktitle = {Proceedings of Workshop for Spoken Language Technology for Under-resourced (SLTU)}, Month = {May}, Title = {Semi-supervised G2P bootstrapping and its application to ASR for a very under-resourced language: Iban}, Year = {2014}} @inproceedings{Juan2015, Title = {Using Resources from a closely-Related language to develop ASR for a very under-resourced Language: A case study for Iban}, Author = {Sarah Samson Juan and Laurent Besacier and Benjamin Lecouteux and Mohamed Dyab}, Booktitle = {Proceedings of INTERSPEECH}, Year = {2015}, Address = {Dresden, Germany}, Month = {September}} ``` ### Contributions Thanks to [meisin](https://github.com/meisin) for adding this dataset.

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null

cynefin/llama-2-7b-chat-aave

cynefin

2023-11-01T16:51:04Z

21

0

null

[ "region:us" ]

2023-11-01T16:51:04Z

2023-11-01T11:02:34.000Z

2023-11-01T11:02:34

Entry not found

[ -0.3227645754814148, -0.22568479180335999, 0.8622264862060547, 0.43461528420448303, -0.52829909324646, 0.7012971639633179, 0.7915720343589783, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104477167129517, 0.5715674161911011, -...

null

HumanF-MarkrAI/WIKI_QA_Near_dedup

HumanF-MarkrAI

2023-11-03T08:55:52Z

21

0

null

[ "license:cc-by-nc-sa-4.0", "region:us" ]

2023-11-03T08:55:52Z

2023-11-02T10:57:34.000Z

2023-11-02T10:57:34

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: input dtype: float64 - name: output dtype: string - name: instruction dtype: string splits: - name: train num_bytes: 145724140 num_examples: 137505 download_size: 87382170 dataset_size: 145724140 license: cc-by-nc-sa-4.0 --- # Dataset Card for "WIKI_QA_Near_dedup" **The license is `cc-by-nc-sa`.** ## Datasets Details **Developers** SeungyooLee (DopeorNopeLee) WIKI_QA_Near_dedup dataset was created by Near dedup algorithm to reduce similarity. **It's original source is [maywell/wikidata_QA](maywell/wikidata_QA), which created by an innovative developer named [maywell(Jeonghwan Park)](https://huggingface.co/maywell).** It follows "cc-by-nc-sa-4.0 lisence" policy.

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null

jackhogan/agemo_json_prompts

jackhogan

2023-11-02T16:19:45Z

21

0

null

[ "region:us" ]

2023-11-02T16:19:45Z

2023-11-02T11:17:04.000Z

2023-11-02T11:17:04

--- dataset_info: features: - name: text dtype: string - name: correct dtype: string - name: scrambled dtype: string splits: - name: train num_bytes: 5053590.0 num_examples: 5319 - name: test num_bytes: 561510.0 num_examples: 591 download_size: 1349329 dataset_size: 5615100.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "agemo_json_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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null

Pclanglais/Brahe-Novels

Pclanglais

2023-11-02T23:11:30Z

21

1

null

[ "license:cc0-1.0", "region:us" ]

2023-11-02T23:11:30Z

2023-11-02T23:04:43.000Z

2023-11-02T23:04:43

--- license: cc0-1.0 --- The Brahe-Novels dataset is a collection of annotated novel excerpts in the public domain. It was originally created to train Brahe, an LLM fine-tuned for literary analysis. Most of the texts come from the Gutenberg project. The annotations include a mix of synthetic data and manual annotations. In accordance with the principles laid out by the US copyright office, all synthetic data and hybrid synthetic data are in the public domain as well.

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null

pyimagesearch/blog-title

pyimagesearch

2023-11-03T19:41:10Z

21

0

null

[ "license:mit", "region:us" ]

2023-11-03T19:41:10Z

2023-11-03T18:34:29.000Z

2023-11-03T18:34:29

--- license: mit ---

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null

Diegulio/PetClassification

Diegulio

2023-11-05T04:26:06Z

21

0

null

[ "region:us" ]

2023-11-05T04:26:06Z

2023-11-05T04:25:38.000Z

2023-11-05T04:25:38

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': No detectado '1': affenpinscher '2': afghan_hound '3': african_hunting_dog '4': airedale '5': american_staffordshire_terrier '6': appenzeller '7': australian_terrier '8': basenji '9': basset '10': beagle '11': bedlington_terrier '12': bernese_mountain_dog '13': black-and-tan_coonhound '14': blenheim_spaniel '15': bloodhound '16': bluetick '17': border_collie '18': border_terrier '19': borzoi '20': boston_bull '21': bouvier_des_flandres '22': boxer '23': brabancon_griffon '24': briard '25': brittany_spaniel '26': bull_mastiff '27': cairn '28': cardigan '29': chesapeake_bay_retriever '30': chihuahua '31': chow '32': clumber '33': cocker_spaniel '34': collie '35': curly-coated_retriever '36': dandie_dinmont '37': dhole '38': dingo '39': doberman '40': english_foxhound '41': english_setter '42': english_springer '43': entlebucher '44': eskimo_dog '45': flat-coated_retriever '46': french_bulldog '47': gato '48': german_shepherd '49': german_short-haired_pointer '50': giant_schnauzer '51': golden_retriever '52': gordon_setter '53': great_dane '54': great_pyrenees '55': greater_swiss_mountain_dog '56': groenendael '57': ibizan_hound '58': irish_setter '59': irish_terrier '60': irish_water_spaniel '61': irish_wolfhound '62': italian_greyhound '63': japanese_spaniel '64': keeshond '65': kelpie '66': kerry_blue_terrier '67': komondor '68': kuvasz '69': labrador_retriever '70': lakeland_terrier '71': leonberg '72': lhasa '73': malamute '74': malinois '75': maltese_dog '76': mexican_hairless '77': miniature_pinscher '78': miniature_poodle '79': miniature_schnauzer '80': newfoundland '81': norfolk_terrier '82': norwegian_elkhound '83': norwich_terrier '84': old_english_sheepdog '85': otterhound '86': papillon '87': pekinese '88': pembroke '89': pomeranian '90': pug '91': redbone '92': rhodesian_ridgeback '93': rottweiler '94': saint_bernard '95': saluki '96': samoyed '97': schipperke '98': scotch_terrier '99': scottish_deerhound '100': sealyham_terrier '101': shetland_sheepdog '102': shih-tzu '103': siberian_husky '104': silky_terrier '105': soft-coated_wheaten_terrier '106': staffordshire_bullterrier '107': standard_poodle '108': standard_schnauzer '109': sussex_spaniel '110': tibetan_mastiff '111': tibetan_terrier '112': toy_poodle '113': toy_terrier '114': vizsla '115': walker_hound '116': weimaraner '117': welsh_springer_spaniel '118': west_highland_white_terrier '119': whippet '120': wire-haired_fox_terrier '121': yorkshire_terrier splits: - name: train num_bytes: 344179685.94 num_examples: 7499 - name: validation num_bytes: 29205702.0 num_examples: 834 - name: test num_bytes: 81732756.983 num_examples: 2083 download_size: 379294077 dataset_size: 455118144.923 --- # Dataset Card for "PetClassification" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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null

RachitD15673/DSA_problems_with_code

RachitD15673

2023-11-05T10:26:34Z

21

0

null

[ "region:us" ]

2023-11-05T10:26:34Z

2023-11-05T09:09:09.000Z

2023-11-05T09:09:09

Entry not found

[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

Ujan/github_classification_no_empty_readme

Ujan

2023-11-05T18:51:37Z

21

0

null

[ "region:us" ]

2023-11-05T18:51:37Z

2023-11-05T18:50:54.000Z

2023-11-05T18:50:54

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: names dtype: string - name: readmes dtype: string - name: topics dtype: string - name: labels dtype: string splits: - name: train num_bytes: 51299344.74701966 num_examples: 10334 - name: validation num_bytes: 6413659.126490169 num_examples: 1292 - name: test num_bytes: 6413659.126490169 num_examples: 1292 download_size: 29121376 dataset_size: 64126663.0 --- # Dataset Card for "github_classification_no_empty_readme" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.454999178647995, -0.16174374520778656, 0.2662765681743622, 0.012564101256430149, -0.3255958557128906, 0.025421343743801117, -0.04565252736210823, -0.08200530707836151, 0.9701449871063232, 0.523841142654419, -0.7977185249328613, -1.1055704355239868, -0.5461364984512329, -0.19592304527759...

null

kowndinya23/flan2022

kowndinya23

2023-11-09T07:10:33Z

21

0

null

[ "region:us" ]

2023-11-09T07:10:33Z

2023-11-09T06:49:11.000Z

2023-11-09T06:49:11

--- configs: - config_name: default data_files: - split: flan2021 path: data/flan2021-* - split: t0 path: data/t0-* - split: cot path: data/cot-* - split: niv2 path: data/niv2-* - split: dialog path: data/dialog-* dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: task_source dtype: string - name: task_name dtype: string - name: template_type dtype: string splits: - name: flan2021 num_bytes: 8988026240 num_examples: 5362361 - name: t0 num_bytes: 4602180562 num_examples: 1650308 - name: cot num_bytes: 209004809 num_examples: 183848 - name: niv2 num_bytes: 13104211362 num_examples: 10066896 - name: dialog num_bytes: 1024507265 num_examples: 553869 download_size: 16511300644 dataset_size: 27927930238 --- # Dataset Card for "flan2022" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.7777625918388367, -0.22854623198509216, 0.13734540343284607, 0.2484925091266632, -0.03060133196413517, -0.1322588324546814, 0.22366920113563538, -0.2977968156337738, 0.9536900520324707, 0.5755918622016907, -0.9104466438293457, -0.5695282816886902, -0.51900714635849, -0.23268893361091614...

null

fw1zr/rahul-gandhi-captions

fw1zr

2023-11-09T16:58:53Z

21

0

null

[ "region:us" ]

2023-11-09T16:58:53Z

2023-11-09T16:58:34.000Z

2023-11-09T16:58:34

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 4491114.0 num_examples: 116 download_size: 4452636 dataset_size: 4491114.0 --- # Dataset Card for "rahul-gandhi-captions" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.5244444608688354, -0.2024354636669159, 0.06294974684715271, 0.18529672920703888, -0.3119725286960602, 0.31233659386634827, 0.010114111937582493, -0.05708202347159386, 0.5696579217910767, 0.4180799722671509, -0.8513811826705933, -0.5097954273223877, -0.7064807415008545, 0.049259904772043...

null

alsubari/Israel-palestine-war

alsubari

2023-11-13T12:19:20Z

21

0

null

[ "task_categories:text-classification", "task_categories:text-generation", "task_categories:conversational", "task_categories:summarization", "task_categories:text2text-generation", "task_categories:sentence-similarity", "task_categories:zero-shot-classification", "task_categories:fill-mask", "langua...

2023-11-13T12:19:20Z

2023-11-09T21:10:23.000Z

2023-11-09T21:10:23

--- language: - en license: afl-3.0 task_categories: - text-classification - text-generation - conversational - summarization - text2text-generation - sentence-similarity - zero-shot-classification - fill-mask dataset_info: features: - name: id dtype: string - name: publish_channel dtype: string - name: title dtype: string - name: start_duration dtype: string - name: end_duration dtype: string - name: lengthSeconds dtype: int64 - name: total_comments dtype: int64 - name: total_unique_users dtype: int64 - name: comments list: - name: author dtype: string - name: channel dtype: string - name: cid dtype: string - name: heart dtype: bool - name: reply dtype: bool - name: text dtype: string - name: time dtype: string - name: time_parsed dtype: float64 - name: votes dtype: string splits: - name: train num_bytes: 274986362 num_examples: 310 download_size: 152975094 dataset_size: 274986362 configs: - config_name: default data_files: - split: train path: data/train-* tags: - not-for-all-audiences --- # Dataset Card for "Israel-palestine-war" comments on some youtube channels regarding Israel-palestine-war video published on the peroid # number of comment per channel as follow: {'NBCNews': 188490, 'aljazeeraenglish': 100164, 'CNN': 182272, 'Piers Morgan Uncensored': 128093, 'BBCNews': 267834, 'FoxNews': 161493} # data structure: Dataset({ features: ['id', 'publish_channel', 'title', 'start_duration', 'end_duration', 'lengthSeconds', 'total_comments', 'total_unique_users', 'comments'], num_rows: 310 }) # total number of videos: 310 total number of comments: 1028346

[ -0.8059569597244263, -0.5958005785942078, -0.047708660364151, 0.4725136458873749, -0.7441005110740662, 0.09650488942861557, -0.2835055887699127, 0.4304427206516266, 0.3910925090312958, 0.525567889213562, -0.8450614809989929, -0.879160463809967, -1.0285890102386475, 0.16555914282798767, -...

null

pphuc25/vlsp-train-vectorized

pphuc25

2023-11-11T17:47:59Z

21

0

null

[ "region:us" ]

2023-11-11T17:47:59Z

2023-11-11T17:25:18.000Z

2023-11-11T17:25:18

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: input_length dtype: int64 - name: labels sequence: int64 splits: - name: train num_bytes: 24115945291.875 num_examples: 171441 download_size: 24036430824 dataset_size: 24115945291.875 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "vlsp-train-vectorized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.5707027912139893, 0.1251530796289444, 0.1144338846206665, 0.3281501531600952, -0.28592076897621155, -0.07634536921977997, 0.2746751010417938, -0.14268070459365845, 0.7080532312393188, 0.3233305811882019, -0.7882729172706604, -0.6010646224021912, -0.6947891712188721, -0.38588935136795044...

null

eddielin0926/chinese-icd

eddielin0926

2023-11-18T14:49:09Z

21

1

null

[ "task_categories:text-classification", "size_categories:1M<n<10M", "language:zh", "language:en", "license:mit", "medical", "region:us" ]

2023-11-18T14:49:09Z

2023-11-11T17:39:25.000Z

2023-11-11T17:39:25

--- language: - zh - en license: mit size_categories: - 1M<n<10M task_categories: - text-classification pretty_name: chicd tags: - medical dataset_info: features: - name: year dtype: int32 - name: month dtype: int32 - name: 'no' dtype: int32 - name: death dtype: int32 - name: input_code dtype: int32 - name: result_code dtype: int32 - name: check dtype: bool - name: serial_no dtype: int32 - name: catalog dtype: int32 - name: inputs sequence: string - name: results sequence: string - name: icds sequence: string - name: encodes sequence: class_label: names: '0': L519 '1': A523 '2': I898 '3': A047 '4': E144 '5': C797 '6': C755 '7': K831 '8': B379 '9': S621 '10': C672 '11': K409 '12': D073 '13': A179 '14': I255 '15': K353 '16': C029 '17': W11 '18': D139 '19': R944 '20': V785 '21': T502 '22': C921 '23': K228 '24': S069(TR) '25': K226 '26': N501(nTR) '27': D136 '28': Q878 '29': S610 '30': L032 '31': T835 '32': O699 '33': K820 '34': V827 '35': K256 '36': M769 '37': C677 '38': K920 '39': C689 '40': T183 '41': T327 '42': B948 '43': T213 '44': C160 '45': R060 '46': T812 '47': F104 '48': I311 '49': I670 '50': C112 '51': H931 '52': K868 '53': S158 '54': R35 '55': L109 '56': T115 '57': G2009 '58': H348 '59': P012 '60': Q019 '61': V878 '62': G969 '63': H441 '64': K099 '65': M431 '66': X97 '67': C773 '68': J989 '69': F191 '70': Q445 '71': O691 '72': I110 '73': K109 '74': S121 '75': Q069 '76': D302 '77': K650 '78': D447 '79': N508 '80': V875 '81': E702 '82': J840 '83': T174 '84': S360(TR) '85': R798 '86': N428 '87': C629 '88': O690 '89': O441 '90': M624 '91': I519 '92': R093 '93': I471 '94': A78 '95': T818 '96': X78 '97': D133 '98': P252 '99': N920 '100': K627 '101': V455 '102': B86 '103': C509 '104': P229 '105': V892 '106': I350(nRH) '107': H309 '108': C944 '109': T360 '110': S618 '111': J9840 '112': L409 '113': L038 '114': B457 '115': H431(nTR) '116': R509 '117': F952 '118': A421 '119': S208 '120': A509 '121': I472 '122': H108 '123': B004 '124': C52 '125': P052 '126': E721 '127': F072 '128': W14 '129': T561 '130': C153 '131': Q639 '132': N209 '133': T794 '134': T110 '135': N041 '136': K918 '137': S010 '138': J940 '139': M726 '140': H350 '141': E782 '142': C632 '143': V339 '144': C223 '145': P544 '146': J128 '147': C252 '148': M954 '149': T210 '150': C752 '151': C720 '152': S275 '153': R11 '154': S699 '155': E785 '156': E169 '157': V804 '158': Y839 '159': E210 '160': K902 '161': V275 '162': E720 '163': K262 '164': R202 '165': T357 '166': J81 '167': P015 '168': I423 '169': V389 '170': G378 '171': S315 '172': Q257 '173': T809 '174': V294 '175': Q269 '176': C695 '177': D519 '178': S710 '179': N764 '180': B853 '181': I748 '182': T858 '183': T424 '184': O998 '185': B371 '186': J948 '187': I318 '188': S450 '189': C139 '190': E771 '191': T400 '192': G822(nTR) '193': J387 '194': A029 '195': J1110 '196': D449 '197': K620 '198': B450 '199': S311 '200': C441 '201': O365 '202': D125 '203': P111 '204': T797 '205': S829 '206': D261 '207': I959 '208': Q789 '209': C442 '210': L209 '211': R292 '212': Q2830 '213': S909 '214': C73 '215': K257 '216': M1998 '217': S328 '218': D691 '219': N308 '220': G709 '221': T604 '222': A46 '223': J019 '224': W74 '225': J329 '226': S141 '227': H409 '228': E876 '229': E211 '230': N481 '231': R630 '232': Q070 '233': I718(nTR) '234': T041 '235': O669 '236': H919 '237': T307 '238': M125 '239': K719 '240': I738 '241': S242 '242': V285 '243': T455 '244': I5150 '245': M319 '246': C947 '247': I868 '248': S223 '249': X599 '250': N251 '251': N419 '252': S910 '253': Q336 '254': J110 '255': B461 '256': E140 '257': C220 '258': N390 '259': T814 '260': F239 '261': N159 '262': Q559 '263': B250 '264': F051 '265': P251 '266': D486 '267': C770 '268': M798 '269': Q677 '270': D759 '271': N760 '272': C248 '273': C023 '274': A809 '275': S251 '276': I214 '277': B589 '278': K7210 '279': K628 '280': O85 '281': T145 '282': G048 '283': D762 '284': V195 '285': I671 '286': J150 '287': N946 '288': S327 '289': K669 '290': E833 '291': T829 '292': T597 '293': C260 '294': M620 '295': I270 '296': T07 '297': T826 '298': G258 '299': D164 '300': C968 '301': R51 '302': K630 '303': Q917 '304': K863 '305': F155 '306': P241 '307': I808 '308': N059 '309': T549 '310': S519 '311': S522 '312': A529 '313': C740 '314': G040 '315': R258 '316': C321 '317': G600 '318': O868 '319': C062 '320': F03 '321': N649 '322': K566 '323': M480 '324': T901 '325': E148 '326': T242 '327': V115 '328': N719 '329': A401 '330': S501 '331': M236 '332': T383 '333': P740 '334': L030 '335': C64 '336': D013 '337': R33 '338': Y26 '339': Q271 '340': I6149(nTR) '341': I254 '342': V465 '343': Y86 '344': I603(nTR) '345': Q030 '346': C172 '347': M703 '348': X36 '349': T250 '350': I310 '351': W32 '352': M659 '353': N86 '354': B008 '355': N835 '356': M130 '357': B909 '358': N322 '359': S701 '360': D24 '361': H335 '362': Q935 '363': S930 '364': T932 '365': B689 '366': Q233 '367': D166 '368': G231 '369': S819 '370': E859 '371': B789 '372': B977 '373': T733 '374': T132 '375': O909 '376': V499 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'1110': V913 '1111': C140 '1112': K36 '1113': K739 '1114': T675 '1115': A481 '1116': M600 '1117': C519 '1118': V775 '1119': T97 '1120': E209 '1121': T813 '1122': F842 '1123': K633 '1124': C780 '1125': D531 '1126': J219 '1127': V145 '1128': Y590 '1129': Q323 '1130': T202 '1131': V655 '1132': E871 '1133': I722(nTR) '1134': C390 '1135': P220 '1136': Q423 '1137': H308 '1138': I459 '1139': W33 '1140': L728 '1141': K800 '1142': D868 '1143': J157 '1144': Q262 '1145': C310 '1146': T028 '1147': T658 '1148': S688 '1149': T435 '1150': K639 '1151': L031 '1152': E744 '1153': F162 '1154': Q410 '1155': A89 '1156': Y079 '1157': S053(TR) '1158': J60 '1159': A749 '1160': C381 '1161': F432 '1162': R53 '1163': K560 '1164': A812 '1165': F419 '1166': C432 '1167': Q749 '1168': S028 '1169': X12 '1170': A199 '1171': S771 '1172': D397 '1173': H669 '1174': I701 '1175': W55 '1176': N12 '1177': I700 '1178': C081 '1179': B005 '1180': Q893 '1181': T253 '1182': M216 '1183': D728 '1184': K859 '1185': D445 '1186': Q059 '1187': P250 '1188': E874 '1189': L029 '1190': S531 '1191': Q230 '1192': C761 '1193': C472 '1194': D439 '1195': V756 '1196': C759 '1197': S272 '1198': G64 '1199': M311 '1200': D131 '1201': C261 '1202': C022 '1203': D731 '1204': S589 '1205': T827 '1206': Q212 '1207': G318 '1208': R098 '1209': K260 '1210': C33 '1211': N399 '1212': S063(TR) '1213': I710(nTR) '1214': D433 '1215': C496 '1216': A403 '1217': M089 '1218': T378 '1219': F259 '1220': Q179 '1221': Q631 '1222': J041 '1223': W36 '1224': K632 '1225': C787 '1226': Q348 '1227': Y842 '1228': S411 '1229': L26 '1230': C791 '1231': S421 '1232': T958 '1233': I772(nTR) '1234': I348 '1235': K30 '1236': Q438 '1237': K661(nTR) '1238': K255 '1239': T433 '1240': D381 '1241': E46 '1242': H819 '1243': K088 '1244': C01 '1245': C433 '1246': S179 '1247': I951 '1248': H702 '1249': I724(nTR) '1250': G618 '1251': V635 '1252': S510 '1253': S684 '1254': Q874 '1255': I209 '1256': N411 '1257': G245 '1258': P017 '1259': R471 '1260': Q248 '1261': T321 '1262': P070 '1263': W41 '1264': S978 '1265': N759 '1266': P159 '1267': M625 '1268': F410 '1269': E511 '1270': N211 '1271': D103 '1272': I078 '1273': Q220 '1274': D45 '1275': Q819 '1276': F105 '1277': M341 '1278': I139 '1279': R162 '1280': K828 '1281': C493 '1282': I6120(nTR) '1283': E055 '1284': C412 '1285': C920 '1286': M513 '1287': R390 '1288': T864 '1289': V134 '1290': T148 '1291': S728 '1292': G510 '1293': C403 '1294': E639 '1295': Q309 '1296': E728 '1297': C851 '1298': L039 '1299': Q909 '1300': G936(nTR) '1301': K292 '1302': E009 '1303': R638 '1304': Q202 '1305': S051(TR) '1306': T486 '1307': S451 '1308': I259 '1309': N189 '1310': T913 '1311': M898 '1312': C340 '1313': T887 '1314': N398 '1315': N971 '1316': F112 '1317': M340 '1318': C382 '1319': A178 '1320': T596 '1321': S159 '1322': H189 '1323': C880 '1324': W44 '1325': D484 '1326': C132 '1327': E781 '1328': K060 '1329': A491 '1330': D385 '1331': Y369 '1332': J80 '1333': Q860 '1334': M541 '1335': M321 '1336': M053 '1337': T600 '1338': R97 '1339': I880 '1340': K593 '1341': D099 '1342': M259 '1343': G911 '1344': A499 '1345': P789 '1346': E109 '1347': E220 '1348': I469 '1349': V873 '1350': V656 '1351': K382 '1352': L52 '1353': W70 '1354': N049 '1355': V385 '1356': B485 '1357': L899 '1358': V932 '1359': C20 '1360': P375 '1361': P288 '1362': S009 '1363': G404 '1364': C765 '1365': B699 '1366': L405 '1367': T96 '1368': Q897 '1369': E113 '1370': K740 '1371': H472 '1372': G522 '1373': I675 '1374': M419 '1375': Q675 '1376': C779 '1377': P77 '1378': E760 '1379': E059 '1380': R160 '1381': D595 '1382': K830 '1383': D809 '1384': D721 '1385': E279 '1386': I350 '1387': T520 '1388': L509 '1389': C728 '1390': F193 '1391': I351 '1392': M878 '1393': E761 '1394': F103 '1395': M45 '1396': C166 '1397': D696 '1398': D390 '1399': F208 '1400': N498 '1401': T116 '1402': Y01 '1403': G570 '1404': S134 '1405': M080 '1406': S022 '1407': E834 '1408': D181 '1409': Q239 '1410': I499 '1411': C005 '1412': K704 '1413': C343 '1414': T848 '1415': Q552 '1416': I692 '1417': S878 '1418': D758 '1419': D598 '1420': X04 '1421': T460 '1422': K612 '1423': M844 '1424': K928(nTR) '1425': V909 '1426': C796 '1427': V949 '1428': C700 '1429': L922 '1430': C349 '1431': Y33 '1432': S900 '1433': M310 '1434': C165 '1435': M313 '1436': K223 '1437': V154 '1438': P833 '1439': S019 '1440': N052 '1441': Q793 '1442': I498 '1443': G544 '1444': Q245 '1445': C37 '1446': Q411 '1447': S681 '1448': I510 '1449': Q386 '1450': B428 '1451': T282 '1452': G712 '1453': M512 '1454': Y20 '1455': J151 '1456': K419 '1457': W27 '1458': G08 '1459': T12 '1460': E878 '1461': I6329 '1462': T860 '1463': Q871 '1464': S630 '1465': O680 '1466': Q870 '1467': K282 '1468': E275 '1469': T602 '1470': D841 '1471': F302 '1472': S898 '1473': J339 '1474': Q433 '1475': R198 '1476': B353 '1477': Q792 '1478': I050 '1479': M948 '1480': T180 '1481': L010 '1482': T914 '1483': W24 '1484': T817 '1485': S014 '1486': N300 '1487': O723 '1488': T509 '1489': D569 '1490': J684 '1491': C750 '1492': G403 '1493': C312 '1494': Q782 '1495': R64 '1496': S199 '1497': I850 '1498': D380 '1499': D690 '1500': Q228 '1501': J985 '1502': V575 '1503': D610 '1504': S350 '1505': I712 '1506': S365(TR) '1507': C436 '1508': N412 '1509': W75 '1510': K113 '1511': W23 '1512': Y871 '1513': W92 '1514': E018 '1515': Q255 '1516': R402 '1517': G610 '1518': R71 '1519': R008 '1520': P702 '1521': T869 '1522': Q224 '1523': L021 '1524': I6139(nTR) '1525': D361 '1526': Q894 '1527': C473 '1528': C837 '1529': T287 '1530': K570 '1531': B359 '1532': D891 '1533': O410 '1534': C940 '1535': V061 '1536': M318 '1537': T603 '1538': G409 '1539': Q246 '1540': L102 '1541': A399 '1542': Q790 '1543': S269 '1544': S071 '1545': L589 '1546': N850 '1547': D100 '1548': C320 '1549': I864 '1550': Y355 '1551': C030 '1552': O001 '1553': T618 '1554': C031 '1555': G710 '1556': D109 '1557': S820 '1558': K761 '1559': Q000 '1560': W80 '1561': B487 '1562': Y05 '1563': B084 '1564': P838 '1565': P009 '1566': A420 '1567': G471 '1568': D142 '1569': A310 '1570': R068 '1571': R001 '1572': J159 '1573': T959 '1574': B259 '1575': I720(nTR) '1576': C430 '1577': C830 '1578': O721 '1579': J46 '1580': E854 '1581': T571 '1582': I098 '1583': N250 '1584': G541 '1585': T391 '1586': I6199(nTR) '1587': O711 '1588': T875 '1589': Q627 '1590': N40 '1591': I731 '1592': E788 '1593': Q223 '1594': E830 '1595': K922 '1596': C437 '1597': J180 '1598': R80 '1599': T481 '1600': K210 '1601': J690 '1602': D693 '1603': L080 '1604': J22 '1605': T537 '1606': M330 '1607': J982 '1608': C480 '1609': I716 '1610': I330 '1611': T828 '1612': S299 '1613': V856 '1614': P960 '1615': O141 '1616': T909 '1617': J660 '1618': K552 '1619': M082 '1620': W57 '1621': E780 '1622': E709 '1623': C110 '1624': J100 '1625': M479 '1626': E069 '1627': V125 '1628': I743 '1629': T564 '1630': V021 '1631': R190 '1632': I213 '1633': M779 '1634': E237 '1635': J852 '1636': G958 '1637': F989 '1638': Q210 '1639': D171 '1640': N10 '1641': T730 '1642': D820 '1643': D386 '1644': R000 '1645': G825(nTR) '1646': N280 '1647': E115 '1648': G039 '1649': I340(nRH) '1650': T824 '1651': M489 '1652': T511 '1653': G819 '1654': D150 '1655': P942 '1656': K269 '1657': A879 '1658': S250 '1659': C959 '1660': K289 '1661': Q652 '1662': S331 '1663': F102 '1664': T012 '1665': Q232 '1666': D352 '1667': C159 '1668': X18 '1669': B64 '1670': C914 '1671': C712 '1672': P90 '1673': D090 '1674': M611 '1675': N981 '1676': C222 '1677': I443 '1678': K626 '1679': S923 '1680': K810 '1681': M439 '1682': W31 '1683': H922 '1684': T285 '1685': F519 '1686': P359 '1687': B749 '1688': C721 '1689': B348 '1690': I458 '1691': H549 '1692': E851 '1693': D383 '1694': Q256 '1695': I251 '1696': I775 '1697': S602 '1698': S452 '1699': L020 '1700': I889 '1701': K709 '1702': H913 '1703': L744 '1704': V446 '1705': T462 '1706': C452 '1707': D392 '1708': Q828 '1709': L539 '1710': S225 '1711': A929 '1712': K528 '1713': S026 '1714': M889 '1715': K750 '1716': D550 '1717': S429 '1718': S354 '1719': K869 '1720': N019 '1721': S203 '1722': S280 '1723': J448 '1724': J941 '1725': T912 '1726': I879 '1727': C482 '1728': E249 '1729': T043 '1730': I809 '1731': D201 '1732': T091 '1733': T387 '1734': M509 '1735': S913 '1736': O439 '1737': K5500 '1738': Q614 '1739': F079 '1740': K521 '1741': H113(nTR) '1742': S025 '1743': Q219 '1744': R401 '1745': K564 '1746': K279 '1747': E049 '1748': H932 '1749': C130 '1750': C089 '1751': T540 '1752': R771 '1753': F151 '1754': B448 '1755': N485 '1756': I971 '1757': F319 '1758': C795 '1759': K743 '1760': C838 '1761': K805 '1762': E15 '1763': W269 '1764': R103 '1765': R829 '1766': F603 '1767': A181 '1768': M210 '1769': T751 '1770': K404 '1771': B440 '1772': S142 '1773': C131 '1774': D738 '1775': C049 '1776': C154 '1777': L028 '1778': K261 '1779': X76 '1780': K040 '1781': M350 '1782': H813 '1783': J156 '1784': I517 '1785': V903 '1786': I060(RH) '1787': F202 '1788': T449 '1789': Q674 '1790': G838(nTR) '1791': T172 '1792': P080 '1793': C762 '1794': N762 '1795': C322 '1796': S021 '1797': V685 '1798': M179 '1799': S628 '1800': N328 '1801': J348 '1802': T243 '1803': F709 '1804': L448 '1805': S219 '1806': O979 '1807': I120 '1808': G542(nTR) '1809': F459 '1810': T905 '1811': O40 '1812': N23 '1813': T029 '1814': C474 '1815': H041 '1816': Q459 '1817': M879 '1818': K832(nTR) '1819': T305 '1820': J013 '1821': I604(nTR) '1822': B181 '1823': Q913 '1824': S314 '1825': M109 '1826': I674 '1827': T798 '1828': C103 '1829': Q318 '1830': G439 '1831': M485 '1832': C185 '1833': C342 '1834': Q031 '1835': C061 '1836': C964 '1837': S024 '1838': T503 '1839': O881 '1840': S363(TR) '1841': T147 '1842': A410 '1843': C440 '1844': N368 '1845': F341 '1846': M460 '1847': S355 '1848': D367 '1849': N888 '1850': S623 '1851': B441 '1852': X13 '1853': W28 '1854': K318(nTR) '1855': G908 '1856': G935 '1857': N811 '1858': D898 '1859': D735 '1860': D000 '1861': G500 '1862': S934 '1863': F609 '1864': Q791 '1865': M622 '1866': V274 '1867': E274(nTR) '1868': G210 '1869': Q431 '1870': G939(nTR) '1871': N201 '1872': G609 '1873': D444 '1874': C414 '1875': D134 '1876': D890 '1877': C922 '1878': S410 '1879': K829 '1880': C221 '1881': I481 '1882': K839 '1883': A064 '1884': E222 '1885': E54 '1886': D377 '1887': N135 '1888': V174 '1889': K823 '1890': Q392 '1891': O469 '1892': K909 '1893': J152 '1894': F29 '1895': Q250 '1896': R100 '1897': V475 '1898': V929 '1899': I349(nRH) '1900': E147 '1901': S920 '1902': S129 '1903': N482 '1904': D467 '1905': Y899 '1906': J90 '1907': M316 '1908': K108 '1909': J205 '1910': E86 '1911': V755 '1912': L988 '1913': C19 '1914': C760 '1915': E849 '1916': Q159 '1917': K284 '1918': B027 '1919': N813 '1920': T630 '1921': J410 '1922': Q669 '1923': Q068 '1924': E875 '1925': O692 '1926': W34 '1927': S020 '1928': M952 '1929': P701 '1930': I4290 '1931': F719 '1932': K592 '1933': Q621 '1934': C530 '1935': I050(RH) '1936': K132 '1937': G473 '1938': T840 '1939': H160 '1940': H810 '1941': J392 '1942': K588 '1943': S224 '1944': I4289 '1945': N459 '1946': L309 '1947': I729(nTR) '1948': I280 '1949': T320 '1950': S313 '1951': Q231 '1952': G959(nTR) '1953': N200 '1954': A483 '1955': T099 '1956': C450 '1957': R945 '1958': C187 '1959': S799 '1960': Q447 '1961': V694 '1962': J383 '1963': N19 '1964': T111 '1965': A99 '1966': G120 '1967': N61 '1968': C254 '1969': N903 '1970': T568 '1971': J340 '1972': T181 '1973': S520 '1974': I829 '1975': X82 '1976': V676 '1977': K148 '1978': S090 '1979': T361 '1980': D170 '1981': M256 '1982': C637 '1983': K388 '1984': C58 '1985': P073 '1986': A409 '1987': R478 '1988': T749 '1989': V051 '1990': E050 '1991': M998 '1992': R824 '1993': D802 '1994': J391 '1995': F489 '1996': C709 '1997': J449 '1998': V234 '1999': C542 '2000': S221 '2001': Q969 '2002': M866 '2003': C749 '2004': S064(TR) '2005': Q872 '2006': V799 '2007': C931 '2008': R457 '2009': G008 '2010': S150 '2011': O364 '2012': C060 '2013': V870 '2014': D899 '2015': X84 '2016': V124 '2017': R074 '2018': D190 '2019': I839 '2020': D168 '2021': D694 '2022': M246 '2023': K276 '2024': D420 '2025': A480 '2026': C240 '2027': I6109(nTR) '2028': W12 '2029': A753 '2030': G938 '2031': C451 '2032': S911 '2033': D692 '2034': O624 '2035': K590 '2036': N137 '2037': R300 '2038': G219 '2039': K3190 '2040': S325 '2041': R560 '2042': E872 '2043': W10 '2044': J980 '2045': M331 '2046': F179 '2047': E569 '2048': S359 '2049': J351 '2050': R208 '2051': J188 '2052': T175 '2053': C680 '2054': S323 '2055': H342 '2056': K520 '2057': J040 '2058': I079(RH) '2059': T409 '2060': R011 '2061': K765 '2062': S246 '2063': M802 '2064': R529 '2065': V476 '2066': Q158 '2067': P523 '2068': K469 '2069': C794 '2070': I5149 '2071': S559 '2072': X81 '2073': A430 '2074': B399 '2075': F411 '2076': H959 '2077': E329 '2078': P269 '2079': G001 '2080': P704 '2081': S270 '2082': J121 '2083': C845 '2084': J841 '2085': V839 '2086': N179 '2087': N730 '2088': B49 '2089': A329 '2090': C249 '2091': I5009 '2092': D300 '2093': N26 '2094': C402 '2095': D046 '2096': K629 '2097': B003 '2098': S770 '2099': I070 '2100': C009 '2101': C039 '2102': I698 '2103': E725 '2104': T599 '2105': T423 '2106': T189 '2107': C300 '2108': S220 '2109': A162 '2110': K439 '2111': S274 '2112': A020 '2113': R030 '2114': I208 '2115': N832 '2116': B376 '2117': I210 '2118': Q048 '2119': D002 '2120': G038 '2121': N119 '2122': R943 '2123': Q2780 '2124': D588 '2125': B069 '2126': K611 '2127': V579 '2128': L024 '2129': L023 '2130': R14 '2131': C148 '2132': T139 '2133': C23 '2134': I602(nTR) '2135': G809 '2136': K911 '2137': P618 '2138': K293 '2139': S003 '2140': T781 '2141': N302 '2142': X590 '2143': I713(nTR) '2144': M359 '2145': K265 '2146': S322 '2147': K861 '2148': I702 '2149': T842 '2150': L108 '2151': A379 '2152': J158 '2153': I788 '2154': W73 '2155': K833 '2156': N828 '2157': F322 '2158': M402 '2159': X00 '2160': F480 '2161': I871(nTR) '2162': D012 '2163': J949 '2164': B369 '2165': E763 '2166': V244 '2167': N133 '2168': S065(TR) '2169': X72 '2170': R688 '2171': K729 '2172': K658 '2173': L919 '2174': X349 '2175': N450 '2176': C725 '2177': M758 '2178': I518 '2179': P591 '2180': W13 '2181': T629 '2182': M100 '2183': T795 '2184': N831 '2185': Q229 '2186': I742 '2187': M103 '2188': B340 '2189': V011 '2190': C831 '2191': S724 '2192': T844 '2193': D443 '2194': Q759 '2195': S659 '2196': C673 '2197': D869 '2198': T300 '2199': D751 '2200': Q989 '2201': I516 '2202': P072 '2203': C253 '2204': W49 '2205': X70 '2206': S836 '2207': T136 '2208': D329 '2209': V695 '2210': A490 '2211': S259 '2212': V175 '2213': W67 '2214': Q601 '2215': W06 '2216': I721(nTR) '2217': M245 '2218': J680 '2219': G960 '2220': J209 '2221': R161 '2222': S619 '2223': T009 '2224': Q934 '2225': A1699 '2226': D373 '2227': I409 '2228': E832 '2229': A188 '2230': M464 '2231': T436 '2232': S273 '2233': K227 '2234': V379 '2235': T094 '2236': J010 '2237': C051 '2238': C12 '2239': E230 '2240': K862 '2241': B279 '2242': R739 '2243': O009 '2244': D047 '2245': I4299 '2246': Q046 '2247': T313 '2248': M538 '2249': T314 '2250': T535 '2251': K509 '2252': D351 '2253': O429 '2254': M1997 '2255': G903 '2256': E888 '2257': K389 '2258': M899 '2259': C819 '2260': M009 '2261': D144 '2262': A419 '2263': L530 '2264': M301 '2265': C311 '2266': R02 '2267': K913 '2268': S390 '2269': L299 '2270': D369 '2271': A34 '2272': B001 '2273': K802 '2274': K316 '2275': V846 '2276': Q933 '2277': E119 '2278': P351 '2279': K701 '2280': R222 '2281': E042 '2282': I776 '2283': Q647 '2284': G910 '2285': T845 '2286': B608 '2287': V689 '2288': P005 '2289': G062 '2290': K222(nTR) '2291': F402 '2292': B459 '2293': O660 '2294': P749 '2295': K051 '2296': K702 '2297': T920 '2298': Q319 '2299': Q663 '2300': J386 '2301': K358 '2302': B023 '2303': Q643 '2304': J950 '2305': D413 '2306': Q201 '2307': F429 '2308': P285 '2309': D267 '2310': Q320 '2311': A180 '2312': S420 '2313': H110 '2314': A549 '2315': J311 '2316': A099 '2317': K140 '2318': J988 '2319': B169 '2320': K912 '2321': F205 '2322': N648 '2323': K250 '2324': R090 '2325': K604 '2326': S810 '2327': Q061 '2328': Q040 '2329': C675 '2330': K763 '2331': T862 '2332': B020 '2333': Q899 '2334': I482 '2335': D233 '2336': C445 '2337': K266 '2338': D137 '2339': E789 '2340': H050 '2341': C411 '2342': S525 '2343': R440 '2344': E701 '2345': H603 '2346': I451 '2347': E320 '2348': F600 '2349': H471 '2350': M623 '2351': R629 '2352': X11 '2353': I5159 '2354': R601 '2355': T841 '2356': E310 '2357': Q399 '2358': I6409 '2359': S128 '2360': D580 '2361': Q451 '2362': C069 '2363': I709 '2364': D649 '2365': J310 '2366': A028 '2367': M861 '2368': A439 '2369': V425 '2370': D331 '2371': M023 '2372': R101 '2373': B09 '2374': K314 '2375': C091 '2376': C470 '2377': C459 '2378': Q858 '2379': E889 '2380': D684 '2381': B24 '2382': B902 '2383': S332 '2384': T329 '2385': A829 '2386': X09 '2387': C840 '2388': N258 '2389': Q419 '2390': N859 '2391': M063 '2392': N259 '2393': O440 '2394': I690 '2395': V877 '2396': T130 '2397': C786 '2398': S932 '2399': V649 '2400': M929 '2401': C639 '2402': D819 '2403': C383 '2404': Q758 '2405': K551 '2406': M300 '2407': G369 '2408': T328 '2409': H356(nTR) '2410': S369(TR) '2411': Q382 '2412': D763 '2413': I6119(nTR) '2414': V355 '2415': V872 '2416': K271 '2417': J942(nTR) '2418': I621(nTR) '2419': C227 '2420': T922 '2421': J850 '2422': C161 '2423': T021 '2424': J459 '2425': C169 '2426': J129 '2427': C570 '2428': T390 '2429': S47 '2430': C494 '2431': K559 '2432': I339 '2433': I2199 '2434': P025 '2435': P968 '2436': T703 '2437': F845 '2438': L088 '2439': K561 '2440': J634 '2441': H921 '2442': Q605 '2443': Q412 '2444': H000 '2445': V139 '2446': Q740 '2447': L401 '2448': F199 '2449': L110 '2450': T874 '2451': I301 '2452': O960 '2453': A810 '2454': O753 '2455': D479 '2456': P832 '2457': S060(TR) '2458': Q620 '2459': T401 '2460': P949 '2461': T820 '2462': Q892 '2463': E268 '2464': J381 '2465': T799 '2466': G319 '2467': N329 '2468': G723 '2469': S308 '2470': C600 '2471': E271 '2472': I679 '2473': H933 '2474': W17 '2475': D165 '2476': K638 '2477': V585 '2478': T315 '2479': J370 '2480': M213 '2481': K921 '2482': C924 '2483': S801 '2484': Q796 '2485': B009 '2486': V481 '2487': K851 '2488': D379 '2489': T510 '2490': C449 '2491': S351 '2492': C447 '2493': C699 '2494': H540 '2495': E065 '2496': A182 '2497': Q999 '2498': J069 '2499': G830(nTR) '2500': S260 '2501': V456 '2502': I069(RH) '2503': K130 '2504': V099 '2505': K263 '2506': M138 '2507': T938 '2508': R091 '2509': N342 '2510': C313 '2511': T042 '2512': M548 '2513': D591 '2514': Q349 '2515': B900 '2516': E713 '2517': P023 '2518': T929 '2519': C162 '2520': K625(nTR) '2521': D126 '2522': M463 '2523': G442 '2524': G528 '2525': P521 '2526': S301 '2527': T220 '2528': C798 '2529': C690 '2530': C492 '2531': B028 '2532': C435 '2533': C927 '2534': K904 '2535': M1995 '2536': V736 '2537': Q439 '2538': Q252 '2539': C722 '2540': C630 '2541': H498 '2542': K529 '2543': D400 '2544': A870 '2545': N939(nTR) '2546': K565 '2547': I711(nTR) '2548': Y069 '2549': V859 '2550': S120 '2551': C323 '2552': S151 '2553': J039 '2554': V776 '2555': A062 '2556': O069 '2557': G519 '2558': R634 '2559': J051 '2560': J398 '2561': E142 '2562': T025 '2563': Q680 '2564': I495 '2565': A049 '2566': D412 '2567': I715(nTR) '2568': I091 '2569': I490 '2570': B03 '2571': I289 '2572': C841 '2573': I678 '2574': A799 '2575': R999 '2576': E278 '2577': M242 '2578': I6359 '2579': H440 '2580': Q390 '2581': L570 '2582': N818 '2583': C950 '2584': B269 '2585': F059 '2586': O759 '2587': S059 '2588': S015 '2589': I494 '2590': Q606 '2591': T212 '2592': R21 '2593': C250 '2594': C490 '2595': L511 '2596': P219 '2597': D66 '2598': H700 '2599': G950 '2600': C574 '2601': I601(nTR) '2602': G560 '2603': T821 '2604': B000 '2605': T289 '2606': C188 '2607': P059 '2608': B451 '2609': R730 '2610': K029 '2611': Y34 '2612': D022 '2613': K621 '2614': C257 '2615': I212 '2616': I672 '2617': M332 '2618': Q264 '2619': N360 '2620': G441 '2621': T569 '2622': C942 '2623': P350 '2624': C930 '2625': S430 '2626': S018 '2627': V090 '2628': I723(nTR) '2629': C384 '2630': C504 '2631': T659 '2632': Q754 '2633': D269 '2634': M629 '2635': Q189 '2636': S899 '2637': T096 '2638': V871 '2639': G09 '2640': S321 '2641': B029 '2642': F209 '2643': K294 '2644': G003 '2645': S500 '2646': M462 '2647': K732 '2648': F329 '2649': N421(nTR) '2650': F729 '2651': I371 '2652': Q208 '2653': S831 '2654': T447 '2655': T065 '2656': S229 '2657': P969 '2658': T10 '2659': K822(nTR) '2660': H605 '2661': V485 '2662': T638 '2663': J958 '2664': M532 '2665': J698 '2666': J869 '2667': C119 '2668': K811 '2669': C696 '2670': I4229 '2671': X38 '2672': M329 '2673': T918 '2674': G961 '2675': C966 '2676': J691 '2677': G372 '2678': S290 '2679': V295 '2680': J853 '2681': F068 '2682': Q206 '2683': E835 '2684': S499 '2685': V255 '2686': N340 '2687': I801 '2688': T560 '2689': C150 '2690': T310 '2691': J312 '2692': T928 '2693': J385 '2694': D434 '2695': X37 '2696': S929 '2697': J168 '2698': S669 '2699': E149 '2700': C859 '2701': S399 '2702': C229 '2703': V794 '2704': I378 '2705': T144 '2706': I461 '2707': E762 '2708': C080 '2709': A972 '2710': M303 '2711': P071 '2712': Q039 '2713': M478 '2714': C969 '2715': S18 '2716': Q851 '2717': Q273 '2718': T882 '2719': I501 '2720': X01 '2721': B178 '2722': N821 '2723': Q809 '2724': T528 '2725': K122 '2726': N178 '2727': I092 '2728': K769 '2729': P809 '2730': P290 '2731': V535 '2732': R579 '2733': Q891 '2734': E259 '2735': A979 '2736': S925 '2737': I513 '2738': X14 '2739': Q393 '2740': I6319 '2741': C693 '2742': D477 '2743': F431 '2744': T178 '2745': T816 '2746': I258 '2747': C501 '2748': D414 '2749': A630 '2750': W05 '2751': I012 '2752': E243 '2753': T921 '2754': G912 '2755': T598 '2756': C715 '2757': H729(nTR) '2758': P614 '2759': G259 '2760': T223 '2761': T173 '2762': P284 '2763': N312 '2764': E232 '2765': I4259 '2766': C186 '2767': J029 '2768': Q738 '2769': A830 '2770': A520 '2771': T311 '2772': W18 '2773': N138 '2774': T271 '2775': Y850 '2776': D27 '2777': V970 '2778': C099 '2779': M895 '2780': D151 '2781': I509 '2782': I749(TH) '2783': N158 '2784': T301 '2785': C170 '2786': Q043 '2787': G800 '2788': Q610 '2789': G419 '2790': K563 '2791': A850 '2792': L300 '2793': S823 '2794': K290 '2795': A229 '2796': C783 '2797': D500 '2798': M792 '2799': C724 '2800': C579 '2801': T414 '2802': I071 '2803': D069 '2804': K20 '2805': Q441 '2806': C410 '2807': H838(nTR) '2808': D132 '2809': F161 '2810': S670 '2811': H341 '2812': T408 '2813': M818 '2814': B354 '2815': I241 '2816': N433 '2817': R064 '2818': T670 '2819': Q253 '2820': E870 '2821': G629 '2822': N852 '2823': K068 '2824': J61 '2825': C784 '2826': T679 '2827': M939 '2828': M436 '2829': F070 '2830': T825 '2831': C182 '2832': B07 '2833': R578 '2834': P599 '2835': I308 '2836': C184 '2837': N63 '2838': E233 '2839': I6090 '2840': P279 '2841': R418 '2842': N139 '2843': D469 '2844': M819 '2845': V135 '2846': D599 '2847': C446 '2848': S252 '2849': Q799 '2850': F444 '2851': S579 '2852': K431 '2853': K381 '2854': U071 '2855': S529 '2856': N170 '2857': E619 '2858': T784 '2859': B019 '2860': C55 '2861': I099 '2862': O713 '2863': H912 '2864': E242 '2865': Q612 '2866': T355 '2867': V224 '2868': C002 '2869': C495 '2870': D320 '2871': L270 '2872': T134 '2873': A521 '2874': A059 '2875': S822 '2876': K047 '2877': C741 '2878': C111 '2879': R104 '2880': K149 '2881': T412 '2882': K659 '2883': L271 '2884': V185 '2885': B374 '2886': B172 '2887': I201 '2888': K768(nTR) '2889': K264 '2890': N324(nTR) '2891': Q820 '2892': E063 '2893': F500 '2894': C829 '2895': K277 '2896': L984 '2897': T404 '2898': A318 '2899': W19 '2900': T849 '2901': K558 '2902': L00 '2903': R298 '2904': H709 '2905': C505 '2906': S361(TR) '2907': J108 '2908': W56 '2909': K562 '2910': I803 '2911': K440(nTR) '2912': N808 '2913': S320 '2914': V335 '2915': K229 '2916': G92 '2917': K413 '2918': N829 '2919': S298 '2920': W08 '2921': X51 '2922': K700 '2923': P289 '2924': E040 '2925': T609 '2926': J930 '2927': W29 '2928': L219 '2929': F488 '2930': G244 '2931': D643 '2932': E749 '2933': G060 '2934': F799 '2935': K221 '2936': Q203 '2937': C155 '2938': V155 '2939': S372(TR) '2940': T465 '2941': T543 '2942': I079 '2943': C510 '2944': D733 '2945': P293 '2946': P524 '2947': K599 '2948': E742 '2949': K283 '2950': K270 '2951': M250 '2952': E229 '2953': T303 '2954': C269 '2955': T319 '2956': P612 '2957': B377 '2958': S310 '2959': A35 '2960': C020 '2961': Q339 '2962': K112 '2963': S344 '2964': F959 '2965': X19 '2966': I6349 '2967': V899 '2968': W260 '2969': B189 '2970': S318 '2971': A022 '2972': H109 '2973': D001 '2974': F39 '2975': S870 '2976': V784 '2977': R609 '2978': I6159(nTR) '2979': X91 '2980': A493 '2981': T508 '2982': K760 '2983': P281 '2984': Q398 '2985': S852 '2986': A030 '2987': M621(nTR) '2988': K519 '2989': T141 '2990': O343 '2991': Q2820 '2992': V826 '2993': T092 '2994': L538 '2995': I340 '2996': L893 '2997': T322 '2998': T857 '2999': D333 '3000': T981 '3001': A010 '3002': R508 '3003': F449 '3004': B370 '3005': L022 '3006': K649 '3007': T286 '3008': Y09 '3009': C717 '3010': Y04 '3011': T203 '3012': V813 '3013': A184 '3014': D169 '3015': I456 '3016': I6000 '3017': E754 '3018': M729 '3019': G000 '3020': F430 '3021': R520 '3022': V829 '3023': I6399 '3024': R81 '3025': E201 '3026': S999 '3027': C211 '3028': R568 '3029': T504 '3030': C301 '3031': I351(nRH) '3032': C601 '3033': K591 '3034': C775 '3035': D303 '3036': V745 '3037': L940 '3038': T277 '3039': A060 '3040': D689 '3041': T230 '3042': K914 '3043': T233 '3044': M432 '3045': Q330 '3046': H830 '3047': G042 '3048': I447 '3049': B582 '3050': D175 '3051': E031 '3052': T049 '3053': I279 '3054': A168 '3055': G030 '3056': Q929 '3057': I828 '3058': P291 '3059': S824 '3060': D409 '3061': T522 '3062': E880 '3063': I802 '3064': J380 '3065': G952 '3066': J441 '3067': N47 '3068': V950 '3069': R820 '3070': I309 '3071': M511 '3072': D110 '3073': T403 '3074': D480 '3075': F101 '3076': I789 '3077': Q369 '3078': E250 '3079': J118 '3080': V849 '3081': I739 '3082': B022 '3083': E079 '3084': C434 '3085': Q998 '3086': A429 '3087': K624 '3088': C001 '3089': D589 '3090': C004 '3091': E168 '3092': R220 '3093': W83 '3094': E711 '3095': T699 '3096': S278 '3097': P370 '3098': O16 '3099': J189 '3100': J40 '3101': D180 '3102': K135 '3103': L512 '3104': H539 '3105': S828 '3106': G549 '3107': E041 '3108': T024 '3109': S131 '3110': C711 '3111': C059 '3112': H470 '3113': F840 '3114': F107 '3115': V837 '3116': G700 '3117': R591 '3118': Q798 '3119': M434 '3120': B465 '3121': C479 '3122': T306 '3123': E850 '3124': L982 '3125': S202 '3126': E272 '3127': N739 '3128': F111 '3129': D419 '3130': E145 '3131': B659 '3132': H46 '3133': I256 '3134': F328 '3135': T402 '3136': C714 '3137': C66 '3138': Q849 '3139': S907 '3140': J182 '3141': T58 '3142': O152 '3143': O882 '3144': S011 '3145': T702 '3146': I491 '3147': R571 '3148': N304 '3149': M751 '3150': I313 '3151': T325 '3152': S366(TR) '3153': E806 '3154': T55 '3155': H269 '3156': S730 '3157': V430 '3158': M791 '3159': T179 '3160': B54 '3161': Q263 '3162': V092 '3163': F412 '3164': K571 '3165': V093 '3166': K660 '3167': E877 '3168': V489 '3169': C900 '3170': R959 '3171': K852 '3172': S382 '3173': G909 '3174': G35 '3175': G951 '3176': S212 '3177': I061(RH) '3178': K745 '3179': I81 '3180': R570 '3181': C901 '3182': Q859 '3183': B378 '3184': J154 '3185': Q268 '3186': N490 '3187': C151 '3188': M169 '3189': J986 '3190': G968 '3191': K275 '3192': S333 '3193': Q221 '3194': I4220 '3195': I821 '3196': C710 '3197': D291 '3198': M069 '3199': S302 '3200': Q204 '3201': I6360 '3202': P592 '3203': Q742 '3204': D229 '3205': V855 '3206': L850 '3207': C609 '3208': D129 '3209': M869 '3210': T318 '3211': I859 '3212': I129 '3213': V545 '3214': A080 '3215': E269 '3216': K7200 '3217': R5800 '3218': I779 '3219': K259 '3220': D049 '3221': S373(TR) '3222': S970 '3223': J155 '3224': L089 '3225': S198 '3226': I509(A) '3227': K838 '3228': I269 '3229': J64 '3230': P040 '3231': V104 '3232': W66 '3233': W01 '3234': A048 '3235': A64 '3236': G002 '3237': G543 '3238': T939 '3239': I369(nRH) '3240': W35 '3241': P360 '3242': T200 '3243': P522 '3244': V675 '3245': Q442 '3246': K285 '3247': R400 '3248': E141 '3249': J931 '3250': I442 '3251': I890 '3252': I749 '3253': D371 '3254': S523 '3255': E052 '3256': M219 '3257': V679 '3258': V811 '3259': J14 '3260': D440 '3261': B159 '3262': B349 '3263': I119 '3264': G934 '3265': R170 '3266': H100 '3267': T280 '3268': Q890 '3269': T369 '3270': D259 '3271': D483 '3272': O269 '3273': S370(TR) '3274': E348 '3275': Q383 '3276': A309 '3277': D015 '3278': I070(RH) '3279': J439(nTR) '3280': K352 '3281': S400 '3282': T793 '3283': I6080 '3284': V041 '3285': N509 '3286': C471 '3287': I511 '3288': T056 '3289': D812 '3290': H664 '3291': I493 '3292': C491 '3293': I99 '3294': I441 '3295': C782 '3296': I059(RH) '3297': N009 '3298': A759 '3299': C692 '3300': H602 '3301': C469 '3302': M619 '3303': G111 '3304': C919 '3305': C179 '3306': C439 '3307': G589 '3308': W40 '3309': J960 '3310': V479 '3311': T494 '3312': I00 '3313': M469 '3314': K311 '3315': L239 '3316': C939 '3317': T140 '3318': O149 '3319': D34 '3320': S058 '3321': T68 '3322': V194 '3323': Q2829 '3324': T461 '3325': R040 '3326': F172 '3327': Q678 '3328': T622 '3329': O209 '3330': K297 '3331': J700 '3332': D482 '3333': G049 '3334': V821 '3335': B460 '3336': A167 '3337': E559 '3338': L910 '3339': W268 '3340': I881 '3341': J00 '3342': V594 '3343': P008 '3344': G121 '3345': S004(TR) '3346': S418 '3347': L500 '3348': J42 '3349': B488 '3350': I714 '3351': Q161 '3352': K429 '3353': O239 '3354': M542 '3355': R251 '3356': O864 '3357': N289 '3358': G10 '3359': C052 '3360': I370 '3361': K296 '3362': O368 '3363': G379 '3364': S078 '3365': A412 '3366': K623 '3367': C400 '3368': V254 '3369': E838 '3370': R960 '3371': Q750 '3372': D848 '3373': T112 '3374': Q321 '3375': C911 '3376': C764 '3377': Q688 '3378': S068(TR) '3379': C835 '3380': R590 '3381': D65 '3382': M545 '3383': D761 '3384': J961 '3385': F09 '3386': K267 '3387': P282 '3388': C771 '3389': Q359 '3390': K403 '3391': H353 '3392': S729 '3393': K310 '3394': V841 '3395': F848 '3396': M519 '3397': P270 '3398': Q172 '3399': O244 '3400': G114 '3401': I6189(nTR) '3402': T427 '3403': W39 '3404': J120 '3405': V214 '3406': R31 '3407': R55 '3408': H600 '3409': B182 '3410': H609 '3411': T650 '3412': P021 '3413': D821 '3414': A86 '3415': D059 '3416': D70 '3417': I4209 '3418': J451 '3419': K759 '3420': G579 '3421': D688 '3422': K220 '3423': V486 '3424': S062(TR) '3425': D720 '3426': Q803 '3427': B059 '3428': E752 '3429': D510 '3430': D375 '3431': T563 '3432': C467 '3433': C443 '3434': A039 '3435': J82 '3436': T910 '3437': C113 '3438': C164 '3439': N814 '3440': C006 '3441': K298 '3442': Q249 '3443': R230 '3444': R270 '3445': G061 '3446': N281 '3447': C772 '3448': K295 '3449': T324 '3450': T915 '3451': D430 '3452': V686 '3453': D407 '3454': C915 '3455': G311 '3456': D374 '3457': A699 '3458': G458 '3459': S127 '3460': V584 '3461': W54 '3462': K668 '3463': D330 '3464': K461 '3465': I090 '3466': G931 '3467': T240 '3468': T283 '3469': K254 '3470': D682 '3471': V98 '3472': Q699 '3473': D020 '3474': K219 '3475': V823 '3476': K053 '3477': D353 '3478': K819 '3479': Q432 '3480': D152 '3481': G009 '3482': N429 '3483': M840 '3484': T810 '3485': D619 '3486': D804 '3487': N320 '3488': T090 '3489': E215 '3490': D128 '3491': J304 '3492': T931 '3493': N058 '3494': D472 '3495': L88 '3496': J060 '3497': M488 '3498': W02 '3499': C000 '3500': N889 '3501': G406 '3502': D487 '3503': G119 '3504': C549 '3505': E0399 '3506': Q772 '3507': S001 '3508': I319 '3509': Q079 '3510': V149 '3511': O418 '3512': M790 '3513': T876 '3514': S631 '3515': C788 '3516': V840 '3517': I708 '3518': K634 '3519': D411 '3520': E710 '3521': A1690 '3522': T590 '3523': N210 '3524': T304 '3525': G450 '3526': J36 '3527': K052 '3528': M719 '3529': V576 '3530': B832 '3531': O994 '3532': D560 '3533': B341 '3534': P399 '3535': D399 '3536': E831 '3537': G312 '3538': K137 '3539': V299 '3540': S626 '3541': S029 '3542': V779 '3543': Q979 '3544': B661 '3545': H578(nTR) '3546': N709 '3547': D370 '3548': E723 '3549': M868 '3550': B220 '3551': Q188 '3552': T521 '3553': C500 '3554': E881 '3555': S371(TR) '3556': R234 '3557': A38 '3558': K622 '3559': K929 '3560': S903 '3561': M352 '3562': S066(TR) '3563': I358 '3564': D334 '3565': C475 '3566': C07 '3567': V786 splits: - name: train num_bytes: 113287237 num_examples: 1477240 download_size: 28018862 dataset_size: 113287237 configs: - config_name: default data_files: - split: train path: data/train-* --- # Chinese International Statistical Classification of Diseases

[ 0.5462018847465515, 0.06692840158939362, 0.016494937241077423, 0.9068963527679443, -0.3460519313812256, -0.24827896058559418, 0.2844724655151367, -0.28805431723594666, 0.26981407403945923, 0.4322073757648468, 0.019461117684841156, -0.8071548938751221, -0.38881102204322815, -0.0471387691795...

null

Royal-lobster/Enhanced-Slither-Audited-Solidity-QA

Royal-lobster

2023-11-14T09:36:35Z

21

0

null

[ "region:us" ]

2023-11-14T09:36:35Z

2023-11-14T09:36:20.000Z

2023-11-14T09:36:20

--- dataset_info: features: - name: results dtype: string - name: source_code dtype: string - name: question dtype: string - name: answer dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 275448756 num_examples: 9477 download_size: 81424292 dataset_size: 275448756 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Enhanced-Slither-Audited-Solidity-QA" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.42022788524627686, -0.33392050862312317, 0.38023626804351807, -0.12121830135583878, -0.5623644590377808, 0.3164162039756775, 0.5748488306999207, -0.24724993109703064, 1.0150623321533203, 0.7853459715843201, -0.5810708999633789, -0.4877794086933136, -0.2291872799396515, -0.28382119536399...

null

Subcold/common_voice_small.en

Subcold

2023-11-14T12:43:44Z

21

0

null

[ "region:us" ]

2023-11-14T12:43:44Z

2023-11-14T10:50:02.000Z

2023-11-14T10:50:02

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 18247456336 num_examples: 18999 - name: test num_bytes: 3796591936 num_examples: 3953 download_size: 3870258484 dataset_size: 22044048272 --- # Dataset Card for "common_voice_small.en" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.7570565938949585, -0.21919327974319458, 0.18814915418624878, 0.22714000940322876, -0.2661123275756836, -0.2015388458967209, -0.25473350286483765, -0.19715893268585205, 0.9936438798904419, 0.4573577642440796, -0.9053822755813599, -0.749198317527771, -0.4703584313392639, -0.14047649502754...

null

jlbaker361/addition_whole

jlbaker361

2023-11-15T13:00:03Z

21

0

null

[ "region:us" ]

2023-11-15T13:00:03Z

2023-11-14T23:39:10.000Z

2023-11-14T23:39:10

--- dataset_info: features: - name: input dtype: string - name: output dtype: float64 - name: text dtype: string splits: - name: train num_bytes: 1210405.5 num_examples: 29376 - name: test num_bytes: 134489.5 num_examples: 3264 download_size: 730504 dataset_size: 1344895.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "addition_whole" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.6283860802650452, -0.36209484934806824, 0.27053409814834595, 0.43537089228630066, -0.39667367935180664, -0.20659609138965607, 0.24168400466442108, -0.24319447576999664, 1.1123929023742676, 0.6366287469863892, -0.7918803095817566, -0.6536015272140503, -0.6168405413627625, -0.368312269449...

null

jlbaker361/addition_decimal

jlbaker361

2023-11-15T13:00:20Z

21

0

null

[ "region:us" ]

2023-11-15T13:00:20Z

2023-11-14T23:39:16.000Z

2023-11-14T23:39:16

--- dataset_info: features: - name: input dtype: string - name: output dtype: float64 - name: text dtype: string splits: - name: train num_bytes: 2145709.8 num_examples: 29376 - name: test num_bytes: 238412.2 num_examples: 3264 download_size: 884683 dataset_size: 2384122.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "addition_decimal" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.6959035396575928, -0.27149972319602966, 0.10352663695812225, 0.4823888838291168, -0.18485212326049805, -0.24996797740459442, 0.07036090642213821, -0.1856643408536911, 0.8363009691238403, 0.41639330983161926, -0.604400634765625, -0.694763720035553, -0.6129626035690308, -0.273878365755081...

null

maf917/sqltestdata

maf917

2023-11-15T02:26:12Z

21

0

null

[ "region:us" ]

2023-11-15T02:26:12Z

2023-11-15T02:24:30.000Z

2023-11-15T02:24:30

Write 20 SQL programming questions of easy and medium complexity. Provide questions on a diverse range of subjects, and make sure no two questions are alike. Make sure the the SQL syntax consists of select statements and follows ANSI rules. No commercial use

[ -0.3400801122188568, -0.7974334955215454, 0.5027034878730774, 0.7927043437957764, -0.02088814787566662, -0.13264885544776917, -0.020007336512207985, -0.21262572705745697, -0.05637352913618088, 1.031400203704834, -0.9246614575386047, -0.25439730286598206, -0.10471276938915253, 0.36307895183...

null

surabhiMV/qrcode_new_t

surabhiMV

2023-11-15T09:18:12Z

21

0

null

[ "region:us" ]

2023-11-15T09:18:12Z

2023-11-15T08:37:00.000Z

2023-11-15T08:37:00

--- dataset_info: features: - name: image dtype: image - name: label dtype: image splits: - name: train num_bytes: 27849041.0 num_examples: 352 download_size: 0 dataset_size: 27849041.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "qrcode_new_t" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.47327661514282227, -0.1491951048374176, 0.22415554523468018, 0.1165425181388855, -0.5265224575996399, 0.31240206956863403, 0.3087393045425415, -0.005761673208326101, 0.9088855981826782, 0.44011569023132324, -0.7182378172874451, -1.142026662826538, -0.5739120841026306, -0.294203519821167...

null

surabhiMV/qrcode_val_new_tt

surabhiMV

2023-11-15T09:18:20Z

21

0

null

[ "region:us" ]

2023-11-15T09:18:20Z

2023-11-15T08:37:18.000Z

2023-11-15T08:37:18

--- dataset_info: features: - name: image dtype: image - name: label dtype: image splits: - name: train num_bytes: 3442951.0 num_examples: 41 download_size: 0 dataset_size: 3442951.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "qrcode_val_new_tt" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.5438205599784851, -0.20296135544776917, 0.17501139640808105, 0.08683181554079056, -0.5140659213066101, 0.20997841656208038, 0.5064737796783447, -0.03224683925509453, 0.888509213924408, 0.5102009773254395, -0.725538432598114, -0.9899101257324219, -0.6093103885650635, -0.24922700226306915...

null

mrisdi/mask

mrisdi

2023-11-16T07:59:37Z

21

0

null

[ "license:cc-by-4.0", "region:us" ]

2023-11-16T07:59:37Z

2023-11-16T07:30:43.000Z

2023-11-16T07:30:43

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

[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...

null

Jcuhfehl/OpenHermes-ChatML

Jcuhfehl

2023-11-16T22:33:52Z

21

1

null

[ "region:us" ]

2023-11-16T22:33:52Z

2023-11-16T22:17:40.000Z

2023-11-16T22:17:40

--- dataset_info: features: - name: data dtype: string splits: - name: train num_bytes: 314789363 num_examples: 242831 download_size: 136731208 dataset_size: 314789363 configs: - config_name: default data_files: - split: train path: data/train-* --- ChatML converted version of openhermes dataset. Useful for usage in directly fine tuning.

[ -0.4197345972061157, -0.5473476648330688, -0.2609793245792389, 0.18368805944919586, -0.18113110959529877, -0.4932490289211273, -0.6506285667419434, -0.21802332997322083, -0.5224784016609192, 0.8787569403648376, -0.7093074321746826, -0.8458012938499451, 0.07699146121740341, -0.0384285524487...

null

suncy13/longthaisum_8k

suncy13

2023-11-17T03:31:07Z

21

0

null

[ "region:us" ]

2023-11-17T03:31:07Z

2023-11-17T03:30:06.000Z

2023-11-17T03:30:06

Entry not found

[ -0.32276487350463867, -0.22568444907665253, 0.8622263073921204, 0.43461570143699646, -0.5282988548278809, 0.7012969255447388, 0.7915717363357544, 0.07618642598390579, 0.7746027112007141, 0.25632190704345703, -0.7852815389633179, -0.22573848068714142, -0.910447895526886, 0.5715675354003906,...

null

keylazy/ark-raw

keylazy

2023-11-19T04:12:04Z

21

0

null

[ "region:us" ]

2023-11-19T04:12:04Z

2023-11-19T01:09:28.000Z

2023-11-19T01:09:28

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: text1 dtype: string - name: text2 dtype: string splits: - name: train num_bytes: 274489671 num_examples: 1000000 - name: test num_bytes: 27481428 num_examples: 100000 download_size: 189424610 dataset_size: 301971099 --- # Dataset Card for "ark-raw" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.6476241946220398, -0.297654926776886, 0.11160268634557724, 0.032134540379047394, -0.4180198013782501, 0.12880264222621918, 0.20942562818527222, -0.48744621872901917, 0.709886908531189, 0.47843170166015625, -0.7904418110847473, -0.8951270580291748, -0.41208598017692566, 0.047388728708028...

null

EP45/test

EP45

2023-11-27T05:37:17Z

21

0

null

[ "region:us" ]

2023-11-27T05:37:17Z

2023-11-20T05:28:11.000Z

2023-11-20T05:28:11

--- configs: - config_name: 1k data_files: - split: train path: "data/1k.parquet" - split: test path: "data/test.parquet" - config_name: 5k data_files: - split: train path: "data/5k.parquet" - split: test path: "data/test.parquet" - config_name: 10k data_files: - split: train path: "data/10k.parquet" - split: test path: "data/test.parquet" - config_name: 15k data_files: - split: train path: "data/15k.parquet" - split: test path: "data/test.parquet" default: true - config_name: 20k data_files: - split: train path: "data/20k.parquet" - split: test path: "data/test.parquet" - config_name: 30k data_files: - split: train path: "data/30k.parquet" - split: test path: "data/test.parquet" - config_name: 50k data_files: - split: train path: "data/50k.parquet" - split: test path: "data/test.parquet" --- # vicuna 실험용 데이터셋 다음 데이터셋으로부터 변환됨: https://huggingface.co/datasets/junelee/sharegpt_deepl_ko ## 파일구조 - converted.parquet : 원본 데이터셋의 ko_alpaca_style_dataset.json을 트레이닝에 맞도록 형식 변환 ## 라이센스 원본 데이터가 OPENAI 이기 때문에 해당 [약관](https://openai.com/policies/terms-of-use)에 따릅니다. 그 이외의 부분은 다음 라이센스를 따릅니다: 저작자표시 2.0 대한민국 (CC BY 2.0 KR)

[ -0.5931993722915649, -0.8777629137039185, 0.3355540931224823, 0.6611758470535278, -1.0450772047042847, -0.4155179262161255, 0.026989666745066643, -0.01823596842586994, 0.6605675220489502, 0.7207453846931458, -0.5967921018600464, -1.1688706874847412, -0.64545738697052, 0.10915059596300125, ...

null

hyperinfer/old_texts

hyperinfer

2023-11-20T16:03:31Z

21

0

null

[ "license:mit", "region:us" ]

2023-11-20T16:03:31Z

2023-11-20T14:58:10.000Z

2023-11-20T14:58:10

--- license: mit --- This dataset is generated from the latest datadump from the Open Legal Data datadump, dated at 19-Oct-2022 12:11. See source: https://static.openlegaldata.io/dumps/de/2022-10-18/ The dataset was encoded in UTF-8 and cleaned for HTML-Tags and Escape Sequences. The Train / Test Split is 80/20.

[ -0.30216413736343384, -0.5099013447761536, -0.01817041076719761, -0.12767700850963593, -0.47625723481178284, -0.4149077832698822, -0.23575352132320404, -0.5013805031776428, 0.006792398169636726, 0.8810299038887024, -0.48154693841934204, -0.6027430891990662, -0.21151261031627655, 0.16026304...

null

tyzhu/squad_qa_num_v5_full

tyzhu

2023-11-21T09:51:05Z

21

0

null

[ "region:us" ]

2023-11-21T09:51:05Z

2023-11-21T08:55:22.000Z

2023-11-21T08:55:22

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: answer dtype: string - name: context_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 7245335 num_examples: 5070 - name: validation num_bytes: 343184 num_examples: 300 download_size: 0 dataset_size: 7588519 --- # Dataset Card for "squad_qa_num_v5_full" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.5480422973632812, -0.013804401271045208, 0.32792264223098755, 0.47509002685546875, -0.33401840925216675, 0.2334020882844925, 0.5014901757240295, -0.05532803758978844, 0.8024572730064392, 0.4534328877925873, -1.1122581958770752, -0.9252749681472778, -0.332602858543396, 0.0901881754398346...

null

tyzhu/squad_qa_wrong_title_v5_full

tyzhu

2023-11-21T09:53:00Z

21

0

null

[ "region:us" ]

2023-11-21T09:53:00Z

2023-11-21T09:21:02.000Z

2023-11-21T09:21:02

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: answer dtype: string - name: context_id dtype: string - name: correct_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 7596960 num_examples: 5070 - name: validation num_bytes: 361864 num_examples: 300 download_size: 1530108 dataset_size: 7958824 --- # Dataset Card for "squad_qa_wrong_title_v5_full" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.2313343584537506, -0.07906261086463928, 0.31671038269996643, 0.48092979192733765, -0.286103218793869, 0.3063718378543854, 0.5095766186714172, -0.13219214975833893, 0.6741253137588501, 0.36354291439056396, -1.167021632194519, -0.7680577039718628, -0.5951005220413208, 0.15646158158779144,...

null

A-Roucher/Open_Assistant_Chains_German_Translation

A-Roucher

2023-11-23T08:30:05Z

21

1

null

[ "task_categories:conversational", "task_categories:text-generation", "size_categories:10K<n<100K", "language:en", "language:de", "license:apache-2.0", "human-feedback", "region:us" ]

2023-11-23T08:30:05Z

2023-11-21T15:33:31.000Z

2023-11-21T15:33:31

--- language: - en - de license: apache-2.0 size_categories: - 10K<n<100K task_categories: - conversational - text-generation pretty_name: OpenAssistant Conversation Chains - With German Translation tags: - human-feedback configs: - config_name: default data_files: - split: train_english path: data/train_english-* - split: train_german path: data/train_german-* dataset_info: features: - name: conversation_id dtype: string - name: user_id dtype: string - name: created_date dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: lang_original dtype: string - name: could_be_code dtype: bool splits: - name: train_english num_bytes: 29675151 num_examples: 18192 - name: train_german num_bytes: 28931906 num_examples: 18192 download_size: 21854409 dataset_size: 58607057 --- # Dataset Card for Dataset Name ## Dataset description  This dataset is derived from [OpenAssistant Conversation Chains](https://huggingface.co/datasets/A-Roucher/Open_Assistant_Conversation_Chains), which is a reformatting of [OpenAssistant Conversations (OASST1)](https://huggingface.co/datasets/OpenAssistant/oasst1), which is itself > a human-generated, human-annotated assistant-style conversation corpus consisting of 161,443 messages in 35 different languages, annotated with 461,292 quality ratings, resulting in over 10,000 fully annotated conversation trees. The corpus is a product of a worldwide crowd-sourcing effort involving over 13,500 volunteers. It was modified from the original by adding a german translation of each conversation, generated with [Helsinki-NLP/opus-mt-en-de](https://huggingface.co/Helsinki-NLP/opus-mt-en-de). #### Who are the editors? Aymeric Roucher, Hugging Face. ### Example ```` { 'conversation_id': '6708c47f-05c9-4346-b3d2-40b2bd24fde4', 'user_id': '2c96e467-66f0-4be7-9693-bda51356a424', 'created_date': '2023-02-06T18:48:49.391686+00:00', 'messages_original': [ {'content': 'Can you write a short introduction about the relevance of the term "monopsony" in economics? Please use examples related to potential monopsonies in the labour market and cite relevant research.', 'role': 'user'}, {'content': '"Monopsony" refers to a market structure where there is only one buyer for a particular good or service. In economics, this term is particularly relevant in the labor market, where a monopsony employer has significant power over the wages and working conditions of their employees. The presence of a monopsony can result in lower wages and reduced employment opportunities for workers, as the employer has little incentive to increase wages or provide better working conditions.\n\nRecent research has identified potential monopsonies in industries such as retail and fast food, where a few large companies control a significant portion of the market (Bivens & Mishel, 2013). In these industries, workers often face low wages, limited benefits, and reduced bargaining power, leading to a situation where they are dependent on the employer for their livelihood. This dependence can result in further suppression of wages and a decline in working conditions.\n\nOverall, the concept of monopsony is essential to understanding the dynamics of labor markets and the impact of market power on workers. Further research is needed to understand the extent and impact of monopsonies on the economy and to develop policies to address this issue.\n\nReferences:\nBivens, J., & Mishel, L. (2013). The Pay of Corporate Executives and Financial Professionals as Evidence of Rents in Top 1 Percent Incomes. Journal of Economic Perspectives, 27(3), 57-78.', 'role': 'assistant'}, {'content': 'Now explain it to a dog', 'role': 'user'} ], 'lang_original': 'en' 'messages_german': { "content": "Können Sie eine kurze Einführung über die Relevanz des Begriffs \"Monopsonie\" in der Wirtschaft schreiben? Bitte verwenden Sie Beispiele für mögliche Monopsonien auf dem Arbeitsmarkt und zitieren Sie relevante Forschung.", "role": "user" }, { "content": "\"Monopsony\" bezieht sich auf eine Marktstruktur, in der es nur einen Käufer für ein bestimmtes Gut oder eine bestimmte Dienstleistung gibt. In der Wirtschaft ist dieser Begriff besonders relevant auf dem Arbeitsmarkt, wo ein Monopsony Arbeitgeber über die Löhne und Arbeitsbedingungen ihrer Mitarbeiter verfügt. Das Vorhandensein eines Monopsony kann zu niedrigeren Löhnen und reduzierten Beschäftigungsmöglichkeiten für Arbeitnehmer führen, da der Arbeitgeber wenig Anreiz hat, Löhne zu erhöhen oder bessere Arbeitsbedingungen zu bieten. Neuere Forschung hat mögliche Monopsonies in Branchen wie Einzelhandel und Fast Food identifiziert, wo ein paar große Unternehmen einen erheblichen Teil des Marktes kontrollieren (Bivens & Mishel, 2013). In diesen Branchen ist das Konzept der Monopsony oft mit niedrigen Löhnen, begrenzten Vorteilen und reduzierten Verhandlungsmacht, was zu einer Situation führt, in der sie vom Arbeitgeber für ihren Lebensunterhalt abhängig sind. Diese Abhängigkeit kann zu einer weiteren Unterdrückung der Löhne und einem Rückgang der Arbeitsbedingungen führen. Insgesamt ist das Konzept der Monopsony wesentlich, um die Dynamik der Arbeitsmärkte und die Auswirkungen der Marktmacht auf die Arbeitnehmer zu verstehen. Weitere Forschung ist erforderlich, um das Ausmaß und die Auswirkungen der Monopsonies auf die Wirtschaft zu verstehen und zu entwickeln.", "role": "assistant" }, { "content": "Nun erklären Sie es einem Hund", "role": "user" } ] } ````

[ -0.378668874502182, -0.752553403377533, 0.10294798761606216, 0.32993966341018677, -0.2658137083053589, -0.12438317388296127, 0.04059802368283272, -0.8141024112701416, 1.155905842781067, 0.5101513266563416, -0.6865105032920837, -0.3921552002429962, -0.5777449607849121, 0.35048583149909973, ...

null

Aman279/Updated_Msc_Dataset

Aman279

2023-11-21T21:53:50Z

21

0

null

[ "region:us" ]

2023-11-21T21:53:50Z

2023-11-21T20:49:16.000Z

2023-11-21T20:49:16

Entry not found

[ -0.3227645754814148, -0.22568479180335999, 0.8622263669967651, 0.43461522459983826, -0.52829909324646, 0.7012971639633179, 0.7915719747543335, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104475975036621, 0.5715674161911011, -...

null

argilla/distilabel-math-instructions

argilla

2023-11-23T13:55:37Z

21

0

null

[ "region:us" ]

2023-11-23T13:55:37Z

2023-11-21T21:51:41.000Z

2023-11-21T21:51:41

--- dataset_info: features: - name: instructions dtype: string splits: - name: train num_bytes: 410539 num_examples: 4699 download_size: 150900 dataset_size: 410539 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "distilabel-math-instructions" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.5174394845962524, -0.48494142293930054, 0.23860183358192444, 0.4925825297832489, -0.24359136819839478, 0.052883729338645935, 0.11421692371368408, 0.338064044713974, 0.5422951579093933, 0.17207366228103638, -1.0661214590072632, -0.7733709216117859, -0.7634660005569458, -0.267955720424652...

null

gilkeyio/librispeech-alignments

gilkeyio

2023-11-22T16:03:34Z

21

0

null

[ "task_categories:automatic-speech-recognition", "size_categories:100K<n<1M", "language:en", "license:cc-by-4.0", "arxiv:1512.02595", "region:us" ]

2023-11-22T16:03:34Z

2023-11-22T04:15:29.000Z

2023-11-22T04:15:29

--- dataset_info: features: - name: sex dtype: string - name: subset dtype: string - name: id dtype: string - name: audio dtype: audio - name: transcript dtype: string - name: words list: - name: end dtype: float64 - name: start dtype: float64 - name: word dtype: string - name: phonemes list: - name: end dtype: float64 - name: phoneme dtype: string - name: start dtype: float64 splits: - name: dev_clean num_bytes: 365310608.879 num_examples: 2703 - name: dev_other num_bytes: 341143993.784 num_examples: 2864 - name: test_clean num_bytes: 377535532.98 num_examples: 2620 - name: test_other num_bytes: 351207892.569557 num_examples: 2938 - name: train_clean_100 num_bytes: 6694747231.610863 num_examples: 28538 - name: train_clean_360 num_bytes: 24163659711.787865 num_examples: 104008 - name: train_other_500 num_bytes: 32945085271.89443 num_examples: 148645 download_size: 62101682957 dataset_size: 65238690243.50571 configs: - config_name: default data_files: - split: dev_clean path: data/dev_clean-* - split: dev_other path: data/dev_other-* - split: test_clean path: data/test_clean-* - split: test_other path: data/test_other-* - split: train_clean_100 path: data/train_clean_100-* - split: train_clean_360 path: data/train_clean_360-* - split: train_other_500 path: data/train_other_500-* license: cc-by-4.0 task_categories: - automatic-speech-recognition language: - en pretty_name: Librispeech Alignments size_categories: - 100K<n<1M --- # Dataset Card for Librispeech Alignments Librispeech with alignments generated by the [Montreal Forced Aligner](https://montreal-forced-aligner.readthedocs.io/en/latest/). The original alignments in TextGrid format can be found [here](https://zenodo.org/records/2619474) ## Dataset Details ### Dataset Description Librispeech is a corpus of read English speech, designed for training and evaluating automatic speech recognition (ASR) systems. The dataset contains 1000 hours of 16kHz read English speech derived from audiobooks. The Montreal Forced Aligner (MFA) was used to generate word and phoneme level alignments for the Librispeech dataset. - **Curated by:** Vassil Panayotov, Guoguo Chen, Daniel Povey, Sanjeev Khudanpur (for Librispeech) - **Funded by:** DARPA LORELEI - **Shared by:** Loren Lugosch (for Alignments) - **Language(s) (NLP):** English - **License:** Creative Commons Attribution 4.0 International License ### Dataset Sources - **Repository:** https://www.openslr.org/12 - **Paper:** https://arxiv.org/abs/1512.02595 - **Alignments:** https://zenodo.org/record/2619474 ## Uses ### Direct Use The Librispeech dataset can be used to train and evaluate ASR systems. The alignments allow for forced alignment techniques. ### Out-of-Scope Use The dataset only contains read speech, so may not perform as well on spontaneous conversational speech. ## Dataset Structure The dataset contains 1000 hours of segmented read English speech from audiobooks. There are three train subsets: 100 hours (train-clean-100), 360 hours (train-clean-360) and 500 hours (train-other-500). The alignments connect the audio to the reference text transcripts on word and phoneme level. ### Data Fields - sex: M for male, F for female - subset: dev_clean, dev_other, test_clean, test_other, train_clean_100, train_clean_360, train_other_500 - id: unique id of the data sample. (speaker id)-(chapter-id)-(utterance-id) - audio: the audio, 16kHz - transcript: the spoken text of the dataset, normalized and lowercased - words: a list of words with fields: - word: the text of the word - start: the start time in seconds - end: the end time in seconds - phonemes: a list of phonemes with fields: - phoneme: the phoneme spoken - start: the start time in seconds - end: the end time in seconds ## Dataset Creation ### Curation Rationale Librispeech was created to further speech recognition research and to benchmark progress in the field. ### Source Data #### Data Collection and Processing The audio and reference texts were sourced from read English audiobooks in the LibriVox project. The data was segmented, filtered and prepared for speech recognition. #### Who are the source data producers? The audiobooks are read by volunteers for the LibriVox project. Information about the readers is available in the LibriVox catalog. ### Annotations #### Annotation process The Montreal Forced Aligner was used to create word and phoneme level alignments between the audio and reference texts. The aligner is based on Kaldi. In the process of formatting this into a HuggingFace dataset, words with empty text and phonemes with empty text, silence tokens, or spacing tokens were removed #### Who are the annotators? The alignments were generated automatically by the Montreal Forced Aligner and shared by Loren Lugosch. The TextGrid files were parsed and integrated into this dataset by Kim Gilkey. #### Personal and Sensitive Information The data contains read speech and transcripts. No personal or sensitive information expected. ## Bias, Risks, and Limitations The dataset contains only read speech from published books, not natural conversational speech. Performance on other tasks may be reduced. ### Recommendations Users should understand that the alignments may contain errors and account for this in applications. For example, be wary of <UNK> tokens. ## Citation **Librispeech:** ``` @inproceedings{panayotov2015librispeech, title={Librispeech: an ASR corpus based on public domain audio books}, author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev}, booktitle={ICASSP}, year={2015}, organization={IEEE} } ``` **Librispeech Alignments:** ``` Loren Lugosch, Mirco Ravanelli, Patrick Ignoto, Vikrant Singh Tomar, and Yoshua Bengio, "Speech Model Pre-training for End-to-End Spoken Language Understanding", Interspeech 2019. ``` **Montreal Forced Aligner:** ``` Michael McAuliffe, Michaela Socolof, Sarah Mihuc, Michael Wagner, and Morgan Sonderegger. "Montreal Forced Aligner: trainable text-speech alignment using Kaldi", Interspeech 2017. ```

[ -0.15661679208278656, -0.3594324588775635, -0.019701460376381874, -0.06052987277507782, -0.17227020859718323, -0.14650903642177582, -0.12095780670642853, -0.4367973208427429, 0.15700869262218475, 0.563337504863739, -0.5959511399269104, -0.6242700219154358, -0.2992180287837982, -0.019511539...

null

idning/rainbow

idning

2023-11-22T15:29:32Z

21

0

null

[ "region:us" ]

2023-11-22T15:29:32Z

2023-11-22T15:13:20.000Z

2023-11-22T15:13:20

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 2303342.0 num_examples: 9216 download_size: 1170069 dataset_size: 2303342.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...

null

gilkeyio/inclusive_voice

gilkeyio

2023-11-23T03:34:27Z

21

0

null

[ "task_categories:audio-classification", "task_categories:automatic-speech-recognition", "size_categories:1K<n<10K", "language:en", "license:cc0-1.0", "arxiv:1912.06670", "region:us" ]

2023-11-23T03:34:27Z

2023-11-23T00:39:48.000Z

2023-11-23T00:39:48

--- dataset_info: features: - name: client_id dtype: string - name: audio dtype: audio - name: sentence dtype: string - name: age dtype: string - name: gender dtype: string - name: accent dtype: string splits: - name: train num_bytes: 254499181.875 num_examples: 6075 - name: test num_bytes: 76654946.864 num_examples: 1812 download_size: 327803775 dataset_size: 331154128.73899996 license: - cc0-1.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* task_categories: - audio-classification - automatic-speech-recognition language: - en pretty_name: Inclusive Voice size_categories: - 1K<n<10K --- # Dataset Card for Inclusive Voice A dataset curated from Mozilla Common Voice containing equal representation from male, female, and other ## Dataset Details ### Dataset Description - **Curated by:** Kim Gilkey - **Language(s) (NLP):** English - **License:** Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/) ### Dataset Sources Mozilla Common Voice - **Homepage:** https://commonvoice.mozilla.org/en/datasets - **Repository:** https://github.com/common-voice/common-voice - **Paper:** https://arxiv.org/abs/1912.06670 ## Uses ### Direct Use This dataset is intended for use in training and evaluating voice recognition models, particularly for applications in gender classification. ### Out-of-Scope Use The dataset is not suitable for applications requiring a wide range of dialects or non-English languages. It is also not designed for identifying fine-grained demographic characteristics beyond gender. You agree not to attempt to identify the individuals in the dataset. ## Dataset Structure ### Data Fields - **client_id:** A unique identifier for each participant. - **audio:** Audio recording of a spoken sentence. - **sentence:** The text content of the audio recording. - **age:** Categorical age group of the participant. - **gender:** Gender of the participant (balanced representation). - **accent:** Accent information of the participant, if available. ### Splits - **Train:** 6,075 examples (2,025 of each gender). - **Test:** 1,812 examples (604 of each gender). ## Dataset Creation ### Curation Rationale The dataset was specifically curated to address gender imbalance in voice recognition datasets, aiming to provide a balanced representation of male, female, and non-binary voices. ### Source Data #### Data Collection and Processing Derived from Mozilla's Common Voice project, the dataset underwent additional processing to balance gender representation and limit the dominance of any single voice. ## Bias, Risks, and Limitations Despite efforts to balance gender representation, the dataset may still have biases, such as over-representation of certain accents or age groups. It's not comprehensive in representing all possible variations in English speech. Since the original dataset contains relatively few female and even fewer non-binary voices, the dataset becomes quite small when filtered down. ## Citation The original Common Voice citation: ``` @inproceedings{commonvoice:2020, author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, title = {Common Voice: A Massively-Multilingual Speech Corpus}, booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, pages = {4211--4215}, year = 2020 } ``` ## Dataset Card Authors Kim Gilkey ## Dataset Card Contact kim@gilkey.io

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null

chiayewken/saycan

chiayewken

2023-11-23T04:21:11Z

21

0

null

[ "region:us" ]

2023-11-23T04:21:11Z

2023-11-23T04:17:30.000Z

2023-11-23T04:17:30

--- dataset_info: features: - name: INPUT dtype: string - name: OUTPUT dtype: string splits: - name: test num_bytes: 14865 num_examples: 99 download_size: 4765 dataset_size: 14865 configs: - config_name: default data_files: - split: test path: data/test-* --- # SayCan This repo contains the data for ["Do As I Can, Not As I Say: Grounding Language in Robotic Affordances"](https://say-can.github.io). The original data link is here: https://raw.githubusercontent.com/say-can/say-can.github.io/main/data/saycan_plan_v0_l.tsv This dataset is distributed with the CC BY 4.0 license.

[ -0.2915041744709015, -0.28996241092681885, 0.3762490749359131, -0.006807317025959492, -0.19181819260120392, 0.061969440430402756, -0.26167890429496765, -0.3453904390335083, 0.44702818989753723, 0.893625020980835, -0.8383233547210693, -0.722602128982544, -0.3417615592479706, 0.0207117684185...

null

anushvst/law_chat

anushvst

2023-11-24T09:24:06Z

21

0

null

[ "region:us" ]

2023-11-24T09:24:06Z

2023-11-23T05:24:45.000Z

2023-11-23T05:24:45

Entry not found

[ -0.3227645754814148, -0.22568479180335999, 0.8622264862060547, 0.43461528420448303, -0.52829909324646, 0.7012971639633179, 0.7915720343589783, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104477167129517, 0.5715674161911011, -...

null

Liberty-L/race_train_EN

Liberty-L

2023-11-26T06:15:39Z

21

0

null

[ "region:us" ]

2023-11-26T06:15:39Z

2023-11-24T07:23:37.000Z

2023-11-24T07:23:37

--- dataset_info: features: - name: example_id dtype: string - name: article dtype: string - name: answer dtype: string - name: question dtype: string - name: options sequence: string - name: input_ids sequence: sequence: int32 - name: attention_mask sequence: sequence: int8 - name: label dtype: int64 splits: - name: train num_bytes: 162846173 num_examples: 25421 download_size: 27127431 dataset_size: 162846173 configs: - config_name: default data_files: - split: train path: data/train-* ---

[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...

null

Liberty-L/race_val_EN

Liberty-L

2023-11-26T06:15:41Z

21

0

null

[ "region:us" ]

2023-11-26T06:15:41Z

2023-11-24T07:23:43.000Z

2023-11-24T07:23:43

--- dataset_info: features: - name: example_id dtype: string - name: article dtype: string - name: answer dtype: string - name: question dtype: string - name: options sequence: string - name: input_ids sequence: sequence: int32 - name: attention_mask sequence: sequence: int8 - name: label dtype: int64 splits: - name: train num_bytes: 9221971 num_examples: 1436 download_size: 1305467 dataset_size: 9221971 configs: - config_name: default data_files: - split: train path: data/train-* ---

[ -0.12853367626667023, -0.18616794049739838, 0.6529126763343811, 0.4943627417087555, -0.19319313764572144, 0.23607443273067474, 0.36071979999542236, 0.05056338757276535, 0.5793654322624207, 0.7400138974189758, -0.6508103013038635, -0.23783987760543823, -0.710224986076355, -0.047825977206230...

null

Liberty-L/race_test_EN

Liberty-L

2023-11-26T06:15:42Z

21

0

null

[ "region:us" ]

2023-11-26T06:15:42Z

2023-11-24T07:23:44.000Z

2023-11-24T07:23:44

--- dataset_info: features: - name: example_id dtype: string - name: article dtype: string - name: answer dtype: string - name: question dtype: string - name: options sequence: string - name: input_ids sequence: sequence: int32 - name: attention_mask sequence: sequence: int8 - name: label dtype: int64 splits: - name: train num_bytes: 9381086 num_examples: 1436 download_size: 1252839 dataset_size: 9381086 configs: - config_name: default data_files: - split: train path: data/train-* ---

[ -0.12853367626667023, -0.18616794049739838, 0.6529126763343811, 0.4943627417087555, -0.19319313764572144, 0.23607443273067474, 0.36071979999542236, 0.05056338757276535, 0.5793654322624207, 0.7400138974189758, -0.6508103013038635, -0.23783987760543823, -0.710224986076355, -0.047825977206230...

null

SrAlex/datos-leyes-civiles-peruanas-v2

SrAlex

2023-11-25T01:27:05Z

21

0

null

[ "region:us" ]

2023-11-25T01:27:05Z

2023-11-25T01:26:14.000Z

2023-11-25T01:26:14

Entry not found

[ -0.3227649927139282, -0.225684255361557, 0.862226128578186, 0.43461498618125916, -0.5282987952232361, 0.7012963891029358, 0.7915717363357544, 0.07618629932403564, 0.7746025919914246, 0.2563219666481018, -0.7852816581726074, -0.2257382869720459, -0.9104480743408203, 0.5715669393539429, -0...

null

luizlzg/drbyte_test

luizlzg

2023-11-25T19:29:07Z

21

0

null

[ "task_categories:text-generation", "size_categories:10K<n<100K", "language:pt", "medical", "biology", "region:us" ]

2023-11-25T19:29:07Z

2023-11-25T19:24:04.000Z

2023-11-25T19:24:04

--- task_categories: - text-generation language: - pt tags: - medical - biology size_categories: - 10K<n<100K ---

[ -0.12853367626667023, -0.18616794049739838, 0.6529126763343811, 0.4943627417087555, -0.19319313764572144, 0.23607443273067474, 0.36071979999542236, 0.05056338757276535, 0.5793654322624207, 0.7400138974189758, -0.6508103013038635, -0.23783987760543823, -0.710224986076355, -0.047825977206230...

null

andersonbcdefg/fake_dataset

andersonbcdefg

2023-11-26T02:59:28Z

21

0

null

[ "region:us" ]

2023-11-26T02:59:28Z

2023-11-26T01:28:36.000Z

2023-11-26T01:28:36

--- dataset_info: features: - name: tokens sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 6240 num_examples: 8 download_size: 5472 dataset_size: 6240 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "fake_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.5041502118110657, -0.5325407981872559, 0.21512064337730408, 0.24092401564121246, -0.19757810235023499, 0.22814859449863434, 0.4462225139141083, -0.25180584192276, 1.0973870754241943, 0.45643898844718933, -0.9819025993347168, -0.5301318764686584, -0.4496355652809143, -0.23729287087917328...

null

adamjweintraut/eli5_lfqa_slice

adamjweintraut

2023-11-27T06:28:50Z

21

0

null

[ "region:us" ]

2023-11-27T06:28:50Z

2023-11-27T06:28:35.000Z

2023-11-27T06:28:35

--- dataset_info: features: - name: index dtype: int64 - name: q_id dtype: string - name: question dtype: string - name: best_answer dtype: string - name: all_answers sequence: string - name: num_answers dtype: int64 - name: context dtype: string - name: orig dtype: string - name: target dtype: string splits: - name: train num_bytes: 275385111.5562014 num_examples: 20000 - name: test num_bytes: 34423138.944525175 num_examples: 2500 - name: validation num_bytes: 34423138.944525175 num_examples: 2500 download_size: 207239662 dataset_size: 344231389.44525176 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---

[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...

null

jinmang2/common-sense-mrc

jinmang2

2021-12-12T07:56:31Z

20

0

null

[ "region:us" ]

2021-12-12T07:56:31Z

2022-03-02T23:29:22.000Z

2022-03-02T23:29:22

Entry not found

[ -0.3227647542953491, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965083122253, 0.7915717959403992, 0.07618629932403564, 0.7746022343635559, 0.2563222348690033, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

limjiayi/hateful_memes_expanded

limjiayi

2021-12-06T05:17:02Z

20

2

null

[ "region:us" ]

2021-12-06T05:17:02Z

2022-03-02T23:29:22.000Z

2022-03-02T23:29:22

Entry not found

[ -0.3227647542953491, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965083122253, 0.7915717959403992, 0.07618629932403564, 0.7746022343635559, 0.2563222348690033, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

lpsc-fiuba/melisa

lpsc-fiuba

2022-10-22T08:52:56Z

20

3

null

[ "task_categories:text-classification", "task_ids:language-modeling", "task_ids:sentiment-classification", "task_ids:sentiment-scoring", "task_ids:topic-classification", "annotations_creators:found", "language_creators:found", "source_datasets:original", "language:es", "language:pt", "license:oth...

2022-10-22T08:52:56Z

2022-03-02T23:29:22.000Z

2022-03-02T23:29:22

--- annotations_creators: - found language_creators: - found language: - es - pt license: - other multilinguality: all_languages: - multilingual es: - monolingual pt: - monolingual paperswithcode_id: null size_categories: all_languages: - 100K<n<1M es: - 100K<n<1M pt: - 100K<n<1M source_datasets: - original task_categories: - conditional-text-generation - sequence-modeling - text-classification - text-scoring task_ids: - language-modeling - sentiment-classification - sentiment-scoring - summarization - topic-classification --- # Dataset Card for MeLiSA (Mercado Libre for Sentiment Analysis) ** **NOTE: THIS CARD IS UNDER CONSTRUCTION** ** ** **NOTE 2: THE RELEASED VERSION OF THIS DATASET IS A DEMO VERSION.** ** ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Webpage:** https://github.com/lpsc-fiuba/MeLiSA - **Paper:** - **Point of Contact:** lestienne@fi.uba.ar [More Information Needed] ### Dataset Summary We provide a Mercado Libre product reviews dataset for spanish and portuguese text classification. The dataset contains reviews in these two languages collected between August 2020 and January 2021. Each record in the dataset contains the review content and title, the star rating, the country where it was pubilshed and the product category (arts, technology, etc.). The corpus is roughly balanced across stars, so each star rating constitutes approximately 20% of the reviews in each language. | || Spanish ||| Portugese || |---|:------:|:----------:|:-----:|:------:|:----------:|:-----:| | | Train | Validation | Test | Train | Validation | Test | | 1 | 88.425 | 4.052 | 5.000 | 50.801 | 4.052 | 5.000 | | 2 | 88.397 | 4.052 | 5.000 | 50.782 | 4.052 | 5.000 | | 3 | 88.435 | 4.052 | 5.000 | 50.797 | 4.052 | 5.000 | | 4 | 88.449 | 4.052 | 5.000 | 50.794 | 4.052 | 5.000 | | 5 | 88.402 | 4.052 | 5.000 | 50.781 | 4.052 | 5.000 | Table shows the number of samples per star rate in each split. There is a total of 442.108 training samples in spanish and 253.955 in portuguese. We limited the number of reviews per product to 30 and we perform a ranked inclusion of the downloaded reviews to include those with rich semantic content. In these ranking, the lenght of the review content and the valorization (difference between likes and dislikes) was prioritized. For more details on this process, see (CITATION). Reviews in spanish were obtained from 8 different Latin Amercian countries (Argentina, Colombia, Peru, Uruguay, Chile, Venezuela and Mexico), and portuguese reviews were extracted from Brasil. To match the language with its respective country, we applied a language detection algorithm based on the works of Joulin et al. (2016a and 2016b) to determine the language of the review text and we removed reviews that were not written in the expected language. [More Information Needed] ### Languages The dataset contains reviews in Latin American Spanish and Portuguese. ## Dataset Structure ### Data Instances Each data instance corresponds to a review. Each split is stored in a separated `.csv` file, so every row in each file consists on a review. For example, here we show a snippet of the spanish training split: ```csv country,category,review_content,review_title,review_rate ... MLA,Tecnología y electrónica / Tecnologia e electronica,Todo bien me fue muy util.,Muy bueno,2 MLU,"Salud, ropa y cuidado personal / Saúde, roupas e cuidado pessoal",No fue lo que esperaba. El producto no me sirvió.,No fue el producto que esperé ,2 MLM,Tecnología y electrónica / Tecnologia e electronica,No fue del todo lo que se esperaba.,No me fue muy funcional ahí que hacer ajustes,2 ... ``` ### Data Fields - `country`: The string identifier of the country. It could be one of the following: `MLA` (Argentina), `MCO` (Colombia), `MPE` (Peru), `MLU` (Uruguay), `MLC` (Chile), `MLV` (Venezuela), `MLM` (Mexico) or `MLB` (Brasil). - `category`: String representation of the product's category. It could be one of the following: - Hogar / Casa - Tecnologı́a y electrónica / Tecnologia e electronica - Salud, ropa y cuidado personal / Saúde, roupas e cuidado pessoal - Arte y entretenimiento / Arte e Entretenimiento - Alimentos y Bebidas / Alimentos e Bebidas - `review_content`: The text content of the review. - `review_title`: The text title of the review. - `review_rate`: An int between 1-5 indicating the number of stars. ### Data Splits Each language configuration comes with it's own `train`, `validation`, and `test` splits. The `all_languages` split is simply a concatenation of the corresponding split across all languages. That is, the `train` split for `all_languages` is a concatenation of the `train` splits for each of the languages and likewise for `validation` and `test`. ## Dataset Creation ### Curation Rationale The dataset is motivated by the desire to advance sentiment analysis and text classification in Latin American Spanish and Portuguese. ### Source Data #### Initial Data Collection and Normalization The authors gathered the reviews from the marketplaces in Argentina, Colombia, Peru, Uruguay, Chile, Venezuela and Mexico for the Spanish language and from Brasil for Portuguese. They prioritized reviews that contained relevant semantic content by applying a ranking filter based in the lenght and the valorization (difference betweent the number of likes and dislikes) of the review. They then ensured the correct language by applying a semi-automatic language detection algorithm, only retaining those of the target language. No normalization was applied to the review content or title. Original products categories were grouped in higher level categories, resulting in five different types of products: "Home" (Hogar / Casa), "Technology and electronics" (Tecnologı́a y electrónica / Tecnologia e electronica), "Health, Dress and Personal Care" (Salud, ropa y cuidado personal / Saúde, roupas e cuidado pessoal) and "Arts and Entertainment" (Arte y entretenimiento / Arte e Entretenimiento). #### Who are the source language producers? The original text comes from Mercado Libre customers reviewing products on the marketplace across a variety of product categories. ### Annotations #### Annotation process Each of the fields included are submitted by the user with the review or otherwise associated with the review. No manual or machine-driven annotation was necessary. #### Who are the annotators? N/A ### Personal and Sensitive Information Mercado Libre Reviews are submitted by users with the knowledge and attention of being public. The reviewer ID's included in this dataset are anonymized, meaning that they are disassociated from the original user profiles. However, these fields would likely be easy to deannoymize given the public and identifying nature of free-form text responses. ## Considerations for Using the Data ### Social Impact of Dataset Although Spanish and Portuguese languages are relatively high resource, most of the data is collected from European or United State users. This dataset is part of an effort to encourage text classification research in languages other than English and European Spanish and Portuguese. Such work increases the accessibility of natural language technology to more regions and cultures. ### Discussion of Biases The data included here are from unverified consumers. Some percentage of these reviews may be fake or contain misleading or offensive language. ### Other Known Limitations The dataset is constructed so that the distribution of star ratings is roughly balanced. This feature has some advantages for purposes of classification, but some types of language may be over or underrepresented relative to the original distribution of reviews to acheive this balance. [More Information Needed] ## Additional Information ### Dataset Curators Published by Lautaro Estienne, Matías Vera and Leonardo Rey Vega. Managed by the Signal Processing in Comunications Laboratory of the Electronic Department at the Engeneering School of the Buenos Aires University (UBA). ### Licensing Information Amazon has licensed this dataset under its own agreement, to be found at the dataset webpage here: https://docs.opendata.aws/amazon-reviews-ml/license.txt ### Citation Information Please cite the following paper if you found this dataset useful: (CITATION) [More Information Needed] ### Contributions [More Information Needed]

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null

microsoft/codexglue_method_generation

microsoft

2021-10-28T07:03:55Z

20

7

null

[ "region:us" ]

2021-10-28T07:03:55Z

2022-03-02T23:29:22.000Z

2022-03-02T23:29:22

Entry not found

[ -0.3227645754814148, -0.22568479180335999, 0.8622263669967651, 0.43461522459983826, -0.52829909324646, 0.7012971639633179, 0.7915719747543335, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104475975036621, 0.5715674161911011, -...

null

nlpyeditepe/tr_rte

nlpyeditepe

2022-07-01T15:28:27Z

20

0

null

[ "task_categories:text-classification", "task_ids:natural-language-inference", "annotations_creators:found", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:extended|glue", "license:mit", "region:us" ]

2022-07-01T15:28:27Z

2022-03-02T23:29:22.000Z

2022-03-02T23:29:22

--- annotations_creators: - found language_creators: - machine-generated language: - tr-TR license: - mit multilinguality: - monolingual pretty_name: RTE for Turkish size_categories: - unknown source_datasets: - extended|glue task_categories: - text-classification task_ids: - natural-language-inference ---

[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...

null

ruanchaves/snap

ruanchaves

2022-10-20T19:12:47Z

20

1

null

[ "annotations_creators:expert-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:en", "license:unknown", "word-segmentation", "region:us" ]

2022-10-20T19:12:47Z

2022-03-06T00:17:23.000Z

2022-03-06T00:17:23

--- annotations_creators: - expert-generated language_creators: - machine-generated language: - en license: - unknown multilinguality: - monolingual size_categories: - unknown source_datasets: - original task_categories: - structure-prediction task_ids: [] pretty_name: SNAP tags: - word-segmentation --- # Dataset Card for SNAP ## Dataset Description - **Repository:** [ardax/hashtag-segmentor](https://github.com/ardax/hashtag-segmentor) - **Paper:** [Segmenting hashtags using automatically created training data](http://www.lrec-conf.org/proceedings/lrec2016/pdf/708_Paper.pdf) ### Dataset Summary Automatically segmented 803K SNAP Twitter Data Set hashtags with the heuristic described in the paper "Segmenting hashtags using automatically created training data". ### Languages English ## Dataset Structure ### Data Instances ``` { "index": 0, "hashtag": "BrandThunder", "segmentation": "Brand Thunder" } ``` ### Data Fields - `index`: a numerical index. - `hashtag`: the original hashtag. - `segmentation`: the gold segmentation for the hashtag. ## Dataset Creation - All hashtag segmentation and identifier splitting datasets on this profile have the same basic fields: `hashtag` and `segmentation` or `identifier` and `segmentation`. - The only difference between `hashtag` and `segmentation` or between `identifier` and `segmentation` are the whitespace characters. Spell checking, expanding abbreviations or correcting characters to uppercase go into other fields. - There is always whitespace between an alphanumeric character and a sequence of any special characters ( such as `_` , `:`, `~` ). - If there are any annotations for named entity recognition and other token classification tasks, they are given in a `spans` field. ## Additional Information ### Citation Information ``` @inproceedings{celebi2016segmenting, title={Segmenting hashtags using automatically created training data}, author={Celebi, Arda and {\"O}zg{\"u}r, Arzucan}, booktitle={Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)}, pages={2981--2985}, year={2016} } ``` ### Contributions This dataset was added by [@ruanchaves](https://github.com/ruanchaves) while developing the [hashformers](https://github.com/ruanchaves/hashformers) library.

[ -0.5316988825798035, -0.582749605178833, 0.22594639658927917, -0.0311012901365757, -0.4195573925971985, 0.3902314007282257, -0.11172207444906235, -0.5269083380699158, 0.5141822099685669, 0.09611895680427551, -0.5884799957275391, -0.7664903998374939, -0.5935624241828918, 0.09328082948923111...

null