Datasets:

librarian-bots

/

dataset_cards_with_metadata

like 11

Follow

Librarian Bots 57

violetxi/hendrycks-math-mc-llama-sft-intermediate-part1-of-1

violetxi

--- dataset_info: features: - name: problem dtype: string - name: target_answer dtype: string - name: target_solution dtype: string - name: generated dtype: string - name: answer dtype: string - name: is_target dtype: bool splits: - name: test num_bytes: 30216206 num_examples: 28954 download_size: 4707820 dataset_size: 30216206 configs: - config_name: default data_files: - split: test path: data/test-* ---

udamaurizio/parler_tts_mini_V01_TestVoice_Italian_V1

udamaurizio

--- language: - it ---

juliadollis/stf_regex_ner_2_fuzzycosseno_80

juliadollis

--- dataset_info: features: - name: inteiro_teor dtype: string - name: url_download dtype: string - name: dataDecisao dtype: timestamp[ns] - name: dataPublicacao dtype: timestamp[ns] - name: decisao dtype: string - name: descricaoClasse dtype: string - name: ementa dtype: string - name: id dtype: string - name: jurisprudenciaCitada dtype: string - name: ministroRelator dtype: string - name: nomeOrgaoJulgador dtype: string - name: numeroProcesso dtype: string - name: referenciasLegislativas sequence: string - name: siglaClasse dtype: string - name: tipoDeDecisao dtype: string - name: titulo dtype: string - name: acordaosSimilares sequence: string - name: partes_lista_texto dtype: string - name: temaProcs sequence: string - name: inteiro_teor_regex dtype: string - name: NER struct: - name: JURISPRUDENCIA sequence: string - name: LEGISLACAO sequence: string - name: LOCAL sequence: string - name: ORGANIZACAO sequence: string - name: PESSOA sequence: string - name: TEMPO sequence: string - name: desambiguacao list: - name: class dtype: string - name: count dtype: int64 - name: elements sequence: string - name: entity dtype: string splits: - name: train num_bytes: 160888933 num_examples: 1000 download_size: 44243977 dataset_size: 160888933 configs: - config_name: default data_files: - split: train path: data/train-* ---

RussRobin/VDD

RussRobin

--- license: cc-by-4.0 --- VDD: Varied Drone Dataset for Semantic Segmentation Paper: https://arxiv.org/abs/2305.13608 GitHub Repo: https://github.com/RussRobin/VDD This HF repo contains VDD source images and annotations. Please refer to our GitHub Repo if you want to download our annotation of UDD and UAVid.

CaptionEmporium/dalle3-llama3.2-11b

CaptionEmporium

--- license: cc-by-sa-4.0 language: - en pretty_name: dalle3-llama3.2-11b tags: - image-text-dataset - synthetic-dataset - LLaMA - LLaMA-3.2b - synthetic-captions - synthetic-images - DALLE3 task_categories: - text-to-image - image-to-text - other size_categories: - 1M<n<10M --- # Dataset Card for dalle3-llama3.2-11b ## Dataset Description - **Point of Contact:** Caption Emporium ### Dataset Summary This is 3,577,716 new synthetic captions for the 1,192,572 images found in [ProGamerGov/synthetic-dataset-1m-dalle3-high-quality-captions](https://huggingface.co/datasets/ProGamerGov/synthetic-dataset-1m-dalle3-high-quality-captions). The dataset was filtered for duplicates and then re-encoded with JPEGXL lossless or lossy depending on the source. The long captions were produced using [meta-llama/Llama-3.2-11B-Vision-Instruct](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct). Medium and short captions were produced from these captions using [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct). It includes the original captions from that repository as well. The dataset was also tagged with [SmilingWolf/wd-eva02-large-tagger-v3](https://huggingface.co/SmilingWolf/wd-eva02-large-tagger-v3). Llama-3.2-11B-Vision-Instruct was found to be able to discern an extensively larger number of cultural references as compared to CogVLM used to previous caption the images. For example, > A custom funko pop figurine of juan manuel prat, a data and ai expert at accenture, alongside his figurine in its box. the figurine features prat with short gray hair, wearing a black t-shirt with white chinese characters, gray pants, and black shoes, holding a tablet with a pie chart on its screen. a small figurine of a brown and gray dog sits to his right. the figurine is mounted on a round black base featuring the words "data & ai" in white text. > > to the left of the figurine is its box, which is white with a large window to display the figurine. the box features prat's name in black text on a gray banner, as well as the accenture logo and a speech bubble with the words "apiercard." the background of the image is a light teal color, providing a clean and modern contrast to the figurine and its box. > > overall, the image presents a unique and personalized figurine that showcases juan manuel prat's expertise in data and ai, as well as his connection to accenture. the use of a funko pop figurine adds a playful touch to the image, while the attention to detail in the design of the figurine itself creates a sense of professionalism and expertise. This dataset offers a simple and accessible way to use this dataset. ### Languages The text is in English, but occasionally text in images in other languages is transcribed. ### Intended Usage Training text-to-image models and other machine learning tasks. ### Data Splits | | train | |--------------------------------------|---------:| | dalle3-llama3.2-11b | 1192572 | ## Dataset Creation ### Producing the Dataset The following prompt was used to produce the captions: ```py prompt = "Please make a long, detailed caption of this image, including the contents of the foreground and background. Describe everything you see and use as many paragraphs as necessary. If any well-known characters or people are present, describe who they are." ``` After producing the caption, they were stripped of prefixes as per [anime-caption-danbooru-2021-sfw-5m-hq](https://huggingface.co/datasets/CaptionEmporium/anime-caption-danbooru-2021-sfw-5m-hq). A pair of medium and short length captions were then produced using [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) with the following prompt: ```py prompt_shorten = lambda img_prompt: f""" You have two tasks: prepare a long paragraph length caption and short caption from a long caption. ## Caption for you to distill The following is the caption to distill. \`\`\` {img_prompt} \`\`\` ## Tasks 1. Distill it into a long single paragraph that sufficiently describes all details of the image. Remove any redundant lines or descriptions. 2. Distill it into a single sentence that is a maximum of 25 words. Remove any redundant lines or descriptions. Please only write the two captions, each on a separate line. """ ``` This task had a failure rate of 2.5% that only produced a single medium caption, so to resolve this all failing prompts went through another captioning step where a short prompt was produced from that. ### Data Instances The new captions and tags are found as the columns `caption_llama32_long`, `caption_llama32_medium`, `caption_llama32_short`, and `wd_tagger_eva02_l`. An example of a row: ```json { "image_name": "1698064832265.jpg", "md5_pil_hash": "766d0c40fb109578b4cdc18bb2ede7ff", "md5_file_hash": "9d3454342c5fd20a3bc3383b43072579", "sha512_hash": "e8f8d808abdb499b6012d411137462048fc322a64aba7bfc35dccdcdb801ae9ae3369a814190032a5d82855e8617854fa750530fe40dc09e9beb57cecf8d5a78", "short_caption": "An oversized Shrek figure stands within a supermarket setting, its face exaggerated with enlarged, red, paint-filled eyes, a toothy grin, and a seat upon a shopping cart.", "long_caption": "This image showcases a large, animated green ogre figure with exaggerated facial features. The ogre has wide, bulging eyes that are red with white sclera, and it has a large, open mouth filled with sharp teeth. The figure appears to be in a store, as evidenced by the shopping cart in front of it and the aisles in the background. The store's interior is bright, with white lighting and a few blurred figures, suggesting motion or the use of a slow shutter speed. The overall mood of the image is playful and surreal, as the ogre's expression and the choice of colors give it a comedic and slightly eerie appearance.", "resolution": "1024x1024", "url": "https://desu-usergeneratedcontent.xyz/g/image/1698/06/1698064832265.jpg", "width": 1024, "height": 1024, "source": "chan_archive", "original_prompt": null, "short_caption2": [ "An oversized Shrek figure stands within a supermarket setting, its face exaggerated with enlarged, red, paint-filled eyes, a toothy grin, and a seat upon a shopping cart." ], "long_caption2": [ "a creatively designed figure resembling the character Shrek. The figure has exaggerated facial features, with large, bulging eyes that are red and appear to be filled with a liquid, possibly paint or blood. The skin of the figure is green, and it has a wide, toothy grin, revealing multiple sharp teeth. The figure is wearing a brown vest and is seated inside a shopping cart. The background suggests that the figure is in a store, with bright lights and shelves visible." ], "caption_llama32_long": "A surreal and unsettling scene, featuring a grotesque, zombie-like version of shrek, the beloved animated ogre, situated in the middle of a walmart store.\n\nshrek's face is distorted, with bulging red eyes, bloodshot and dripping with red paint, and a menacing, toothy grin. his skin is a sickly green hue, and he wears a tattered, brown fur vest over a cream-colored shirt, while his hands grasp a blue-handled shopping cart. notably, the shrek figure is surrounded by a burst of light, suggesting motion and movement.\n\nin the background, the walmart store's fluorescent lights and white ceiling are visible, with other shoppers riding the store's circular conveyor belt. the overall atmosphere of the image is one of chaos and disturbance, with the grotesque shrek figure disrupting the mundane and ordinary setting of the walmart store.", "caption_llama32_medium": "A surreal and unsettling scene features a grotesque, zombie-like Shrek in a Walmart store, surrounded by fluorescent lights and other shoppers on a conveyor belt. Shrek's distorted face has bulging red eyes, a menacing grin, and sickly green skin, wearing a tattered fur vest and grasping a blue-handled shopping cart. The image conveys chaos and disturbance as the monstrous figure disrupts the mundane setting.", "caption_llama32_short": "A grotesque, zombie-like Shrek disrupts the mundane Walmart store atmosphere with his distorted face and menacing presence amidst shoppers on a conveyor belt.", "wd_tagger_eva02_l": "{\"ratings\":{\"general\":0.70572,\"sensitive\":0.30799,\"questionable\":0.00991,\"explicit\":0.00546},\"character\":{},\"general\":{\"green_skin\":0.95689,\"colored_skin\":0.91007,\"teeth\":0.88029,\"horror_(theme)\":0.84293,\"male_focus\":0.83938,\"1boy\":0.82954,\"realistic\":0.783,\"open_mouth\":0.77577,\"blood\":0.77054,\"solo\":0.74826,\"blood_on_clothes\":0.61918,\"zombie\":0.54889,\"blurry\":0.52864,\"shopping_cart\":0.51149,\"black_eyes\":0.50906,\"smile\":0.48647,\"bald\":0.44188,\"shirt\":0.42842,\"white_shirt\":0.3891,\"holding\":0.36722,\"motion_blur\":0.36401}}" } ``` ### Loading the dataset with Install the PIL plug-in for JPEGXL (`pip install pillow pillow-jxl-plugin`) and the package for webdataset (`pip install webdataset`). ```py import io import json import pillow_jxl import webdataset as wds from PIL import Image WDS_URLS = "https://huggingface.co/datasets/CaptionEmporium/dalle3-llama3.2-11b/resolve/main/data/wds/{000000..000137}.tar" ds = wds.WebDataset(WDS_URLS) for row in ds: row_json = json.loads(row["json"]) image_pil = Image.open(io.BytesIO(row["jxl"])) ``` ### Discussion of Biases It is biased to the content of the curated dataset along with the training data of LLaMA 3.2 and the tagger. ### Known Limitations - Captions are not manually verified for correctness, so there may be errors. ## Additional Information ### Dataset Curators Caption Emporium, [ProGamerGov](https://huggingface.co/datasets/ProGamerGov) ### Licensing Information The dataset is available under the [Creative Commons ShareAlike (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/legalcode.en). ### Citation Information ``` @misc{dalle3-llama3.2-11b, author = { Caption Emporium }, title = {dalle3-llama3.2-11b}, year = {2024}, publisher = {Huggingface}, journal = {Huggingface repository}, howpublished = {\url{https://huggingface.co/datasets/CaptionEmporium/dalle3-llama3.2-11b}}, } ```

RyanYr/self-reflect_mini8Bit-t0_mistlarge-t12_om2-6_binlabel

RyanYr

--- dataset_info: features: - name: problem dtype: string - name: generated_solution dtype: string - name: answer dtype: string - name: problem_source dtype: string - name: response@0 sequence: string - name: response@1 sequence: string - name: response@2 sequence: string - name: response@0_ans sequence: string - name: response@0_correctness sequence: bool - name: response@2_ans sequence: string - name: response@2_correctness sequence: bool splits: - name: train num_bytes: 289952192 num_examples: 50000 download_size: 125970107 dataset_size: 289952192 configs: - config_name: default data_files: - split: train path: data/train-* ---

KaranCirusbug/guanaco-llama2-1k

KaranCirusbug

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1654448 num_examples: 1000 download_size: 966692 dataset_size: 1654448 configs: - config_name: default data_files: - split: train path: data/train-* ---

chiyuanhsiao/Magpie_rank1_chunk8_interleaf

chiyuanhsiao

--- dataset_info: features: - name: uuid dtype: string - name: model dtype: string - name: gen_input_config struct: - name: temperature dtype: float64 - name: top_p dtype: float64 - name: input dtype: string - name: output dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: task_category dtype: string - name: difficulty dtype: string - name: intent dtype: string - name: knowledge dtype: string - name: input_quality dtype: string - name: quality_explanation dtype: string - name: llama_guard_2 dtype: string - name: reward_model dtype: string - name: instruct_reward dtype: float64 - name: base_output dtype: string - name: base_reward dtype: float64 - name: reward_difference dtype: float64 - name: min_neighbor_distance dtype: float64 - name: repeat_count dtype: int64 - name: min_similar_uuid dtype: string - name: input_length dtype: int64 - name: output_length dtype: int64 - name: input_speech dtype: audio - name: output_speech dtype: audio - name: output_speech_cmu-arctic-xvectors_7306 dtype: audio - name: input_unit sequence: int64 - name: output_unit sequence: int64 - name: output_unit_7306 sequence: int64 - name: output_7306_interleaf dtype: string - name: output_pseudo dtype: string - name: input_pseudo dtype: string splits: - name: train num_bytes: 11857625455.375 num_examples: 10023 download_size: 11586124879 dataset_size: 11857625455.375 configs: - config_name: default data_files: - split: train path: data/train-* ---

recursal/OKReddit-Visionary

recursal

--- size_categories: - 10K<n<100K pretty_name: OKReddit Visionary task_categories: - question-answering - image-to-text source_datasets: - original language: - en --- <div> <a href="https://soundcloud.com/lemmino/biosignature"><img src="https://cdn-uploads.huggingface.co/production/uploads/633e85093a17ab61de8d9073/jh7lskqN9TnF53HmKnFlh.png" title="&quot;We've switched style models from 1.5 to SDXL! Yay! And yes, it's a Style lora once more.&quot;" style="margin-left:auto;margin-right:auto"></a> </div> # Dataset Summary OKReddit Visionary is a collection of **50 GiB** (~74K pairs) of image Question & Answers. This dataset has been prepared for research or archival purposes. - **Curated by:** KaraKaraWitch - **Funded by:** Recursal.ai - **Shared by:** KaraKaraWitch - **Special Thanks:** [harrison](https://huggingface.co/harrisonvanderbyl) (Suggestion) - **Language(s) (NLP):** Mainly English. - **License:** Refer to [Licensing Information](#licensing-information) for data license. ### Dataset Sources - **Source Data:** [Academic Torrents](https://academictorrents.com/details/9c263fc85366c1ef8f5bb9da0203f4c8c8db75f4) by (stuck_in_the_matrix, Watchful1, RaiderBDev & pushshift folks.) ## Supported Tasks and Leaderboards The dataset may be used for a variety of natural language processing (NLP) tasks including: - Visual Questioning: Dataset contains question and answer pairs - Text to Image (and vice versa). ## Languages All the questions and answers should be in english at this size point. ## Dataset Structure ### Data Instances The dataset can be loaded with webdataset. Do note that there are multiple extensions to check: `jpg`, `jpeg` or `png`. They have not been reconverted to preserve the original file from reddit. ```py import webdataset as wds # After concatting, you may use the file like a regular dataset. # The dataset is compatible with WebDataset format. Example... tar_file = "PackedTar.tar" hf_dataset = wds.WebDataset(str(tar_root)).decode("pil") ``` # Dataset Creation ## Curation Rationale Some subreddits are more often than not, Q&A subreddits. Where the submission author asks a question (+An Image), and recieve responses back. ### Subreddits Picked Following a suggestion from harrison, I've selected the following subreddits for this dataset: - PeterExplainsTheJoke - whatisthisanimal - whatisthisbug - whatisthiscar - whatisthisthing Some subreddits were not present in the base OKReddit-RC3 (/r/PeterExplainsTheJoke for example) dataset and had to be pulled from an intermediary step. But the same quality metrics where used in the final subreddit filtering. ### Picking good threads After filtering threads, another round of filtering threads by score is done by: 1. Selecting submissions with >7 score. 2. Select replies from said submissions when it's not a bot (`AutoModerator`) and the score is > 5. 3. Scrape all images (Excluding galleries) 4. Pack data into tar format. # Additional Information ## Recursal's Vision > To make AI accessible to everyone, regardless of language, or economical status This is the collective goal of the `RWKV Open Source foundation` and `Recursal AI`, the commercial entity who backs it. We believe that AI should not be controlled by a select few individual organization. And that it should be made accessible regardless if you are rich or poor, or a native speaker of english. ### About RWKV RWKV is an Open Source, non profit group, under the linux foundation. Focused on developing the RWKV AI architecture, in accordence to our vision. The RWKV architecture scales efficiently and economically. As an RNN & Transformer hybrid, it is able to provide the performance similar to leading transformer models, while having the compute and energy efficiency of an RNN based architecture. You can find out more about the project, and latest models, at the following - [https://blog.rwkv.com](https://blog.rwkv.com) - [https://wiki.rwkv.com](https://wiki.rwkv.com) ### About Recursal AI Recursal AI, is the commercial entity built to provide support for RWKV model development and users, while providing commercial services via its public cloud, or private-cloud / on-premise offerings. As part of our vision. Our commitment, is to ensure open source development and access to the best foundational AI models and datasets. The following dataset/models provided here, is part of that commitment. You can find out more about recursal AI here - [https://recursal.ai](https://recursal.ai) - [https://blog.recursal.ai](https://blog.recursal.ai) ### Licensing Information Since this dataset is derived from a public crawl of reddit, the original content may be subject to copyright and other licensing terms set by the original site owner and/or the content creators. Additionally, this dataset is for research and archival purposes only. Recursal Waifus (The banner image) are licensed under CC-BY-SA. They do not represent the related websites in any official capacity unless otherwise or announced by the website. You may use them as a banner image. However, you must always link back to the dataset. ### Citation Information If you use this dataset in your research or project, please cite it as follows: ```TeX @dataset{OKRedditVisionary, title = {OKReddit-Visionary}, year = {2024}, publisher = {KaraKaraWitch}, url = {<https://huggingface.co/datasets/recursal/OKReddit-Visionary>} } ``` Additionally, pleace cite the following source bibtex as well. ```TeX @article{, title= {Reddit comments/submissions 2005-06 to 2023-12}, journal= {}, author= {stuck_in_the_matrix, Watchful1, RaiderBDev}, year= {}, url= {}, abstract= {Reddit comments and submissions from 2005-06 to 2023-09 collected by pushshift and u/RaiderBDev. These are zstandard compressed ndjson files. Example python scripts for parsing the data can be found here https://github.com/Watchful1/PushshiftDumps The more recent dumps are collected by u/RaiderBDev and questions can be submitted here https://github.com/ArthurHeitmann/arctic_shift}, keywords= {reddit}, terms= {}, license= {}, superseded= {} } ```

chiyuanhsiao/Magpie_rank3_chunk8_interleaf

chiyuanhsiao

--- dataset_info: features: - name: uuid dtype: string - name: model dtype: string - name: gen_input_config struct: - name: temperature dtype: float64 - name: top_p dtype: float64 - name: input dtype: string - name: output dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: task_category dtype: string - name: difficulty dtype: string - name: intent dtype: string - name: knowledge dtype: string - name: input_quality dtype: string - name: quality_explanation dtype: string - name: llama_guard_2 dtype: string - name: reward_model dtype: string - name: instruct_reward dtype: float64 - name: base_output dtype: string - name: base_reward dtype: float64 - name: reward_difference dtype: float64 - name: min_neighbor_distance dtype: float64 - name: repeat_count dtype: int64 - name: min_similar_uuid dtype: string - name: input_length dtype: int64 - name: output_length dtype: int64 - name: input_speech dtype: audio - name: output_speech dtype: audio - name: output_speech_cmu-arctic-xvectors_7306 dtype: audio - name: input_unit sequence: int64 - name: output_unit sequence: int64 - name: output_unit_7306 sequence: int64 - name: output_7306_interleaf dtype: string - name: output_pseudo dtype: string - name: input_pseudo dtype: string splits: - name: train num_bytes: 11784967219.625 num_examples: 10017 download_size: 11513571501 dataset_size: 11784967219.625 configs: - config_name: default data_files: - split: train path: data/train-* ---

Tensorists/SD2_images

Tensorists

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': Airplane '1': Automobile '2': Bird '3': Cat '4': Deer '5': Dog '6': Frog '7': Horse '8': Ship '9': Truck splits: - name: train num_bytes: 1360784016.124 num_examples: 2734 download_size: 1544616944 dataset_size: 1360784016.124 configs: - config_name: default data_files: - split: train path: data/train-* ---

lightblue/response-dataset-plus-qwen-judged

lightblue

--- dataset_info: features: - name: prompt dtype: string - name: language dtype: string - name: dataset_name dtype: string - name: translation struct: - name: error dtype: string - name: text dtype: string - name: response struct: - name: error dtype: string - name: text dtype: string - name: instruction dtype: string - name: output dtype: string - name: row_id dtype: int64 - name: qwen_response dtype: string - name: qwen_stop_reason dtype: string - name: judgement_gpt_x_qwe_y struct: - name: judgement dtype: string - name: output_logprobs sequence: float64 - name: output_logprobs_top5 list: list: - name: logprob dtype: float64 - name: tok dtype: string - name: judgement_qwe_x_gpt_y struct: - name: judgement dtype: string - name: output_logprobs sequence: float64 - name: output_logprobs_top5 list: list: - name: logprob dtype: float64 - name: tok dtype: string - name: has_unanimous_winner dtype: bool - name: unanimous_winner dtype: string - name: pos dtype: string - name: neg dtype: string splits: - name: train num_bytes: 462315005 num_examples: 50782 download_size: 215128813 dataset_size: 462315005 configs: - config_name: default data_files: - split: train path: data/train-* ---

marcelsun/wos_hierarchical_multi_label_text_classification

marcelsun

--- license: cc-by-4.0 task_categories: - text-classification language: - en tags: - NLP - LLM - hierarchical - multi-label - classification pretty_name: WOS Hierarchical Multi-Label Text Classification size_categories: - 10K<n<100K --- Introduced by du Toit and Dunaiski (2024) [Introducing Three New Benchmark Datasets for Hierarchical Text Classification](https://arxiv.org/abs/2411.19119). The WOS Hierarchical Text Classification are three dataset variants created from Web of Science (WOS) title and abstract data categorised into a hierarchical, multi-label class structure. The aim of the sampling and filtering methodology used was to create well-balanced class distributions (at chosen hierarchical levels). Furthermore, the WOS_JTF variant was also created with the aim to only contain publication data such that their class assignments results is classes instances that semantically more similar. The three dataset variants have the following properties: 1. WOS_JT comprises 43,366 total samples (train=30356, dev=6505, test=6505) and only uses the journal-based classifications as labels. 2. WOS_CT comprises 65,200 total samples (train=45640, dev=9780, test=9780) and only uses citation-based classifications as labels. 3. WOS_JTF comprises 42,926 total samples (train=30048, dev=6439, test=6439) and uses a filtered set of papers based on journal and citation classification. Dataset details: *.json: - concatenated title and abstract mapped to a list each associated class label. depth2label.pt: dictionary where: - key = depth of classification hierarchy. - value = list of classes associated with depth. path_list.pt: - list of tuples for every edge between classes in the hierarchical classification. This specifies the acyclic graph. slot.pt: dictionary where: - key = label_id of parent class. - value = label_ids of children classes. value2slot.pt: dictionary where: - key = label_id. - value = label_id of parent class. value_dict.pt: dictionary where: - key = label_id. - value = string representation of class.

infinite-dataset-hub/EthicalEatingEmotions

infinite-dataset-hub

--- license: mit tags: - infinite-dataset-hub - synthetic --- # EthicalEatingEmotions tags: vegan, psychology, dietary choices _Note: This is an AI-generated dataset so its content may be inaccurate or false_ **Dataset Description:** The 'EthicalEatingEmotions' dataset contains anonymized user-generated content from various platforms discussing the emotional aspects of adopting a vegan diet. The data is gathered from social media posts, blog comments, and forum discussions. Each entry includes the original text, a sentiment analysis score, and a label reflecting the user's emotional stance towards veganism (e.g., positive, neutral, negative). **CSV Content Preview:** ``` text,sentiment_score,labels "I've been vegan for 5 years now and I feel healthier than ever!",0.9,"positive" "Trying to be vegan has been challenging but worth it for the planet.",0.7,"positive" "The taste of vegan food can sometimes be off-putting, but I'm learning.",0.6,"neutral" "I'm disappointed by the lack of vegan options at my favorite restaurant.",0.3,"negative" "Veganism isn't for everyone, and that's okay. I respect people's choices.",0.5,"neutral" ``` **Source of the data:** The dataset was generated using the [Infinite Dataset Hub](https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub) and microsoft/Phi-3-mini-4k-instruct using the query 'vegan': - **Dataset Generation Page**: https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub?q=vegan&dataset=EthicalEatingEmotions&tags=vegan,+psychology,+dietary+choices - **Model**: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct - **More Datasets**: https://huggingface.co/datasets?other=infinite-dataset-hub

infinite-dataset-hub/VeganProductPreferences

infinite-dataset-hub

--- license: mit tags: - infinite-dataset-hub - synthetic --- # VeganProductPreferences tags: Consumer Behavior, Product Recommendation, Veganism _Note: This is an AI-generated dataset so its content may be inaccurate or false_ **Dataset Description:** The 'VeganProductPreferences' dataset captures consumer preferences for various vegan products. It includes survey data collected from individuals who follow a vegan lifestyle, with a focus on the type of products they prefer, their reasons for choosing those products, and their willingness to recommend them to others. **CSV Content Preview:** ```csv id,product_name,product_category,user_age,user_gender,user_location,preference_rating,reason_for_preference,likelihood_of_recommendation,labels 1,Vegan Chocolate Bar,"Chocolates",28,"Female","San Francisco, CA",4.5,"Taste and ethical reasons",4.2,"Ethical consumerism, Plant-based desserts" 2,Organic Soy Milk,"Dairy Alternatives",34,"Male","New York, NY",4.8,"Health benefits and environmental concerns",4.7,"Health-conscious, Environmentalism" 3,Vegan Leather Wallet,"Accessories",23,"Non-binary","Austin, TX",3.9,"Lifestyle choice, Durability",3.8,"Sustainable fashion, Lifestyle choices" 4,Plant-Based Burgers,"Food",29,"Female","Chicago, IL",4.2,"Taste and dietary preferences",4.1,"Culinary adventures, Plant-based diet" 5,Cruelty-Free Beauty Products,"Beauty",37,"Female","Los Angeles, CA",4.7,"Ethical reasons, Skin health",4.6,"Animal rights, Skincare" ``` **Source of the data:** The dataset was generated using the [Infinite Dataset Hub](https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub) and microsoft/Phi-3-mini-4k-instruct using the query 'veganism': - **Dataset Generation Page**: https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub?q=veganism&dataset=VeganProductPreferences&tags=Consumer+Behavior,+Product+Recommendation,+Veganism - **Model**: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct - **More Datasets**: https://huggingface.co/datasets?other=infinite-dataset-hub

infinite-dataset-hub/AnimalAdvocacyTracker

infinite-dataset-hub

--- license: mit tags: - infinite-dataset-hub - synthetic --- # AnimalAdvocacyTracker tags: social_media, sentiment, impact _Note: This is an AI-generated dataset so its content may be inaccurate or false_ **Dataset Description:** The 'AnimalAdvocacyTracker' dataset contains tweets related to animal rights activism, categorized based on sentiment and impact. Each row represents a tweet with associated metadata, and the dataset includes labels to classify the overall sentiment and potential impact of the message. **CSV Content Preview:** ```csv tweet_id,text,created_at,label 1,"Animal testing is cruel, we must stand against it. #AnimalRights #SaveLives",2023-03-21T10:00:00Z,PositiveImpact 2,"Watching documentaries on animal farming is heartbreaking. #AnimalWelfare #StopFactoryFarming",2023-03-21T11:30:00Z,NegativeSentiment 3,"The ban on animal circuses is finally here! Good news for our furry friends! #AnimalFreedom #BanCircuses",2023-03-21T12:45:00Z,PositiveImpact 4,"Huge loss today as our beloved dolphin sanctuary was hit by a storm. #AnimalCare #Loss",2023-03-21T14:15:00Z,NeutralSentiment 5,"Every time I see a video of factory-farmed animals, it breaks my heart. #AnimalCruelty #EndFactoryFarming",2023-03-21T15:05:00Z,NegativeSentiment ``` **Source of the data:** The dataset was generated using the [Infinite Dataset Hub](https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub) and microsoft/Phi-3-mini-4k-instruct using the query 'animal rights activism': - **Dataset Generation Page**: https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub?q=animal+rights+activism&dataset=AnimalAdvocacyTracker&tags=social_media,+sentiment,+impact - **Model**: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct - **More Datasets**: https://huggingface.co/datasets?other=infinite-dataset-hub

infinite-dataset-hub/AnimalAlliesAlgorithm

infinite-dataset-hub

--- license: mit tags: - infinite-dataset-hub - synthetic --- # AnimalAlliesAlgorithm tags: vegan advocacy, efficacy prediction, animal rights _Note: This is an AI-generated dataset so its content may be inaccurate or false_ **Dataset Description:** The 'AnimalAlliesAlgorithm' dataset is a curated collection of textual data aimed at understanding the various dimensions of vegan advocacy. It encompasses a range of content, including advocacy campaigns, personal testimonies, scientific studies, and policy discussions related to animal rights and veganism. Each entry has been carefully labeled to reflect its primary focus within the realm of vegan advocacy, from awareness-raising to promoting policy change. **CSV Content Preview:** ``` id,text,label 001,"The Vegan Society's recent campaign highlighted the environmental benefits of a plant-based diet.",Awareness 002,"An interview with a renowned vegan chef discussing the moral imperative of veganism.",PersonalTestimony 003,"A scientific study on the impact of factory farming on local wildlife populations.",ImpactStudy 004,"A blog post from a legislator advocating for stricter animal welfare laws.",PolicyChange 005,"Vegan bloggers collaborating to share recipes and tips for a cruelty-free lifestyle.",CommunitySupport ``` **Source of the data:** The dataset was generated using the [Infinite Dataset Hub](https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub) and microsoft/Phi-3-mini-4k-instruct using the query 'vegan advocacy': - **Dataset Generation Page**: https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub?q=vegan+advocacy&dataset=AnimalAlliesAlgorithm&tags=vegan+advocacy,+efficacy+prediction,+animal+rights - **Model**: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct - **More Datasets**: https://huggingface.co/datasets?other=infinite-dataset-hub

infinite-dataset-hub/PlantProtein

infinite-dataset-hub

--- license: mit tags: - infinite-dataset-hub - synthetic --- # PlantProtein tags: vegan, food, nutrition _Note: This is an AI-generated dataset so its content may be inaccurate or false_ **Dataset Description:** The 'PlantProtein' dataset is a curated collection of various plant-based protein sources that are suitable for a vegan diet. Each entry includes a description of the protein source, its primary nutritional benefits, and common food items it can be found in or used to enhance. The dataset is meant to help vegans find diverse protein options to ensure a balanced diet. **CSV Content Preview:** ``` "Protein Source","Description","Nutritional Benefits","Common Uses","Label" "Lentils","Small, green legume with high protein content.","Rich in iron, folate, and potassium.","Used in soups, stews, and salads.","Complete Protein" "Chickpeas","Bold-flavored bean with a good amount of protein.","Contains protein, fiber, and minerals like magnesium and zinc.","Found in hummus, salads, and curries.","Legume Protein" "Quinoa","Sprouted seed with a balanced amino acid profile.","High in fiber, magnesium, B vitamins, iron, potassium, calcium, phosphorus, vitamin E, and various beneficial antioxidants.","Can be eaten as a grain or used in salads and vegan meat substitutes.","Complete Protein" "Hemp Seeds","Seeds from the hemp plant, containing all nine essential amino acids.","Rich in omega-3 and omega-6 fatty acids, protein, and fiber.","Can be added to smoothies, oatmeal, and salads.","Complete Protein" "Spirulina","Blue-green algae, packed with protein, vitamins B1, B2, B3, copper, and manganese.","High in protein and antioxidants.","Available as a supplement or added to juices and smoothies.","Microalgae Protein" ``` **Source of the data:** The dataset was generated using the [Infinite Dataset Hub](https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub) and microsoft/Phi-3-mini-4k-instruct using the query 'vegan': - **Dataset Generation Page**: https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub?q=vegan&dataset=PlantProtein&tags=vegan,+food,+nutrition - **Model**: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct - **More Datasets**: https://huggingface.co/datasets?other=infinite-dataset-hub

infinite-dataset-hub/EthicalEatingTrends

infinite-dataset-hub

--- license: mit tags: - infinite-dataset-hub - synthetic --- # EthicalEatingTrends tags: Social Media Influence, Consumer Patterns, Veganism _Note: This is an AI-generated dataset so its content may be inaccurate or false_ **Dataset Description:** The 'EthicalEatingTrends' dataset aims to analyze the influence of social media on consumer patterns, specifically focusing on veganism. It includes data collected from various social media platforms and public forums to understand how ethical eating trends spread and evolve over time. The dataset features user comments, articles, and posts discussing veganism, consumer behavior, and the impact of ethical eating on the environment and society. **CSV Content Preview:** ```csv "label","user_id","comment","date","source" "positive","user123","Loving the new plant-based menu at Green Sprout Café! #VeganLife #SustainableEating",2023-01-15,"SocialMedia" "neutral","user456","I'm not sure about giving up cheese, but I'm trying it out. #VeganExperiment",2023-01-20,"SocialMedia" "negative","user789","Vegan options are usually overpriced. Why can't regular food be sustainable?",2023-01-12,"Forum" "positive","user321","Appreciating how the city's vegan festival is spreading awareness about ethical eating! #EcoFriendly",2023-02-05,"NewsArticle" "neutral","user654","Eating vegan can be challenging during holidays. Need more support from restaurants.",2023-01-25,"Blog" ``` This CSV snippet provides a preview of how the dataset may be structured, including different labels indicating the sentiment or stance towards veganism, as well as other relevant details that can be used for analysis. **Source of the data:** The dataset was generated using the [Infinite Dataset Hub](https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub) and microsoft/Phi-3-mini-4k-instruct using the query 'veganism': - **Dataset Generation Page**: https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub?q=veganism&dataset=EthicalEatingTrends&tags=Social+Media+Influence,+Consumer+Patterns,+Veganism - **Model**: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct - **More Datasets**: https://huggingface.co/datasets?other=infinite-dataset-hub

EuclidAI/Geoperception

EuclidAI

--- license: apache-2.0 task_categories: - question-answering - visual-question-answering tags: - multi-modal-qa - math-qa - figure-qa - geometry-qa - math-word-problem - vqa - geometry-reasoning - numeric-common-sense - scientific-reasoning - logical-reasoning - geometry-diagram - synthetic-scene - scientific-figure - function-plot - abstract-scene - mathematics pretty_name: Geoperception size_categories: - 10K<n<100K language: - en --- # Dataset Card for Geoperception A Benchmark for Low-level Geometric Perception ## Dataset Details ### Dataset Description Geoperception is sourced from the Geometry-3K corpus, which offers precise logical forms for geometric diagrams, compiled from popular high-school textbooks. ### Dataset Sources - **Repository:** [https://github.com/euclid-multimodal/Euclid-Model] - **Paper:** [More Information Needed] - **Demo:** [More Information Needed] ## Uses Evaluation of multimodal LLM's ability of low-level visual perception in 2D geometry domain. ## Dataset Structure ### Fields [More Information Needed] ## Citation **BibTeX:** [More Information Needed]

JoeLeelyf/OVBench

JoeLeelyf

--- license: cc-by-nc-sa-4.0 ---

hirundo-io/unlearning-500-persons-multiple-details

hirundo-io

--- license: bsd-2-clause ---

mengcy/LAION-SG

mengcy

--- license: mit task_categories: - image-to-text language: - en --- # Dataset Card for LAION-SG <!-- Provide a quick summary of the dataset. --> <!-- 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). --> LAION-SG is a large-scale dataset with high-quality structural annotations of scene graphs (SG), which precisely describe attributes and relationships of multiple objects, effectively representing the semantic structure in complex scenes. ## Dataset Details <!-- ### Dataset Description --> <!-- Provide a longer summary of what this dataset is. --> <!-- - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] --> - **Language(s) :** All of annotations use English as primary language. - **License:** MIT License. <!-- ### Dataset Sources [optional] --> <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper:** [More Information Needed] <!-- - **Demo [optional]:** [More Information Needed] --> ### Data Splits - A total of 540,005 SG-image pairs annotated with objects, attributes, and relationships. - 480,005 samples for taining - 10,000 samples for validation - 50,000 samples for test ## Uses <!--只提供json里面的url，不提供我们自己的images --> <!-- Address questions around how the dataset is intended to be used. --> <!-- ### Direct Use --> <!-- This section describes suitable use cases for the dataset. --> <!-- [More Information Needed] --> <!-- ### Out-of-Scope Use --> <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> <!-- [More Information Needed] --> ## Dataset Structure ### Data Instances An example is as follows. ``` { "img_id": "482027", "name": "8040361340228797010.jpg", "caption_ori": "Yosemite Falls reflection in Merced River, Yosemite, California Poster by Tom Norring / Danita Delimont for $102.50 CAD", "score": "6.669650077819824", "url": "http://images.artgalore.ca/artgalore_images/PDD/US05TNO0060.jpg", "items": [ { "item_id": 0, "label": "mountains", "attributes": [ "rocky", "tall" ], "global_item_id": 3201429 }, { "item_id": 1, "label": "trees", "attributes": [ "leafless", "slender" ], "global_item_id": 3201430 }, { "item_id": 2, "label": "trees", "attributes": [ "leafless", "slender" ], "global_item_id": 3201431 }, { "item_id": 3, "label": "snow", "attributes": [ "white", "cold" ], "global_item_id": 3201432 }, { "item_id": 4, "label": "river", "attributes": [ "reflective", "calm" ], "global_item_id": 3201433 } ], "relations": [ { "triple_id": 0, "item1": 3, "relation": "adjacent to", "item2": 4, "global_relation_id": 2118313 }, { "triple_id": 1, "item1": 1, "relation": "growing near", "item2": 4, "global_relation_id": 2118314 }, { "triple_id": 2, "item1": 2, "relation": "growing near", "item2": 4, "global_relation_id": 2118315 } ] }, ``` ### Data Fields - ```"img_id"```: Unique numeric ID of the image. - ```"name"```: Name of source image. - ```"caption_ori"```: Original caption of the image in LAION-Aesthetics. - ```"score"```: Aesthetic score of the image. - ```"url"```: URL of source image. - ```"items"```: List of objects recognized in the image. - ```"item_id"```: Unique numeric ID of the object in current image. - ```"label"```: Label of the object. - ```"attributes"```: List of attributes of the object. - ```"global_item_id"```: Unique numeric ID of the object in all images in LAION-SG. - ```"relations"```: List of relations recognized in the image. - ```"triple_id"```: Unique numeric ID of the relation in current image. - ```"item1"```: The item_id of the subject in scene graph triplet <subject, relation, object>. - ```"relation"```: The relation between the subject and the object in scene graph triplet <subject, relation, object>. - ```"item2"```: The item_id of the object in scene graph triplet <subject, relation, object>. - ```"global_relation_id"```: Unique numeric ID of the relation in all images in LAION-SG. <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> <!-- [More Information Needed] --> ## Dataset Creation ### Source Data All images are from the LAION-Aestheics V2 (6.5+) dataset. <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing  From the paper: > Our LAION-SG dataset is built on high-quality images in LAION-Aesthetic V2 (6.5+) with automated annotation performed using GPT-4o. LAION-Aesthetics V2 (6.5+) is a subset of LAION-5B, comprising 625,000 image-text pairs with predicted aesthetic scores over 6.5, curated using the LAION-Aesthetics Predictor V2 model. > During our construction, only 540,005 images are available in the dataset due to copyright or other issues. > > Through prompt engineering, we devised a set of specific requirements for scene graph annotations to ensure comprehensiveness, systematic structure, and precision in the annotation results. The above figure illustrates the detailed construction pipeline of LAION-SG. > Each component plays a crucial role in achieving high-quality automated annotation. > > First, as scene graphs typically contain multiple objects and their relations, the prompt requires “identification of as many objects, attributes, and their relations within the image as possible”. > This design encourages that all objects and interactions in a scene are annotated. > Each object is assigned a unique ID, even for multiple objects of the same type, ensuring that the entirety of the scene's structure and hierarchy is accurately represented. > > Second, the attribute section mandates that each object must have at least one abstract adjective attribute, while avoiding the use of other objects as attributes. This design is especially important in complex scenes as it helps differentiate objects' appearance, state, and characteristics from the background and other elements, maintaining consistency and clarity in annotations. > By avoiding the confusion between specific objects and abstract attributes, the annotations become more interpretable and generalizable. > > In the relation section, we specify the use of concrete verbs to describe relations between objects rather than relying solely on spatial orientation. > This is because relations are often more critical in scene graphs than mere spatial information. > By using precise verbs like “standing on” or “holding”, we capture dynamic interactions within the scene, which is essential for complex scene generation. > > Leveraging these prompts with the multimodal large language model GPT-4o, we generate annotations representing scene graphs. > Our annotation is expect to achieve accuracy for every object, attribute, and relationship, thoroughly covering each detail in the scene and providing robust data support for subsequent compositional image generation tasks. <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> <!-- [More Information Needed] --> <!-- #### Who are the source data producers? --> <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> <!-- [More Information Needed] --> <!-- ### Annotations --> <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> <!-- #### Annotation process --> <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> <!-- [More Information Needed] --> <!-- #### Who are the annotators? --> <!-- This section describes the people or systems who created the annotations. --> <!-- [More Information Needed] --> <!-- #### Personal and Sensitive Information --> <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> <!-- [More Information Needed] --> <!-- ## Bias, Risks, and Limitations --> <!-- This section is meant to convey both technical and sociotechnical limitations. --> <!-- [More Information Needed] --> <!-- ### Recommendations --> <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> <!-- Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. --> ## Citation <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] <!-- **APA:** [More Information Needed] ## Glossary [optional] If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed] -->

taufiqsyed/salami_truncsplit_finetune__enriched

taufiqsyed

--- dataset_info: features: - name: audio dtype: audio - name: song_id dtype: string - name: structure dtype: string - name: start_time dtype: float64 - name: end_time dtype: float64 - name: tempos dtype: string - name: keys dtype: string - name: instruments dtype: string - name: genres dtype: string - name: moods dtype: string - name: metadata dtype: string splits: - name: train num_bytes: 2199008170.0 num_examples: 831 - name: eval num_bytes: 84679315.0 num_examples: 32 download_size: 2235466097 dataset_size: 2283687485.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: eval path: data/eval-* ---

FlippyDora/dpo_pair_data

FlippyDora

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: chosen_score dtype: float64 - name: rejected_score dtype: float64 - name: step dtype: int64 - name: num_steps dtype: int64 splits: - name: train num_bytes: 321550584 num_examples: 148945 download_size: 40008369 dataset_size: 321550584 configs: - config_name: default data_files: - split: train path: data/train-* ---

EdsonKanou/new_sql_training

EdsonKanou

--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 30110 num_examples: 78 download_size: 2933 dataset_size: 30110 configs: - config_name: default data_files: - split: train path: data/train-* ---

WayneMao/RoboMatrix

WayneMao

--- license: mit ---

denkCF/UsersCodeforcesSubmissionsEnd2024

denkCF

--- datasets: - name: usersCodeforcesSubmissionsEnd2024 size: 200MB task_categories: - other languages: - en licenses: - cc-by-4.0 tags: - codeforces - competitive-programming - submissions pretty_name: Codeforces Users Submissions (End of 2024) description: > This dataset contains anonymized submission data of ≈15,000 Codeforces users, spanning from the inception of Codeforces to the end of November 2024. download_size: 100MB dataset_size: 1.2GB license: cc-by-4.0 language: - en tags: - codeforces - submissions size_categories: - 10M<n<100M --- # Codeforces Users Submissions Dataset (End of 2024) This project provides the usersCodeforcesSubmissionsEnd2024.csv file, containing anonymized submission data of approximately 15,000 active Codeforces users. The dataset includes all submissions from the inception of Codeforces up to the end of November 2024. It is designed to support AI and data-driven projects. (All data was collected using the open Codeforces API) ## Dataset Overview The file contains **17,607,999 rows** with the following columns: - **`handle`**: An anonymized and shuffled user nickname (e.g., `user{i}`). - **`rating_at_submission`**: User's rating at the time of submission. - **`problem_rating`**: Problem difficulty rating. - **`id_of_submission_task`**: Unique problem identifier on Codeforces. - **`verdict`**: Result of the submission (e.g., `OK`, `WRONG_ANSWER`). - **`time`**: Time of submission (in seconds since the Unix epoch). ## Purpose of the Dataset 1. **AI Development**: This dataset can be used to create intelligent systems to enhance user learning on Codeforces. _(Example: The author of this dataset is currently working on the project "Codeforces User Analysis System for Generating Individual Training Recommendations," which aims to recommend tasks to users based on their weaknesses.)_ 2. **Time Saving**: Collecting such data manually can be time-consuming (it took ≈7 hours for this dataset). By providing it in a ready-to-use format, we aim to save your time and effort. 3. **Reduce Server Load**: This dataset minimizes repetitive data scraping, thereby reducing the load on Codeforces servers. ## License This dataset is shared under the [CC BY 4.0 License](https://creativecommons.org/licenses/by/4.0/). You are free to use it for your projects with proper attribution. ## How to Use 1. Download the `usersCodeforcesSubmissionsEnd2024.zip` file. 2. Unzip the file to access the `usersCodeforcesSubmissionsEnd2024.csv` dataset: - On Linux/macOS: Use the `unzip` command in the terminal. - On Windows: Right-click the file and select "Extract All." 3. Load the CSV file into your favorite data analysis tool: ```python import pandas as pd df = pd.read_csv("usersCodeforcesSubmissionsEnd2024.csv") # Good luck with your projects :)

procit002/STT_2.0.1_generatedByRikesh_EnhancedData

procit002

--- dataset_info: features: - name: gender dtype: string - name: accent dtype: string - name: speaker_id dtype: int64 - name: speaker_name dtype: string - name: text dtype: string - name: normalized_text dtype: string - name: audio dtype: audio splits: - name: train num_bytes: 22401105547.24 num_examples: 70252 - name: test num_bytes: 2800032279.34 num_examples: 8782 - name: validation num_bytes: 2777406854.47 num_examples: 8781 download_size: 26241693521 dataset_size: 27978544681.050003 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---

sdiazlor/my-distiset-3b08370d

sdiazlor

--- size_categories: n<1K dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': neutral '1': negative '2': positive splits: - name: train num_bytes: 370 num_examples: 1 download_size: 3801 dataset_size: 370 configs: - config_name: default data_files: - split: train path: data/train-* tags: - synthetic - distilabel - rlaif - datacraft --- <p align="left"> <a href="https://github.com/argilla-io/distilabel"> <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/> </a> </p> # Dataset Card for my-distiset-3b08370d This dataset has been created with [distilabel](https://distilabel.argilla.io/). ## Dataset Summary This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI: ```console distilabel pipeline run --config "https://huggingface.co/datasets/sdiazlor/my-distiset-3b08370d/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/sdiazlor/my-distiset-3b08370d/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "label": 0, "text": "I recently purchased the new laptop and although it has a lot of great features, I\u0027m not entirely satisfied with the battery life. It lasts about 6 hours, which is less than the 8 hours advertised. The keyboard is also a bit too small for my liking. However, the display is fantastic and the performance is fast. Overall, it\u0027s a good product but not perfect." } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-3b08370d", "default") ``` Or simply as it follows, since there's only one configuration and is named `default`: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-3b08370d") ``` </details>

ocisd4/language_detection

ocisd4

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 65524866.82 num_examples: 196 download_size: 60589113 dataset_size: 65524866.82 configs: - config_name: default data_files: - split: train path: data/train-* ---

sdiazlor/my-distiset-29371374

sdiazlor

--- size_categories: n<1K dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': negative '1': neutral '2': positive splits: - name: train num_bytes: 543 num_examples: 1 download_size: 4937 dataset_size: 543 configs: - config_name: default data_files: - split: train path: data/train-* tags: - synthetic - distilabel - rlaif - datacraft --- <p align="left"> <a href="https://github.com/argilla-io/distilabel"> <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/> </a> </p> # Dataset Card for my-distiset-29371374 This dataset has been created with [distilabel](https://distilabel.argilla.io/). ## Dataset Summary This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI: ```console distilabel pipeline run --config "https://huggingface.co/datasets/sdiazlor/my-distiset-29371374/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/sdiazlor/my-distiset-29371374/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "label": 0, "text": "The recent changes to the company\u0027s algorithm have resulted in a decrease in the efficiency of the recommendation system, leading to a substantial increase in the time spent by users searching for relevant content. The new system\u0027s reliance on opaque black box models hinders transparency and trust among users, which is critical for the success of the platform. Furthermore, the absence of clear explanations for the recommendations undermines the value proposition of the platform, causing potential users to turn to competitors." } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-29371374", "default") ``` Or simply as it follows, since there's only one configuration and is named `default`: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-29371374") ``` </details>

sdiazlor/my-distiset-0732d793

sdiazlor

--- size_categories: n<1K dataset_info: features: - name: instruction dtype: string - name: generations sequence: string - name: ratings_overall-rating sequence: int64 - name: rationale_for_ratings_overall-rating sequence: string - name: model_name dtype: string splits: - name: train num_bytes: 2659 num_examples: 1 download_size: 15201 dataset_size: 2659 configs: - config_name: default data_files: - split: train path: data/train-* tags: - synthetic - distilabel - rlaif - datacraft --- <p align="left"> <a href="https://github.com/argilla-io/distilabel"> <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/> </a> </p> # Dataset Card for my-distiset-0732d793 This dataset has been created with [distilabel](https://distilabel.argilla.io/). ## Dataset Summary This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI: ```console distilabel pipeline run --config "https://huggingface.co/datasets/sdiazlor/my-distiset-0732d793/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/sdiazlor/my-distiset-0732d793/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "generations": [ " Sure, I\u0027d be happy to help! Here are the RDF triplets for the input sentence:\n\n[AFC Ajax (amateurs), hasGround, Sportpark De Toekomst]\n[Ajax Youth Academy, playsAt, Sportpark De Toekomst]\n\nExplanation:\n\n* AFC Ajax (amateurs) is the subject of the first triplet, and hasGround is the predicate that describes the relationship between AFC Ajax (amateurs) and Sportpark De Toekomst.\n* Ajax Youth Academy is the subject of the second triplet, and playsAt is the predicate that describes the relationship between Ajax Youth Academy and Sportpark De Toekomst.\n\nNote that there may be other possible RDF triplets that could be derived from the input sentence, but the above triplets capture the main relationships present in the sentence.", "[\n [\"AFC Ajax (amateurs)\", \"has ground\", \"Sportpark De Toekomst\"],\n [\"Ajax Youth Academy\", \"plays at\", \"Sportpark De Toekomst\"]\n]" ], "instruction": "You will be given a definition of a task first, then some input of the task.\nThis task is about using the specified sentence and converting the sentence to Resource Description Framework (RDF) triplets of the form (subject, predicate object). The RDF triplets generated must be such that the triplets accurately capture the structure and semantics of the input sentence. The input is a sentence and the output is a list of triplets of the form [subject, predicate, object] that capture the relationships present in the sentence. When a sentence has more than 1 RDF triplet possible, the output must contain all of them.\n\nAFC Ajax (amateurs)\u0027s ground is Sportpark De Toekomst where Ajax Youth Academy also play.\nOutput:", "model_name": "meta-llama/Meta-Llama-3.1-8B-Instruct", "ratings_overall-rating": [ 4, 5 ], "rationale_for_ratings_overall-rating": [ "The output provides accurate RDF triplets that capture the relationships between AFC Ajax (amateurs), Ajax Youth Academy, and Sportpark De Toekomst. Both triplets are relevant and correctly formatted. However, it could be argued that there is an additional possible triplet for Ajax Youth Academy\u0027s relationship with Sportpark De Toekomst, which is \"Ajax Youth Academy, hasGround, Sportpark De Toekomst\" since it\u0027s also mentioned that the \"ground\" is Sportpark De Toekomst. Nevertheless, the output is mostly correct, and the explanation provided clarifies the reasoning behind the chosen triplets.", "The output is accurate, and the RDF triplets are correctly formatted. The two triplets captured accurately represent the relationships between AFC Ajax (amateurs), Ajax Youth Academy, and Sportpark De Toekomst. This output is more concise and directly presents the triplets without extra explanation, which is in line with the instruction\u0027s expectation that the output is a list of triplets." ] } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-0732d793", "default") ``` Or simply as it follows, since there's only one configuration and is named `default`: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-0732d793") ``` </details>

chiyuanhsiao/Magpie_rank3_chunk9_interleaf

chiyuanhsiao

--- dataset_info: features: - name: uuid dtype: string - name: model dtype: string - name: gen_input_config struct: - name: temperature dtype: float64 - name: top_p dtype: float64 - name: input dtype: string - name: output dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: task_category dtype: string - name: difficulty dtype: string - name: intent dtype: string - name: knowledge dtype: string - name: input_quality dtype: string - name: quality_explanation dtype: string - name: llama_guard_2 dtype: string - name: reward_model dtype: string - name: instruct_reward dtype: float64 - name: base_output dtype: string - name: base_reward dtype: float64 - name: reward_difference dtype: float64 - name: min_neighbor_distance dtype: float64 - name: repeat_count dtype: int64 - name: min_similar_uuid dtype: string - name: input_length dtype: int64 - name: output_length dtype: int64 - name: input_speech dtype: audio - name: output_speech dtype: audio - name: output_speech_cmu-arctic-xvectors_7306 dtype: audio - name: input_unit sequence: int64 - name: output_unit sequence: int64 - name: output_unit_7306 sequence: int64 - name: output_7306_interleaf dtype: string - name: output_pseudo dtype: string - name: input_pseudo dtype: string splits: - name: train num_bytes: 11933331070.5 num_examples: 10020 download_size: 11658373162 dataset_size: 11933331070.5 configs: - config_name: default data_files: - split: train path: data/train-* ---

sdiazlor/my-distiset-0732d7

sdiazlor

--- size_categories: n<1K dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: generation dtype: string - name: model_name dtype: string splits: - name: train num_bytes: 87 num_examples: 1 download_size: 2417 dataset_size: 87 configs: - config_name: default data_files: - split: train path: data/train-* tags: - synthetic - distilabel - rlaif - datacraft --- <p align="left"> <a href="https://github.com/argilla-io/distilabel"> <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/> </a> </p> # Dataset Card for my-distiset-0732d7 This dataset has been created with [distilabel](https://distilabel.argilla.io/). ## Dataset Summary This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI: ```console distilabel pipeline run --config "https://huggingface.co/datasets/sdiazlor/my-distiset-0732d7/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/sdiazlor/my-distiset-0732d7/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "generation": "{\"shortness\" : 3}", "model_name": "meta-llama/Meta-Llama-3.1-8B-Instruct", "prompt": "What\u0027s A?", "response": "A letter" } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-0732d7", "default") ``` Or simply as it follows, since there's only one configuration and is named `default`: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-0732d7") ``` </details>

chiyuanhsiao/Magpie_rank1_chunk9_interleaf

chiyuanhsiao

--- dataset_info: features: - name: uuid dtype: string - name: model dtype: string - name: gen_input_config struct: - name: temperature dtype: float64 - name: top_p dtype: float64 - name: input dtype: string - name: output dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: task_category dtype: string - name: difficulty dtype: string - name: intent dtype: string - name: knowledge dtype: string - name: input_quality dtype: string - name: quality_explanation dtype: string - name: llama_guard_2 dtype: string - name: reward_model dtype: string - name: instruct_reward dtype: float64 - name: base_output dtype: string - name: base_reward dtype: float64 - name: reward_difference dtype: float64 - name: min_neighbor_distance dtype: float64 - name: repeat_count dtype: int64 - name: min_similar_uuid dtype: string - name: input_length dtype: int64 - name: output_length dtype: int64 - name: input_speech dtype: audio - name: output_speech dtype: audio - name: output_speech_cmu-arctic-xvectors_7306 dtype: audio - name: input_unit sequence: int64 - name: output_unit sequence: int64 - name: output_unit_7306 sequence: int64 - name: output_7306_interleaf dtype: string - name: output_pseudo dtype: string - name: input_pseudo dtype: string splits: - name: train num_bytes: 11883350233.75 num_examples: 10022 download_size: 11609076135 dataset_size: 11883350233.75 configs: - config_name: default data_files: - split: train path: data/train-* ---

procit002/test_10_dataset

procit002

--- dataset_info: features: - name: speaker_id dtype: string - name: speaker_name dtype: string - name: age dtype: string - name: accent dtype: string - name: language dtype: string - name: text dtype: string - name: audiopath dtype: string - name: gender dtype: string - name: audio dtype: audio - name: normalized_text dtype: string splits: - name: train num_bytes: 3643072.0 num_examples: 10 download_size: 3334451 dataset_size: 3643072.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

procit002/test_30_dataset

procit002

--- dataset_info: features: - name: speaker_id dtype: string - name: speaker_name dtype: string - name: age dtype: string - name: accent dtype: string - name: language dtype: string - name: text dtype: string - name: audiopath dtype: string - name: gender dtype: string - name: audio dtype: audio - name: normalized_text dtype: string splits: - name: train num_bytes: 11828153.0 num_examples: 30 download_size: 10673102 dataset_size: 11828153.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

procit002/test_50_dataset

procit002

--- dataset_info: features: - name: speaker_id dtype: string - name: speaker_name dtype: string - name: age dtype: string - name: accent dtype: string - name: language dtype: string - name: text dtype: string - name: audiopath dtype: string - name: gender dtype: string - name: audio dtype: audio - name: normalized_text dtype: string splits: - name: train num_bytes: 18820859.0 num_examples: 50 download_size: 17381323 dataset_size: 18820859.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

Susan774/STT_2.0.1_generatedByRikesh_EnhancedData

Susan774

--- dataset_info: features: - name: gender dtype: string - name: accent dtype: string - name: speaker_id dtype: int64 - name: speaker_name dtype: string - name: text dtype: string - name: normalized_text dtype: string - name: audio dtype: audio splits: - name: train num_bytes: 22401105547.24 num_examples: 70252 - name: test num_bytes: 2800032279.34 num_examples: 8782 - name: validation num_bytes: 2777406854.47 num_examples: 8781 download_size: 26241693521 dataset_size: 27978544681.050003 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_64_64_0.01_64_BestF1_it

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 146361 num_examples: 100 - name: test num_bytes: 1023662 num_examples: 655 download_size: 228625 dataset_size: 1170023 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_64_64_0.05_64_BestF1_it

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 146361 num_examples: 100 - name: test num_bytes: 1023662 num_examples: 655 download_size: 228374 dataset_size: 1170023 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_16_32_0.05_64_BestF1_it

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 146361 num_examples: 100 - name: test num_bytes: 1023662 num_examples: 655 download_size: 228390 dataset_size: 1170023 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_16_32_0.01_64_BestF1_it

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 146361 num_examples: 100 - name: test num_bytes: 1023662 num_examples: 655 download_size: 228560 dataset_size: 1170023 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_16_16_0.01_64_BestF1_it

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 146361 num_examples: 100 - name: test num_bytes: 1023662 num_examples: 655 download_size: 228590 dataset_size: 1170023 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

sdiazlor/my-distiset-2d7d7002

sdiazlor

--- size_categories: n<1K dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': neutral '1': positive '2': negative splits: - name: train num_bytes: 223 num_examples: 1 download_size: 2783 dataset_size: 223 configs: - config_name: default data_files: - split: train path: data/train-* tags: - synthetic - distilabel - rlaif - datacraft --- <p align="left"> <a href="https://github.com/argilla-io/distilabel"> <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/> </a> </p> # Dataset Card for my-distiset-2d7d7002 This dataset has been created with [distilabel](https://distilabel.argilla.io/). ## Dataset Summary This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI: ```console distilabel pipeline run --config "https://huggingface.co/datasets/sdiazlor/my-distiset-2d7d7002/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/sdiazlor/my-distiset-2d7d7002/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "label": 0, "text": "The speaker\u0027s narrative employs a peculiar blend of nostalgic sentiment and a somewhat jaded tone, which creates an interesting dynamic but also makes it challenging to pinpoint a definitive emotional undertone." } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-2d7d7002", "default") ``` Or simply as it follows, since there's only one configuration and is named `default`: ```python from datasets import load_dataset ds = load_dataset("sdiazlor/my-distiset-2d7d7002") ``` </details>

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_16_64_0.01_64_BestF1_it

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 146361 num_examples: 100 - name: test num_bytes: 1023662 num_examples: 655 download_size: 228346 dataset_size: 1170023 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_32_16_0.05_64_BestF1_it

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 146361 num_examples: 100 - name: test num_bytes: 1023662 num_examples: 655 download_size: 228579 dataset_size: 1170023 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_32_64_0.05_64_BestF1_it

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 146361 num_examples: 100 - name: test num_bytes: 1023662 num_examples: 655 download_size: 228253 dataset_size: 1170023 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_16_64_0.05_64_BestF1_it

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 146361 num_examples: 100 - name: test num_bytes: 1023662 num_examples: 655 download_size: 228278 dataset_size: 1170023 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_64_32_0.01_64_BestF1_it

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 146361 num_examples: 100 - name: test num_bytes: 1023662 num_examples: 655 download_size: 228602 dataset_size: 1170023 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_32_32_0.01_64_BestF1_it

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 146361 num_examples: 100 - name: test num_bytes: 1023662 num_examples: 655 download_size: 228514 dataset_size: 1170023 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_64_32_0.05_64_BestF1_it

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 146361 num_examples: 100 - name: test num_bytes: 1023662 num_examples: 655 download_size: 228434 dataset_size: 1170023 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

dgambettaphd/D_gen5_run2_llama2-7b_wiki_doc1000_real32_synt96

dgambettaphd

--- dataset_info: features: - name: id dtype: int64 - name: doc dtype: string splits: - name: train num_bytes: 523943 num_examples: 1000 download_size: 288773 dataset_size: 523943 configs: - config_name: default data_files: - split: train path: data/train-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_32_16_0.01_64_BestF1_it

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 146361 num_examples: 100 - name: test num_bytes: 1023662 num_examples: 655 download_size: 228468 dataset_size: 1170023 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_32_64_0.01_64_BestF1_it

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 146361 num_examples: 100 - name: test num_bytes: 1023662 num_examples: 655 download_size: 228486 dataset_size: 1170023 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_64_16_0.01_64_BestF1_it

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 146361 num_examples: 100 - name: test num_bytes: 1023662 num_examples: 655 download_size: 228489 dataset_size: 1170023 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

all-oj-gen/ds_coder_reflct_rmsprop_iter2_sppo_hard_new_all_oj_iter2-bin

all-oj-gen

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: rejected_traceback dtype: string - name: chosen_probs dtype: float64 - name: chosen_probs_win dtype: float64 - name: chosen_probs_lose dtype: float64 splits: - name: train num_bytes: 21045501 num_examples: 5305 download_size: 9250215 dataset_size: 21045501 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ds_coder_reflct_rmsprop_iter2_sppo_hard_new_all_oj_iter2-bin" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_32_32_0.05_64_BestF1_it

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 146361 num_examples: 100 - name: test num_bytes: 1023662 num_examples: 655 download_size: 228566 dataset_size: 1170023 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

all-oj-gen/ds_coder_reflct_rmsprop_iter2_sppo_hard_new_all_oj_iter2-full_resp_trace

all-oj-gen

--- dataset_info: features: - name: prompt dtype: string - name: test dtype: string - name: tag dtype: string - name: id dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: text_prompt dtype: string - name: text_chosen dtype: string - name: text_rejected dtype: string - name: generate_0 dtype: string - name: generate_0_score dtype: int64 - name: traceback_0 dtype: string - name: generate_1 dtype: string - name: generate_1_score dtype: int64 - name: traceback_1 dtype: string - name: generate_2 dtype: string - name: generate_2_score dtype: int64 - name: traceback_2 dtype: string - name: generate_3 dtype: string - name: generate_3_score dtype: int64 - name: traceback_3 dtype: string - name: probability sequence: sequence: float64 - name: rm_scores sequence: int64 splits: - name: train num_bytes: 54080058 num_examples: 5305 download_size: 21032273 dataset_size: 54080058 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ds_coder_reflct_rmsprop_iter2_sppo_hard_new_all_oj_iter2-full_resp_trace" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

all-oj-gen/ds_coder_reflct_rmsprop_iter2_sppo_hard_new_all_oj_iter2-bin_all_pairs

all-oj-gen

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: rejected_traceback dtype: string - name: test dtype: string splits: - name: train num_bytes: 40862878 num_examples: 10169 download_size: 12285543 dataset_size: 40862878 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ds_coder_reflct_rmsprop_iter2_sppo_hard_new_all_oj_iter2-bin_all_pairs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_16_16_0.05_64_BestF1_it

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 146361 num_examples: 100 - name: test num_bytes: 1023662 num_examples: 655 download_size: 228594 dataset_size: 1170023 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_64_16_0.05_64_BestF1_it

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 146361 num_examples: 100 - name: test num_bytes: 1023662 num_examples: 655 download_size: 228574 dataset_size: 1170023 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_16_16_0.01_64_BestF1_sk

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 148050 num_examples: 102 - name: test num_bytes: 1034730 num_examples: 653 download_size: 248654 dataset_size: 1182780 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_16_16_0.05_64_BestF1_sk

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 148050 num_examples: 102 - name: test num_bytes: 1034730 num_examples: 653 download_size: 248498 dataset_size: 1182780 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_64_16_0.01_64_BestF1_sk

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 148050 num_examples: 102 - name: test num_bytes: 1034730 num_examples: 653 download_size: 248486 dataset_size: 1182780 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_64_16_0.05_64_BestF1_sk

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 148050 num_examples: 102 - name: test num_bytes: 1034730 num_examples: 653 download_size: 248386 dataset_size: 1182780 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_16_64_0.01_64_BestF1_sk

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 148050 num_examples: 102 - name: test num_bytes: 1034730 num_examples: 653 download_size: 248228 dataset_size: 1182780 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_16_64_0.05_64_BestF1_sk

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 148050 num_examples: 102 - name: test num_bytes: 1034730 num_examples: 653 download_size: 248064 dataset_size: 1182780 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_16_32_0.05_64_BestF1_sk

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 148050 num_examples: 102 - name: test num_bytes: 1034730 num_examples: 653 download_size: 248406 dataset_size: 1182780 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_16_32_0.01_64_BestF1_sk

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 148050 num_examples: 102 - name: test num_bytes: 1034730 num_examples: 653 download_size: 248492 dataset_size: 1182780 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_32_64_0.01_64_BestF1_sk

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 148050 num_examples: 102 - name: test num_bytes: 1034730 num_examples: 653 download_size: 248292 dataset_size: 1182780 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_32_16_0.05_64_BestF1_sk

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 148050 num_examples: 102 - name: test num_bytes: 1034730 num_examples: 653 download_size: 248321 dataset_size: 1182780 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_64_64_0.01_64_BestF1_sk

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 148050 num_examples: 102 - name: test num_bytes: 1034730 num_examples: 653 download_size: 248623 dataset_size: 1182780 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_64_64_0.05_64_BestF1_sk

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 148050 num_examples: 102 - name: test num_bytes: 1034730 num_examples: 653 download_size: 248308 dataset_size: 1182780 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_64_32_0.05_64_BestF1_sk

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 148050 num_examples: 102 - name: test num_bytes: 1034730 num_examples: 653 download_size: 248375 dataset_size: 1182780 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_32_64_0.05_64_BestF1_sk

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 148050 num_examples: 102 - name: test num_bytes: 1034730 num_examples: 653 download_size: 248096 dataset_size: 1182780 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_32_32_0.05_64_BestF1_sk

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 148050 num_examples: 102 - name: test num_bytes: 1034730 num_examples: 653 download_size: 248458 dataset_size: 1182780 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

r1v3r/bitflags-filterbyLLM-verified

r1v3r

--- dataset_info: features: - name: repo dtype: string - name: issue_numbers sequence: string - name: version dtype: string - name: problem_statement dtype: string - name: hints_text dtype: string - name: created_at dtype: string - name: base_commit dtype: string - name: pull_number dtype: int64 - name: instance_id dtype: string - name: test_patch dtype: string - name: patch dtype: string - name: environment_setup_commit dtype: string - name: FAIL_TO_PASS sequence: string - name: PASS_TO_PASS sequence: string - name: FAIL_TO_FAIL sequence: string - name: PASS_TO_FAIL sequence: 'null' splits: - name: train num_bytes: 146844 num_examples: 8 download_size: 53602 dataset_size: 146844 configs: - config_name: default data_files: - split: train path: data/train-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_32_32_0.01_64_BestF1_sk

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 148050 num_examples: 102 - name: test num_bytes: 1034730 num_examples: 653 download_size: 248385 dataset_size: 1182780 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_64_32_0.01_64_BestF1_sk

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 148050 num_examples: 102 - name: test num_bytes: 1034730 num_examples: 653 download_size: 248339 dataset_size: 1182780 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_32_16_0.01_64_BestF1_sk

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 148050 num_examples: 102 - name: test num_bytes: 1034730 num_examples: 653 download_size: 248376 dataset_size: 1182780 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_16_32_0.01_64_BestF1_sl

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 149854 num_examples: 101 - name: test num_bytes: 1063090 num_examples: 654 download_size: 242053 dataset_size: 1212944 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_64_16_0.05_64_BestF1_sl

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 149854 num_examples: 101 - name: test num_bytes: 1063090 num_examples: 654 download_size: 242025 dataset_size: 1212944 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_64_64_0.05_64_BestF1_sl

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 149854 num_examples: 101 - name: test num_bytes: 1063090 num_examples: 654 download_size: 241970 dataset_size: 1212944 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_64_64_0.01_64_BestF1_sl

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 149854 num_examples: 101 - name: test num_bytes: 1063090 num_examples: 654 download_size: 242327 dataset_size: 1212944 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_32_64_0.01_64_BestF1_sl

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 149854 num_examples: 101 - name: test num_bytes: 1063090 num_examples: 654 download_size: 242042 dataset_size: 1212944 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_32_16_0.05_64_BestF1_sl

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 149854 num_examples: 101 - name: test num_bytes: 1063090 num_examples: 654 download_size: 241988 dataset_size: 1212944 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_16_16_0.05_64_BestF1_sl

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 149854 num_examples: 101 - name: test num_bytes: 1063090 num_examples: 654 download_size: 242045 dataset_size: 1212944 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_16_64_0.05_64_BestF1_sl

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 149854 num_examples: 101 - name: test num_bytes: 1063090 num_examples: 654 download_size: 241586 dataset_size: 1212944 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_d30e7a76-647b-4e0a-9d8f-3327d2a3ccd9

argilla-internal-testing

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

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_48503ed1-9c86-4d10-a41f-b40823ff650d

argilla-internal-testing

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

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_32_64_0.05_64_BestF1_sl

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 149854 num_examples: 101 - name: test num_bytes: 1063090 num_examples: 654 download_size: 241799 dataset_size: 1212944 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_bd080b98-5420-46fe-9e41-2a4cf3fb054e

argilla-internal-testing

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

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_0c958668-a039-42ce-bc3f-abc303522e12

argilla-internal-testing

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

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_ae395be0-6c08-4531-8698-3c13ad1eed43

argilla-internal-testing

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

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-all-revised_NoQuant_16_16_0.01_64_BestF1_sl

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 149854 num_examples: 101 - name: test num_bytes: 1063090 num_examples: 654 download_size: 242317 dataset_size: 1212944 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_fd26c500-4f5c-4d05-a20e-4dfd6147eff5

argilla-internal-testing

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

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_9882ad92-3c06-4ebe-a6dd-664cd9a43922

argilla-internal-testing

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

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_5c2cf429-f784-47aa-a90b-56ca191017e7

argilla-internal-testing

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